WO2016203652A1

WO2016203652A1 - System related to data analysis, control method, control program, and recording medium therefor

Info

Publication number: WO2016203652A1
Application number: PCT/JP2015/067779
Authority: WO
Inventors: 喜勝白井; 菜々子吉田
Original assignee: 株式会社Ｕｂｉｃ
Priority date: 2015-06-19
Filing date: 2015-06-19
Publication date: 2016-12-22

Abstract

The data analysis system evaluates data to be evaluated which belongs to a first phase of a predetermined case wherein multiple phases sequentially progress, generates profile information of an agent of the first phase on the basis of the evaluation results, and predicts a behavior of the agent in a second phase on the basis of the profile information.

Description

Data analysis system, control method, control program, and recording medium therefor

This application relates to a data analysis system for analyzing data, and can be applied to, for example, an artificial intelligence system for analyzing big data.

As a result of computerization of society due to the rapid development of computers, an enormous amount of information (big data) has become widely and closely related to the activities of companies and individuals. Therefore, recently, the necessity of accurately separating desired information from big data has been emphasized.

As an approach for extracting desired information from big data, we apply data analysis by reviewers to some data sampled from the data group, and use the analysis results to extract the remaining data. A system capable of automatic analysis is known (for example, JP 2013-182338 A).

JP 2013-182338 A

The above data analysis system makes it possible to predict a behavior directly related to a predetermined case of the subject by analyzing a tendency of the subject acting toward the predetermined case and a behavior tendency. However, there is a problem that the action cannot be predicted depending on the type of the predetermined case.

Therefore, the present application has been made in view of such problems, and provides a data analysis technology system capable of predicting actions related to a predetermined case of the subject and related technology regardless of the type of the predetermined case. The purpose is to do.

A first disclosure that achieves the above object is a data analysis system that analyzes data related to a predetermined case that progresses through a plurality of phases in order, and includes a memory, an input control device, and a controller, The controller generates an index for ranking a plurality of target data for the first phase, the index corresponds to the relationship between each target data and the predetermined case, and the user The memory changes at least temporarily based on an input given via an input control device, and the memory stores at least temporarily the plurality of target data, and the input control device responds to sample data presented to the user. The classification information can be input to the user, and the classification information is applied to the sample data based on the input to classify the sample data. The controller uses a combination of the sample data and the classification information received from the user as reference data, acquires a plurality of the reference data, extracts components from the plurality of reference data, The component constitutes at least a part of the reference data, evaluates the degree to which the component contributes to the combination, and based on the evaluation result of the component, the first phase By generating the index, the relevance between the plurality of target data and the predetermined case is evaluated, and profile information is created based on the evaluation of the relevance, and the profile information is the first phase Including the identification information of the action subject relating to the first fading by the action subject identified by the profile information. Predicting behavior in the subsequent second phase in, characterized in that.

A second disclosure for achieving the object is a method for controlling a data analysis system that analyzes data related to a predetermined case that sequentially progresses through a plurality of phases, the data analysis system including a plurality of target data. An index to be ordered is generated for the first phase, the index corresponds to the relationship between each target data and the predetermined case, and changes based on an input from the user. , At least temporarily storing the plurality of target data, and enabling the user to input classification information for the sample data presented to the user, the classification information being used to classify the sample data It is associated with the sample data based on the input, and refers to the combination of the sample data and the classification information received from the user And acquiring a plurality of the reference data, extracting components from the plurality of reference data, the components constituting at least a part of the reference data, and the components in the combination Evaluate the degree of contribution, and evaluate the relevance between the plurality of target data and the predetermined case by generating the index for the first phase based on the evaluation result of the component, Based on the evaluation of the relationship, profile information is created, the profile information includes identification information of an action subject related to the first phase, and
A behavior in a second phase following the first phase by an action subject specified by the profile information is predicted.

The third disclosure for achieving the object is a data analysis system control program for causing a computer to execute each step included in the control method of the data analysis system.

A fourth disclosure for achieving the object is a computer-readable recording medium in which a control program for a data analysis system is recorded.

According to the above-described disclosure, a data analysis technology system, a control method, a control program, and a recording medium capable of predicting an action related to a predetermined case of an action subject regardless of the type of the predetermined case are provided.

It is a block diagram which shows an example of the hardware constitutions of a data analysis system. It is a functional block diagram which shows an example of the prediction coding function with which the said data analysis system was equipped. It is a flowchart which shows an example of the process which the predictive coding part with which the said data analysis system was provided. It is a table which shows an example of profile information.

Next, an embodiment of a data analysis system will be described based on the accompanying drawings.
[Data analysis system configuration]
FIG. 1 is a block diagram illustrating an example of a hardware configuration of a data analysis system (hereinafter, simply referred to as “system”) according to the present embodiment. The system includes, for example, an arbitrary recording medium (eg, memory, hard disk, etc.) capable of storing data (including digital data and analog data), and a controller capable of executing a control program stored in the recording medium. (E.g., CPU: Central Processing Unit) or a computer (e.g., personal computer, server device, client device, workstation, mainframe, etc.) or computer that analyzes data stored at least temporarily in the recording medium System (for example, server device that executes main processing for data analysis, client device used by user, file server that stores data to be analyzed, etc.) Realize It may be implemented as Temu). In the present embodiment, an example (FIG. 1) in which the system is realized by the latter will be mainly described.

In the present embodiment, “data” may be any data expressed in a format that can be processed by the computer. The data may be, for example, unstructured data whose structure definition is incomplete at least in part, and document data (for example, e-mail (attached file header) Information), technical documents (including a wide range of documents explaining technical matters such as academic papers, patent publications, product specifications, design drawings, etc.), presentation materials, spreadsheets, financial statements, meeting materials, Record reports, sales documents, contracts, organization charts, business plans, company analysis information, electronic medical records, web pages, blogs, comments posted on social network services, etc., audio data (eg conversation / music) Data), image data (eg, data composed of a plurality of pixels or vector information), video data (eg, Broadly includes such configured data) of a plurality of frame images.

Further, in the present embodiment, “reference data” (reference data) may be, for example, data associated with classification information by a user (data that has been classified, which is a combination of data and classification information). . On the other hand, the “target data” (target data) may be data not associated with the classification information (unclassified data that is not presented to the user as reference data and is not classified for the user). . Here, the “classification information” may be an identification label used for classifying the reference data. For example, the “Related” label indicating that the data and the predetermined case are related is particularly related. Information that classifies the reference data into three types, such as “High” label indicating that the two are not related and “Non-Related” label indicating that the two are not related. ”,“ Ordinary ”,“ slightly bad ”, and“ bad ”, the information may be information that classifies the reference data into five.

In addition, the “predetermined case” includes a wide range of targets for which the system is evaluated for relevance to data, and the scope thereof is not limited. For example, the predetermined case may be a case where the discovery procedure is required when the system is realized as a discovery support system, or a crime to be investigated when the system is realized as a criminal investigation support system. When implemented as an email monitoring system, it may be fraudulent (eg information leakage, collusion, etc.), or a medical application system (eg pharmacovigilance support system, clinical trial efficiency system, medical risk hedging) System, fall prediction (fall prevention) system, prognosis prediction system, diagnosis support system, etc.), it may be a case or case related to medicine, or an Internet application system (for example, smart mail system, information aggregation ( System), user monitoring system System, social media management system, etc., it may be case examples / cases related to the Internet, and if implemented as a project evaluation system, it may be a project that has been carried out in the past, or implemented as a marketing support system. If it is, it may be a product / service targeted for marketing, or it may be realized as an intellectual property evaluation system, it may be an intellectual property subject to evaluation, or it may be realized as an unauthorized transaction monitoring system, It may be a fraudulent financial transaction, if it is realized as a call center escalation system, it may be a past response case, if it is realized as a credit check system, it may be a subject of credit check, and driving support When implemented as a system, driving the vehicle It may be that concerned, if it is implemented as a sales support system, may be in the operating results.

As illustrated in FIG. 1, the data analysis system 1 according to the present embodiment includes, for example, a server device 2 that can execute main processing of data analysis and one or more that can execute related processing of data analysis. A storage system 5 including a plurality of client devices 3, a database 4 for recording data and evaluation results for the data, and a management computer 6 that provides a management function for data analysis to the client device 3 and the server device 2. And may be provided.

The client device (input control device) 3 can present a part of a plurality of target data to the user as sample data before classification. As a result, the user can input (provide classification information) for the evaluation / classification of the sample data via the client device 3. The server device 2 can randomly sample a plurality of target data, extract a predetermined number of sample data, and provide this to a predetermined client device. The sample data may be data belonging to a data group that is not included in the target data to be analyzed but has a predetermined case that is the same as or similar to the target data. The client device 3 includes, as hardware resources, for example, a memory, a controller, a bus, an input / output interface (for example, a keyboard and a display), and a communication interface (communication means using a predetermined network). And the server apparatus 2 and the management computer 6 are communicably connected).

Based on the sample data to which the classification information is attached, that is, the combination of the sample data and the classification information (this is referred to as “reference data”), the server device 2 uses the reference data to include a pattern (for example, included in the data). Abstract rules, meanings, concepts, styles, distributions, samples, etc., not limited to so-called “specific patterns”), and based on the patterns, the relationship between the target data and the specified case evaluate. That is, the server device 2 can also evaluate the relationship between the target data and fraud (for example, information leakage) based on the learned pattern, and evaluate the relationship between the target data and the lawsuit. You can also evaluate the relationship between the target data and criminal investigation, you can evaluate the relationship between the target data and user preferences, and the relationship between the target data and any other event Sex can also be evaluated. Similarly to the client device 3, the server device 2 may include, for example, a memory, a controller, a bus, an input / output interface, and a communication interface as hardware resources.

The management computer 6 executes predetermined management processing for the client device 3, the server device 2, and the storage system 5. Similarly to the client device 3, the management computer 6 may include, for example, a memory, a controller, a bus, an input / output interface, and a communication interface as hardware resources. Note that application programs that can control each device are stored in the memory provided in each of the client device 3, the server device 2, and the management computer 6, and each controller executes the application program to thereby execute the application program. Programs (software resources) and hardware resources cooperate to operate each device.

The storage system 5 may be composed of, for example, a disk array system, and may include a database 4 that records data and results of evaluation / classification of the data. The server apparatus 2 and the storage system 5 are connected by a DAS (Direct Attached Storage) method or a SAN (Storage Area Network).

Note that the hardware configuration shown in FIG. 1 is merely an example, and the above system can be realized by other hardware configurations. For example, a part or all of the processing executed in the server device 2 may be executed in the client device 3, or a part or all of the processing may be executed in the server device 2. Alternatively, the storage system 5 may be built in the server device 2. Further, the user not only performs input for evaluation / classification of sample data via the client device 3 (gives classification information), but also performs the above input via an input device directly connected to the server device 2. You can also. It is understood by those skilled in the art that there can be various hardware configurations capable of realizing the system, and the present invention is not limited to one specific configuration (for example, the configuration illustrated in FIG. 1).

[Predictive coding function of data analysis system 1]
FIG. 2 is a functional block diagram showing an example of the predictive coding function realized by the data analysis system according to the present embodiment.

(Basic configuration of predictive coding function)
As illustrated in FIG. 2, the system can include a predictive coding unit 10. The predictive coding (Predictive Coding) unit 10 is a large number of data (target data not associated with classification information) based on a small number of data manually classified (referred to as the reference data described above). For example, it is big data.) The target data is evaluated so that significant information can be extracted.

The predictive coding unit 10 includes, for example, a data acquisition unit 11, a classification information acquisition unit 12, a data classification unit 13, a component extraction unit 14, a component evaluation unit 15, a component storage 16 and a data evaluation unit 17. Can do.

The data acquisition unit 11 acquires data from an arbitrary storage resource (for example, the database 4, a web server on the Internet, a mail server on the intranet, etc.). The data acquisition unit 11 provides all data to be subjected to data analysis as target data to the component extraction unit 14, randomly samples the target data, acquires a predetermined number of sample data, and classifies the data Provided to part 13.

The classification information acquisition unit 12 acquires the classification information input by the user for each sample data from an arbitrary input device (for example, the client device 3), and outputs the classification information to the data classification unit 13.

The data classification unit 13 combines the plurality of sample data sent from the data acquisition unit 11 and the classification information input to each sample data from the classification information acquisition unit 12, and uses the combination as a plurality of reference data To the component extraction unit 14.

The component extraction unit 14 extracts the components constituting the reference data from the plurality of reference data received from the data classification unit 13. Here, the “component” may be partial data constituting at least a part of the data, for example, a morpheme, a keyword, a sentence, a paragraph, and / or metadata (for example, an email header) constituting the document. Information), partial audio that constitutes audio, volume (gain) information, and / or timbre information, partial image that constitutes an image, partial pixels, and / or luminance information, and video Frame image, motion information, and / or 3D information. The component extraction unit 14 outputs the extracted component and classification information corresponding to the component to the component evaluation unit 15. Further, the constituent element extraction unit 14 extracts constituent elements constituting the target data from the target data input from the data acquisition unit 11 and outputs the constituent elements to the data evaluation unit 17.

The component evaluation unit 15 evaluates the component input from the component extraction unit 14. For example, the component evaluation unit 15 determines the degree of contribution of the plurality of components constituting at least part of the reference data to the combination (in other words, the distribution in which the components appear according to the classification information). Evaluate each. More specifically, the constituent element evaluation unit 15 uses, for example, a transmission information amount (for example, an information amount calculated from a predetermined definition formula using the appearance probability of the constituent element and the appearance probability of the classification information). Then, the evaluation value of the component is calculated by evaluating the component. Thereby, the component evaluation unit 15 can learn a pattern included in the reference data (learns a pattern characterized by the reference data according to classification information given by an input from the user). The component evaluation unit 15 outputs the component and the evaluation value of the component to the component storage unit 16.

The component storage unit 16 associates the component and the evaluation value input from the component evaluation unit 15, and stores both in an arbitrary memory (for example, the storage system 5).

The data evaluation unit 17 reads an evaluation value associated with the component input from the component extraction unit 14 from an arbitrary memory (for example, the database 4 of the storage system 5), and obtains target data based on the evaluation value. evaluate. More specifically, the data evaluation unit 17 ranks the index of the target data (for example, ranks the target data, for example, by adding the evaluation values associated with the constituent elements constituting at least a part of the target data. Numerical values, letters, and / or symbols) can be derived. A form suitable as the index is a score obtained by adding the evaluation values. The data evaluation unit 17 associates the target data with the index, and stores both in an arbitrary memory (for example, the storage system 5).

The component evaluation unit 15 selects the component until the evaluation of the data with the “Related” or “High” label set becomes larger than the evaluation of the data with no label set, and the component Can be repeatedly evaluated to correct the evaluation value of the component. As a result, the component evaluation unit 15 can find a component that appears in a plurality of reference data to which the classification information “Related” or “High” is attached and has an influence on the combination of the reference data and the label. . The component evaluation unit 15 calculates the evaluation value wgt of the component using, for example, the following formula.

Here, wgt indicates the initial value of the evaluation value of the i-th component before evaluation. Wgt indicates the evaluation value of the i-th component after the Lth evaluation. γ means an evaluation parameter in the L-th evaluation, and θ means a threshold value in the evaluation. Thereby, the component evaluation part 15 can evaluate, for example, that a component represents the characteristic of predetermined classification information, so that the value of the calculated transmission information amount is large. Note that the component evaluation unit 15 sets, as target data, an intermediate value between the lowest value of the index of the reference data set with “Related” and the highest value of the index of the reference data set with “Non-Related”. On the other hand, a threshold value (predetermined reference value) for automatically determining whether or not “Related” is set can be used. And the data evaluation part 17 calculates each score of each of several target data and each of several reference data from the following formula | equation, for example from the evaluation value of a component. The score is an index that quantitatively evaluates the strength of the connection of these data to the classification code.

m _j : frequency of occurrence of the i-th component
wgt _i : Evaluation value of the i-th component

In addition, in the above, since the configuration described as “*** part” is a functional configuration that is realized by executing a program (data analysis program) by a controller included in the data analysis system, It may be paraphrased as ** processing or *** function. In addition, since the *** part can be replaced by hardware resources, those skilled in the art will understand that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof. Yes, it is not limited to either.

[Processing performed by the predictive coding unit 10]
FIG. 3 is a flowchart showing an example of processing executed by the predictive coding unit 10 included in the data analysis system according to the present embodiment.

First, the data acquisition unit 11 acquires sample data from an arbitrary memory (step 10, hereinafter “step” is abbreviated as “S”). Next, the classification information acquisition unit 12 acquires the classification information input by the user from an arbitrary input device (S11). Next, the data classification unit 13 classifies the data by combining the data and the classification information to configure reference data (S12), and the component extraction unit 14 configures the reference data. Are extracted from the reference data (S13). Then, the component evaluation unit 15 evaluates the component (S14), and the component storage unit 16 associates the component with the evaluation value and stores both in an arbitrary memory (S15). The processing of S10 to S15 is referred to as a “learning phase” (a phase in which the system learns a pattern).

The data acquisition unit 11 acquires target data from an arbitrary memory (S16). The component extraction unit 14 extracts the components constituting the target data from the target data (S17). The data evaluation unit 17 reads an evaluation value associated with the constituent element from an arbitrary memory, and evaluates the target data based on the evaluation value (S18). The processing of S16 to S18 is referred to as “evaluation phase” (the system evaluates target data based on the pattern). Note that each process included in the learning phase is not an essential process in the system. For example, a memory that associates and stores a component and an evaluation value of the component is given in advance, and the predictive coding unit 10 performs target data based on the component and the evaluation value stored in the memory. Can also be evaluated.

(Phase analysis function)
The predictive coding unit 10 may further include a phase analysis unit 19. The phase analysis unit 19 has the following functions (1) to (4), for example.

(1) Phase analysis The phase analysis part 19 can analyze the phase which shows each step in which a predetermined case progresses. Here, a flow in which the phase analysis unit 19 analyzes a phase based on an example in which the above-described system is realized as a fraud investigation support system and the predetermined case is “collusion” will be described.

The collusion involves the fostering phase (the stage of building relationships with competitors), the preparation phase (the stage of exchanging information about competitors with competitors), the execution phase (providing prices to customers, obtaining feedback, Progress in the order of communication). Therefore, the system administrator sets the above three phases in the phase analysis unit 19. The system learns a plurality of patterns corresponding to the plurality of phases from a plurality of types of reference data respectively prepared for a plurality of preset phases, and the target data based on the plurality of phases, respectively. Can be specified, for example, “in which phase the action subject (individual or organization, etc.) to be analyzed is currently in”.

That is, the component evaluation unit 15 refers to a plurality of types of reference data respectively prepared for a plurality of preset phases, evaluates components included in the plurality of types of reference data, and The element and the result (evaluation value) obtained by evaluating the component are associated with each other and stored in the memory for each phase (that is, a plurality of patterns corresponding to the plurality of phases are respectively learned). Next, the data evaluation unit 17 derives an index for each of a plurality of phases by analyzing the target data based on the pattern learned for each phase.

Then, the phase analysis unit 19 determines whether or not the index satisfies a predetermined determination criterion (for example, a threshold value) set in advance for each phase (for example, whether or not the index exceeds the threshold value). ) And the count value corresponding to the phase is increased. Finally, the phase analysis unit 19 specifies the current phase based on the count value (for example, the phase having the maximum count value is set as the current phase). Or when it determines with the parameter | index derived for every phase satisfy | filling the predetermined criterion set to the said phase, the phase analysis part 19 can also specify the said phase as a present phase. Thereby, the data analysis system can suggest a phase indicating each stage in which the predetermined case progresses to the user.

(2) Phase Progress Prediction Based on Prediction Model The phase analysis unit 19 calculates the following from an index derived by evaluating a plurality of target data based on a model that can predict the progress of a predetermined action related to a predetermined case. Can be predicted and presented.

That is, for example, the phase analysis unit 19 performs regression using the index derived for the first phase (for example, the brewing phase) and the index derived for the second phase (for example, the preparation phase) as variables. Assuming a model (a model that can predict the progress), the possibility (for example, probability) of proceeding to the third phase (for example, execution phase) can be predicted based on a regression coefficient that has been optimized in advance. Thereby, the data analysis system further has an additional effect that the result of predicting the progress of the predetermined action related to the predetermined case can be suggested to the user.

(3) Optimization of determination criteria The phase analysis unit 19 uses the above-mentioned determination criteria (predetermined determination criteria set in advance for each phase, for specifying phases based on the index derived by the data evaluation unit 17, For example, the threshold) can be optimized according to given data. For example, the management unit 18 performs regression analysis on the relationship between the index derived for each of the plurality of target data and the ranking of the index (that is, the rank when the indices are arranged in ascending order), and the regression Based on the result of the analysis, the determination criterion can be reset (for example, the threshold value is changed).

First, the administrator of the system previously sets a ranking threshold for the ranking. For example, a function (y = e ^αx + β (e is the base of the natural logarithm) where the phase analysis unit 19 determines the relationship between the index derived by the data evaluation unit 17 and the ranking of the index. α and β are parameters that take real values)) (for example, the parameters of the function are determined by the method of least squares), and the index corresponding to the ranking threshold is newly set in the function. Is set as a simple criterion (the threshold after change). As a result, the data analysis system can optimize the determination criterion according to given data, and thus has the additional effect of improving the accuracy of data analysis.

(4) Behavior prediction using profile information (improvement of phase progress prediction based on prediction model)
As mentioned earlier, the collusion process is in the fostering phase (the stage of building relationships with competitors), the preparation phase (the stage of exchanging information about competitors with competitors), and the competition phase (showing prices to customers and obtaining feedback) Thus, the predetermined case is composed of a plurality of phases in which the action subject is distinguished by the manner of involvement in the predetermined case so as to progress in the order of communication with competitors). The phases constituting the predetermined case include, for example, a nurturing phase, a preparation phase, and an execution phase, and the predetermined case (collusion) is achieved by the actions of the action subject proceeding through these modes in order. The data analysis system 1 (predictive coding unit 10) can evaluate each phase by performing predictive coding on data such as electronic mail.

The fostering phase may be, for example, a stage (fostering a predetermined case) in which events that can be an indirect factor with respect to the predetermined case, such as a distant cause or background of the predetermined case, grow. The preparation phase may be, for example, a stage where a preliminary event for a predetermined case occurs (preparation of a predetermined case). The execution phase may be, for example, a stage where an event related to a predetermined case occurs (execution of the predetermined case).

If the prescribed case is an illegal act other than the aforementioned collusion (for example, information leakage), the fostering phase is, for example, that the subject of action has complaints or dissatisfaction with the treatment, organization, or workplace environment, etc. Alternatively, the preparation phase includes, for example, an event in which the action entity collects secret information or exchanges information, and the execution phase includes, for example, the action entity It may have an event of sending or receiving secret information. In addition, since the nurturing phase is only a remote cause and background level of the predetermined case, just because the action of the action subject is in the nurture phase, the action of the action subject does not necessarily shift to the execution phase. Therefore, the data analysis system 1 makes it possible, for example, to predict that an action (direct action) belonging to the execution phase is executed by the action subject using the evaluation result of the preparation phase.

However, depending on the type of case, there may be no preparation phase at all or only a few may be expressed. For example, if the specified case is information leakage, the action subject should secretly execute information collection and information exchange related to the confidential information by a method other than the method that remains in the form of data such as e-mail before sending the confidential information. However, it is difficult to predict the behavior belonging to the execution phase from the behavior in the preparation phase of the action subject in the first place. Therefore, the data analysis system 1 does not use the evaluation result of the preparation phase, and the direct action of the action subject classified into the execution phase (second phase) is also determined from the evaluation result of the fostering phase (first phase). I was able to predict.

First, on the premise of the above description of predictive coding performed by the predictive coding unit 10, a brewing phase in an embodiment in which a predetermined event is information leakage and a data analysis method for an execution phase will be described. When analyzing sample data, the reviewer, when evaluating sample data, is complaining or dissatisfied with the subject of action (individuals belonging to an organization or group, etc.), organization, or workplace environment, or money. Focus on the behavioral thoughts and behavioral trends, such as whether or not they have a desire for status, etc., and “High”, “Relative”, or “Non-Relative” for each of the sample data ”Tag. The component evaluation unit 15 evaluates the component included in the reference data, and the data evaluation unit 19 evaluates target data (for example, e-mail, database access log information, etc.) based on the evaluation result, and the target Data is ordered by score.

Next, the phase analysis unit 19 creates the profile information of the action subject based on the target data (related target data) in the upper predetermined rank range (or a predetermined score or higher) of the plurality of target data arranged in order. The phase analysis unit 19 analyzes the metadata or the like of the related target data, so that for each of the plurality of related target data, the data transmission destination, the data transmission source, the creator of the attached file, the updater of the attached file, the data message The personal name mentioned in the column can be specified. The data transmission source specified in this way becomes the identification information of the action subject.

∙ There are multiple action subject entries in the profile information. When the phase analysis unit 19 evaluates the brewing phase, if there is no entry for the specific action subject in the profile information, this is added to the profile information. If there is an entry for the specific action subject in the profile information, The information registered in the profile information is updated. FIG. 4 shows an example of profile information. The profile information includes, for each action subject entry, an action subject identification information field 40, a brewing phase date / time history field 42, an execution phase date / time history field 44, a brewing phase score history field 46, an execution phase score history field 48, and a monitor. A flag field 50 is provided.

The action subject identification information is, for example, the transmission destination of the related target data, the transmission source thereof, the creator of the additional data such as an attached file attached to the related target data, the updater thereof, or the message column of the related target data. It is the personal name mentioned. The brewing phase date and time is the date and time when the brewing phase evaluation was performed, and the brewing phase date and time history is a collection of the date and time of multiple brewing phase evaluations as a history. The execution phase date and time is the date and time when execution phase evaluation (described later) is performed, and the execution phase date and time history is a set of dates and times of multiple execution phase evaluations collected as a history. The brewing phase score is a set value of scores of related object data in which the action subject is involved in the brewing phase evaluation performed at the brewing phase evaluation date and time (for example, an average of the sum of scores of each of multiple related object data) The brewing phase score history is a summary of the set values of the scores for each brewing phase evaluation date and time.

The phase analysis unit 19 determines whether the action subject is involved as the transmission source of the related target data, whether the action subject is involved as the transmission destination of the related target data, or whether the action subject is the transmission destination and the transmission source of the related target data. When calculating the set value of the scores, the score may be corrected or adjusted so that the score of the data to be related is given a weight depending on whether the score is involved. For example, the corrected score of the related target data includes the related target data in which the action subject is involved as the transmission source of the related target data, the related target data in which the action subject is involved as the transmission destination of the related target data, What is necessary is just to make it small in order of the relevant subject data which the action subject does not participate as a transmission destination and a transmission source of relevant subject data. In addition, for example, the score may be corrected so that the score becomes higher for an action subject having more emails other than the normal working hours and having more emails on holiday days.

The execution phase score is a set value of scores of related target data related to the action subject in the evaluation of the execution phase performed at the execution phase evaluation date and time (for example, an average of the sum of scores of each of the plurality of related target data) The execution phase score history is a summary of the set values of the scores for the dates and times of multiple execution phase evaluations as a history. The monitoring flag is control information for causing the data analysis system 1 to recognize an action subject for which the management flag is set as a monitoring target. As a result of the evaluation of the execution phase, the phase analysis unit 19 sets the monitoring flag 50 to the entry of the action subject when the entry of the action subject involved in the related target data exists in the profile information.

The phase analysis unit 19 scans each entry of the action subject in the profile information, and, for example, from the brewing phase date / time history field 42 and the brewing phase score history field 44, the occurrence frequency of the related target data and the score of the related target data are calculated. Analyzing numerical information such as rate of change, and setting a monitoring flag regardless of the result of the evaluation of the execution phase when it is determined that the consciousness tendency of the subject of dissatisfaction or inequality exceeds a predetermined threshold May be.

The phase analysis unit 19 performs a predetermined warning notification to the action subject for which the monitoring flag is set. For example, the warning notification may indicate “There is a risk of information leakage in a short time for the action subject identified as *******” (an example of predicting the action in the execution phase of the action subject). In addition, a warning notification is notified from the phase analysis unit 19 to the client device 3 of the manager or the management computer 6. Therefore, the action subject specified by the notice is profiled as “person with risk of information leakage” and the possibility of reaching the execution phase is predicted in advance by the data analysis manager. In other words, the data analysis administrator suppresses actions related to the execution phase of the specific action subject by restricting data transmission / reception or warning against information leakage to the specific action subject based on the warning notification. can do.

The data evaluation unit 17 evaluates the execution phase in addition to the evaluation of the brewing phase. When the data acquisition unit 11 acquires the target data, the data classification unit 13 evaluates the reviewer based on the sample data. The reviewer reviews the sample data and the additional information such as the attached file, evaluates whether the sample data and the additional information are related to the confidential information, and performs tagging as described above. The secret information includes secret information useful for a company such as a customer list and product design data.

The data evaluation unit 17 sets a score for the target data based on the sample data, and ranks the target data in order of score. The phase analysis unit 19 compares each target data (related target data) within the upper specified number with the profile information (FIG. 4), and determines whether or not the action subject entry 40 related to the related target data is in the profile information. judge. When the phase analysis unit 19 affirms this, the execution phase date / time history field 44 and the execution phase score history field 48 of the profile information are updated, the management flag 50 is set, and if this is denied, the action is added to the profile information. An entry of the subject is created, and related information is registered in the execution phase date / time history field 44 and the execution phase score history field 48. The phase analysis unit 19 sets the monitoring flag 50 to an entry in which the difference between the current date and time and the latest value of the date and time data (the brewing phase date and time history field 42 and the execution phase date and time history field 44) is a predetermined value or more. If the difference is greater than or equal to the second predetermined value, the entry itself may be deleted from the profile information.

In the data analysis system according to the present embodiment, all of the data (relevant data) determined to be related to the fostering of a predetermined case is monitored for all the actors involved in the data without depending on the evaluation of the execution phase. If so, there is a concern that the scope of monitoring will become too wide. For example, even if a data actor whose degree of involvement in a given case does not have a relatively high level sends out useful information, etc., this may be an action within the proper job scope. If the target is selected, the monitoring target becomes too wide, and as a result, the monitoring process may be complicated. On the other hand, if all the data judged to be related to the creation of the specified case is awaiting the evaluation of the execution phase, it is deeply involved in the preparation of the specified case by deciding whether or not the action subject is to be monitored. The related action of the action subject cannot be predicted until the action subject who actually performed the action related to the execution phase. According to the data analysis system according to the present embodiment, in any case, it is possible to accurately predict that the action subject may execute an action belonging to the execution phase.

According to the data analysis system according to the present embodiment, even in a case where the preparation phase is not expressed (for example, information leakage), an action belonging to the execution phase of the action subject may occur based on the evaluation of the development phase. Can be predicted in advance.

(Pattern update function)
The predictive coding unit 10 optimizes evaluation values of constituent elements based on given reference data and / or newly obtained reference data, for example, as described in (1) to (3) below. Can do.

(1) Optimization of evaluation value The component evaluation unit 15 calculates the recall rate or the conformance rate based on the result of evaluating the target data, and the component is the data and the data so that the recall rate or the conformance rate increases. By repeatedly evaluating the degree of contribution to the combination with the classification information, the learned pattern can be updated.

Here, the above-mentioned “recall rate” (RecallateRate) is an index indicating the ratio (coverability) of the data to be discovered to the predetermined number of data. For example, when “reproducibility is 80% with respect to 30% of all data”, it indicates that 80% of the data to be found is included in the data of the top 30% of the index (data If the data is brute force (linear review) without using an analysis system, the amount of data to be discovered is proportional to the amount reviewed, so the greater the deviation from the proportion, the better the system performance.) . The “Precision Rate” is an index indicating the ratio (accuracy) of data to be truly discovered to the data discovered by the system. For example, when the expression “the relevance rate is 80% when 30% of all data is processed” is shown, the proportion of data to be discovered is 80% of the data of the top 30% of the index. .

The component extraction unit 14 calculates the recall rate or the conformance rate based on the result evaluated by the data evaluation unit 17, and when the recall rate or the conformance rate is lower than the target value, the recall rate or the conformance rate is the target. Re-extract the component from the data until the value is exceeded. At this time, the component extraction unit 14 may extract the component excluding the component extracted last time, or may replace a part of the component extracted last time with a new component. When the data evaluation unit 17 derives the index of the target data using the re-extracted component, the index (second index) of each data is derived using the re-extracted component and its evaluation value. The recall rate or the matching rate may be derived again from the first index and the second index obtained before re-extracting the constituent elements. Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

(2) Evaluation of component based on convolution method The component evaluation unit 15 evaluates the component included in the reference data, and then convolves the evaluation value of the component other than the component with the component The component can be re-evaluated so that the evaluation value of the other component is reflected in the evaluation value. As a result, the relevance between the constituent element and the other constituent elements is evaluated as an evaluation value of the constituent element, so that the data analysis system can further improve the accuracy of data analysis. Play.

(3) Optimization Timing The component evaluation unit 15 can update a pattern (for example, a combination of a component and an evaluation value of the component) at an arbitrary timing. That is, for example, the component evaluation unit 15 (a) at a timing when an update request is received from an administrative user who manages the system, (b) at a timing when a preset date and time arrives, and / or (c) The pattern can be updated at a timing when an input regarding the additional review is received from the user.

The user can confirm (confirmation review) the content of the target data from which the index is derived by the data evaluation unit 17, and can newly input classification information for the target data. At this time, the classification information acquisition unit 12 may acquire newly input classification information, and the data classification unit 13 may combine the target data and the classification information and use the combination as new reference data. The new reference data is stored in an arbitrary memory, and is fed back to the system, for example, at the timings (a) to (c).

Thereby, the component extraction unit 14 extracts the component from the new reference data, and the component evaluation unit 15 evaluates the component. When the constituent element has been evaluated before and the constituent element and its evaluation value are stored in the memory, the constituent element storage unit 16 replaces the evaluation value with a new evaluation result (evaluation value) and stores it. If not, the component and the evaluation value are associated with each other and newly stored in the memory. That is, the predictive coding unit 10 includes a plurality of constituent elements constituting at least a part of data corresponding to the classification information at an arbitrary timing (for example, timings (a) to (b) described above). The learned pattern can be updated by re-evaluating the degree of contribution to the combination with the classification information. Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

(Management function)
The predictive coding unit 10 may further include a management unit 18. For example, the management unit 18 has the following functions (1) to (5)).

(1) Review Heat Map
The data evaluation unit 17 derives an index for each of the plurality of target data, and the user (for example, in the order in which the index indicates that the target data is highly related to the predetermined case) Consider the case where the target data is confirmed and classification information is given (confirmed review) as an example. At this time, the management unit 18 uses the gradation corresponding to the ratio that the target data associated with the classification information occupies for all the target data, and the distribution of the ratio with respect to the result of evaluating each of the plurality of target data. Can be displayed in a visible manner.

For example, when the data evaluation unit 17 derives a numerical value in the range of 0 to 10000 as the index, the management unit 18, for example, has a range obtained by dividing the index every 1000 (that is, 0 to 1000 in the first interval). , 1001 to 2000 as the second section, 2001 to 3000 as the third section, etc.) (for example, the target data with the index of 2500 is classified into the third section), and a certain range For example, by changing the color tone of the range so that the ratio of the target data to which the predetermined classification information (for example, “Related”) occupies the total number of target data classified into The range can be displayed (for example, the higher the ratio, the closer to the warm color system and the lower, the closer to the cold color system). The management unit 18 displays the other ranges in the same manner for the other ranges.

Thereby, since the management unit 18 can display the distribution of the ratio in each range using gradation, for example, the index indicates that the relevance between the target data and the predetermined case is high. Even if the range (for example, the 9th section where the index is 8001 to 9000) is displayed in the cold color tone, the confirmation review by the user may be wrong. Can be suggested. That is, the data analysis system further provides an additional effect that allows the user to grasp the distribution at a glance.

(2) Central Linkage
The management unit 18 can visualize interrelationships (eg, hierarchical relationships, series relationships, data transmission / reception, etc.) between a plurality of subjects (eg, people, organizations, computers, etc.). For example, when an e-mail is transmitted from the first computer to the second computer, the management unit 18 converts the first circle representing the first computer and the second circle representing the second computer into the first circle. A predetermined display device (for example, a display provided in the client device 10) is a diagram that is connected by an arrow (for example, a thickness corresponding to the size of the e-mail) from the circle to the second circle. Can be displayed.

Further, the management unit 18 can visualize the interrelationship according to the result evaluated by the data evaluation unit 17. For example, when the data evaluation unit 17 derives a numerical value in the range of 0 to 10000 as the index, the management unit 18 may, for example, target data (for example, first data) associated with an index belonging to a specified section. The diagram can be displayed on the predetermined display device only on the basis of the electronic mail transmitted from the computer to the second computer. Thereby, the data analysis system further exhibits an additional effect that allows the user to grasp the mutual relationship between a plurality of subjects at a glance.

(3) Behavior Extractor
The management unit 18 determines whether or not the first component representing the predetermined operation is included in the target data. When determining that the first component is included, the management unit 18 identifies the second component representing the target of the predetermined operation can do. For example, when the sentence “determine the specification” is included in the target data, the component “specification” and “determine” are extracted from the sentence, and the component (determining the predetermined operation) The other component (object) called "specification" that is the target of the verb) is specified. Next, the management unit 18 associates the meta information (attribute information) indicating the attribute (property / feature) of the target data including the above constituent element and other constituent elements with the constituent element and the second constituent element. Here, the meta information is information indicating a predetermined attribute of data. For example, when the target data is an e-mail, the name of the person who sent the e-mail, the name of the person who received the e-mail, and the e-mail It may be an address, the date and time of transmission / reception, and the like.

Then, the management unit 18 associates the two components with the meta information and displays them on a predetermined display device (for example, a display provided in the client device 3). For example, the management unit 18 connects the circle representing the first component and the circle representing the second component with an arrow from the first circle to the second circle. It can be displayed on a display device. Thereby, the data analysis system further exhibits an additional effect that the user can grasp the predetermined operation and the target at a glance.

(4) Automatic summarization based on generative concept extraction The management unit 18 can extract data including constituent elements corresponding to subordinate concepts of a preselected concept from a plurality of target data, and can summarize the plurality of target data. Content (eg, sentences, graphs, tables, etc.) can be generated.

First, the user selects some concepts according to the topic to be detected from the target data, and registers the selected concepts in the management unit 18 in advance. For example, if the topic to be detected is “illegal” or “dissatisfied”, the concept category is divided into five categories of “behavior”, “emotion”, “nature / state”, “risk”, and “money” For example, “behavior” for “behavior”, “despise”, etc. “feeling” for “feelings”, “being angry”, etc. “dullness” for “nature / state”, “ The concept of “risk” and “danger” for “risk”, such as “bad attitude”, and “money paid for human labor” for “money” are given to the management unit 18 by the user. sign up.

For each registered concept, the management unit 18 searches the reference data for a component corresponding to the subordinate concept of the concept, associates the searched component with the concept, and stores an arbitrary memory (for example, storage Store in system 18). Then, the management unit 18 extracts the stored constituent element from the target data, specifies a concept associated with the constituent element, and outputs a summary using the concept. For example, the management unit 18 extracts the concepts “system”, “sales” and “do” from the text “monitoring system order” included in a certain e-mail, and “accounting system introduction” included in another e-mail. The concepts “system”, “sale”, and “do” are extracted from the text “”, and “sell system” is output as a summary of these emails. At this time, the management unit 18 can show, for example, a graph (for example, a pie chart) indicating the ratio of target data including the concept of “sell system” to all target data. Thereby, the data analysis system further exhibits an additional effect of allowing the user to grasp the entire image of the target data.

(5) Topic clustering
The management unit 18 can cluster the plurality of target data according to topics (subjects) included in the plurality of target data. For example, the management unit 18 can cluster a plurality of target data using an arbitrary classification model (for example, K-means, support vector machine, spherical clustering, etc.). Thereby, the data analysis system further exhibits an additional effect of allowing the user to grasp the entire image of the target data.

(Auxiliary function)
Each unit included in the predictive coding unit 10 can have, for example, the following auxiliary functions (1) to (6).

(1) High Resolution Evaluation The data evaluation unit 17 can evaluate target data with high resolution. That is, the data evaluation unit 17 not only derives an index for the target data but also divides the target data into a plurality of parts (for example, sentences or paragraphs (partial target data) included in the target data). Based on the learned pattern, each of the plurality of partial target data can be evaluated (an index is derived for the partial target data). The data evaluation unit 17 can also integrate a plurality of indices derived for each of the plurality of partial target data, and use the integrated index as an evaluation result of the target data (for example, each index is derived as a numerical value). The maximum value of the index is extracted and used as an integrated index for the target data, or the average of the index is set as an integrated index for the target data, or a predetermined number of the indexes are added in descending order, Or an integrated indicator). Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

(2) Time-series evaluation When analyzing data whose properties change with the passage of time (for example, an electronic medical record that records a medical condition that progresses with the passage of time), the component evaluation unit 15 delimits at predetermined intervals. Each pattern is learned from the obtained reference data (for example, the reference data of the first section, the reference data of the second section, etc.) (that is, the component and the result of evaluating the component at each predetermined time) The data evaluation unit 17 can evaluate the target data based on each of the patterns. That is, the data evaluation unit 17 can derive an index for the target data along the time series. Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

At this time, the data evaluation unit 17 can predict a future index based on the temporal change of the index. For example, the data evaluation unit 17 sets a model for time series analysis (for example, autoregressive model, moving average model, etc.) and within a predetermined period (for example, the past month) before new target data is obtained. The next index obtained when the new target data is evaluated can be predicted based on the index derived in step. Thereby, the data analysis system can further exhibit an additional effect that an event that can occur in the future (for example, a risk that an undesirable situation occurs) can be presented to the user.

(3) Case-by-case evaluation Data that changes in nature depending on the type of case (for example, litigation-related documents whose contents change according to the type of lawsuit (for example, violation of antitrust law, information leakage, patent infringement, etc.) Etc.), the component evaluation unit 15 learns each pattern from the reference data prepared for each case (for example, reference data related to violation of the Antimonopoly Act, reference data related to information leakage, etc.) (that is, The data evaluation unit 17 can evaluate the target data based on the pattern, respectively, by acquiring the component and the result of evaluating the component for each case. Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

(4) Syntax analysis The data evaluation unit 17 can analyze the structure of the target data and reflect the analysis result in the evaluation of the target data. For example, when the target data includes a sentence (text) at least partially, the data evaluation unit 17 expresses each sentence included in the sentence (for example, whether the sentence is a positive form or a negative form). Or the like, and the result of the analysis can be reflected in an index derived for the target data. Here, the positive form is an expression that affirms the subject (for example, “the dish is delicious”), and the negative form is an expression that denies the subject (for example, “the dish is not delicious” or “the dish is not delicious”). Yes, the negative form may be an expression that affirms or denies the subject matter (eg, “the food was not delicious” or “the food was not delicious”).

The data evaluation unit 17 can adjust the index according to the expression form. For example, when the data evaluation unit 17 derives a numerical value in a predetermined range as the index, the data evaluation unit 17 adds, for example, “+ α” to the positive form and “−β” to the negative form, The above index can be adjusted by adding “+ θ” to the depolarized form (α, β, and θ may be arbitrary numerical values, respectively). Further, when the data evaluation unit 17 detects that the sentence included in the target data is negative, for example, by canceling the sentence, the component included in the sentence is not used as a basis for deriving the index ( The component is not considered).

Furthermore, the constituent element evaluation unit 15 can increase or decrease the evaluation value of the constituent element depending on, for example, whether a certain morpheme (constituent element) is a subject, an object, or a predicate of the sentence. Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

(5) Evaluation Considering Correlation (Co-occurrence) Between Components The data evaluation unit 17 correlates the first component included in the target data with the second component included in the target data (co-occurrence, For example, the index for the target data can be derived in consideration of the frequency of occurrence of both at the same time. For example, when the target data includes a sentence (text) at least in part, and the first keyword (first component) “price” appears in the sentence, the data evaluation unit 17 determines that the first keyword is Based on the number of occurrences of the second keyword (second component) at a second position (for example, a position included in a predetermined range including the first position) in the vicinity of the appearing first position, the index Can be derived. Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

(6) Emotion analysis When the target data includes user evaluation information for a predetermined case, the data evaluation unit 17 is the emotion of the user who generated the target data, and is for the predetermined case generated based on the evaluation information. Emotions can be extracted from the target data (emotions included in the target data are evaluated).

For example, when data included in a website introducing a product / service (for example, an online product site, a restaurant guide) is to be analyzed, the data evaluation unit 17 is included in a comment (review) on the product / service. Components (for example, keywords such as “good”, “fun”, “bad”, “clogged”) and evaluation of the product / service (eg, “very good”, “good”, “ The target data (for example, data included in other websites) can be evaluated based on a combination (reference data) with a combination of “normal”, “bad”, and “very bad”. At this time, the data evaluation unit 17 can increase or decrease the evaluation result according to, for example, exaggerated expressions (for example, “very”, “very”, etc.). Thereby, the data analysis system further exhibits an additional effect that the accuracy of data analysis can be improved.

[Example of data analysis system processing data other than document data]
In the present embodiment, the case where the data analysis system analyzes document data is mainly assumed, and an example based on the assumption has been described. However, the system is not limited to document data (for example, audio data, image data). , Video data, etc.).

For example, when analyzing speech data, the system may analyze the speech data itself, convert the speech data into document data by speech recognition, and convert the converted document data as an analysis target. Good. In the former case, for example, the system divides the voice data into partial voices of a predetermined length to form components, and uses the voice analysis method (for example, hidden Markov model, Kalman filter, etc.) to convert the partial voices. By identifying, the voice data can be analyzed. In the latter case, a speech is recognized using an arbitrary speech recognition algorithm (for example, a recognition method using a hidden Markov model), and the procedure similar to the procedure described in the embodiment is performed on the recognized data. Can be analyzed.

When analyzing image data, the system, for example, divides the image data into partial images of a predetermined size to form components, and any image recognition method (for example, pattern matching, support vector machine, neural network) Etc.) can be used to identify the partial image.

Further, when analyzing video data, the system, for example, divides a plurality of frame images included in the video data into partial images each having a predetermined size to form a component, and an arbitrary image recognition technique (for example, a pattern The video data can be analyzed by identifying the partial image using matching, a support vector machine, a neural network, or the like.

[Example of implementation using software and hardware]
The control block of the data analysis system may be realized by a logic circuit (hardware) formed on an integrated circuit (IC chip) or the like, or may be realized by software using a CPU. In the latter case, the system includes a CPU that executes a program (control program for the data analysis system) that is software that implements each function, and a ROM (in which the program and various data are recorded so as to be readable by the computer (or CPU)). A Read Only Memory) or a storage device (these are referred to as “recording media”), a RAM (Random Access Memory) for developing the program, and the like are provided. And the objective of this data analysis system is achieved when a computer (or CPU) reads and runs the said program from the said recording medium. As the recording medium, a “non-temporary tangible medium” such as a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used. The program may be supplied to the computer via an arbitrary transmission medium (such as a communication network or a broadcast wave) that can transmit the program. The data analysis system can also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission. The above program can be implemented in any programming language, for example, a script language such as Python, ActionScript, JavaScript (registered trademark), an object-oriented programming language such as Objective-C, Java (registered trademark), HTML5, or the like Can be implemented using other markup languages. Also, any recording medium that records the above program falls within the category of this data analysis system.

[Other application examples]
In the above-described embodiment, an example has been described in which the system uses profile information to deal with an action subject's information leakage act. However, the system includes, for example, a discovery support system, a forensic system, and an e-mail monitor. Systems, medical application systems (eg, pharmacovigilance support systems, clinical trial efficiency systems, medical risk hedging systems, fall prediction (fall prevention) systems, prognosis prediction systems, diagnosis support systems), Internet application systems (eg, smart mail) System, information aggregation (curation) system, user monitoring system, social media management system, etc.), information leakage detection system, project evaluation system, marketing support system, intellectual property evaluation system, fraudulent transaction Visual system, call center escalation systems, such as credit checking system, may also be implemented as any system.

For example, when the data analysis system is realized as a discovery support system, the data analysis system uses the target data (eg, document, email, spreadsheet data, etc.) in a predetermined evaluation standard (eg, in the discovery procedure in this case). When evaluating based on whether the data should be submitted, etc., the profile information of the actor (eg, custodian) can be used. Evaluate subject data related to the subject and efficiently submit documents related to the case to the court.

In addition, when the data analysis system is realized as a forensic system, the data analysis system uses target data (for example, documents, e-mails, spreadsheet data, etc.) based on a predetermined evaluation standard (for example, the data proves criminal activity). (E.g., whether or not it is possible evidence), since the profile information of the action subject (e.g., suspect) can be used, the specific action while predicting the action of the particular action subject Evidence that proves the criminal act can be efficiently extracted by evaluating the target data related to the subject.

When the data analysis system is realized as an e-mail monitoring system, the data analysis system uses target data (e.g., e-mail, attached file, etc.) based on a predetermined evaluation standard (e.g., a user who sent and received the e-mail). (E.g., whether or not an attempt is being made to cheat) In order to be able to use the profile information of the actor (for example, the user) By accurately evaluating the target data related to the action subject, it is possible to efficiently and reliably detect signs of fraud such as information leakage and collusion.

If the data analysis system is implemented as a medical application system (for example, pharmacovigilance support system, clinical trial efficiency system, medical risk hedging system, fall prediction (fall prevention) system prognosis prediction system, diagnosis support system, etc.) The data analysis system uses target data (for example, electronic medical records, nursing records, patient diaries, etc.) based on predetermined evaluation criteria (for example, whether or not to take specific risky behaviors of patients) In order to be able to use the profile information of the action subject (eg, patient) when evaluating based on whether or not For example, predicting that the patient will be in a dangerous state (for example, falling) and efficacy of the drug , It is possible to efficiently objective evaluation.

When the data analysis system is realized as an Internet application system (for example, a smart mail system, an information aggregation (curation) system, a user monitoring system, a social media management system, etc.), the data analysis system includes target data (for example, , Whether the user's preferences are similar to other users' preferences, such as messages posted by the SNS, recommended information posted on the website, user or group profiles, etc. No., whether the user's preference and restaurant attributes match, etc.), because the profile information of the action subject (eg, the user) can be used While predicting the behavior of an action subject, When the target data to be accurately evaluated, the target data can be evaluated to display a list of other users who are likely to be interested in the user, or to present restaurant information that suits the user's preferences It is possible to efficiently execute a warning for an organization that may cause harm to the user.

In addition, when the data analysis system is realized as an information asset utilization system (project evaluation system), the data analysis system obtains effective information for the project from the information assets (target data) held by the company / experts. Since the profile information of the action subject (eg, skilled technician) can be used when extracting dynamically according to the situation of the situation, it is related to the specific action subject while predicting the action of the particular action subject The target data is evaluated, for example, (1) In order to improve the efficiency of the development site where the shortening of the development period is desired, the information on the product developed in the past is reused according to the requirements of the development. (2 ) Based on the expertise possessed by skilled engineers, it is possible to efficiently identify useful information assets.

When the data analysis system is realized as a marketing support system, the data analysis system uses target data (for example, company / individual profile, product information, etc.) based on a predetermined evaluation standard (for example, the individual is male or female). In order to be able to use the profile information of the actor (for example, the company / individual / product) Evaluating target data related to a specific behavior subject while predicting the behavior of the specific behavior subject, for example, efficiently extracting a market evaluation for a certain product is achieved.

When the data analysis system is realized as an intellectual property evaluation system, the data analysis system uses target data (for example, a patent gazette, a document summarizing the invention, an academic paper, etc.) as a predetermined evaluation standard (for example, the patent The gazette can use the profile information of the subject of action (for example, the company that owns the patent) when evaluating based on whether or not it can be a proof to reject or invalidate a given patent. In addition, the target data related to the specific action subject is evaluated while predicting the behavior of the specific action subject, for example, invalid from a large number of documents (for example, patent gazettes, academic papers, sentences published on the Internet) Achieve efficient material extraction. At this time, the data analysis system, for example, combines each claim of a patent to be invalidated with a “Related” label (classification information), and each claim of an unrelated patent different from the patent and “Non- A combination with a “Related” label (classification information) is acquired as reference data, a pattern is learned from the reference data, and an index is calculated for a large number of documents (target data) (for example, an index for each paragraph of a patent publication) The target data can be evaluated by calculating and adding a predetermined number from the top of the index to obtain the index of the patent publication.

When the data analysis system is realized as an unauthorized transaction monitoring system, the data analysis system uses target data (for example, e-mail, financial transaction information, bid information, etc.) as a predetermined evaluation standard (for example, the e-mail). When evaluating based on whether or not the user who sent and received is going to conduct fraudulent transactions), it is possible to use the profile information of the behavioral entity (for example, the entity engaged in financial transactions). While predicting the behavior of the subject, the target data related to the specific behavior subject can be evaluated to efficiently detect signs of fraud such as cartels and collusion.

Further, when the data analysis system is realized as a call center escalation system, the data analysis system uses target data (for example, telephone call history, recorded voice, etc.) as a predetermined evaluation standard (for example, a past correspondence case and the like). When evaluating based on whether or not they are similar, etc., since the profile information of the action subject (for example, the user who made the call) can be used, By evaluating target data related to the specific action subject, for example, it is possible to efficiently achieve the extraction of the most appropriate response method for the current situation from past response cases.

When the data analysis system is realized as a credit check system, the data analysis system uses target data (for example, company profile, company performance information, stock price information, press release, etc.) according to a predetermined evaluation standard ( For example, when evaluating based on whether the company goes bankrupt or whether the company grows, etc., the profile information of the action subject (eg, the company) can be used. While predicting the behavior of the action subject, the target data related to the specific action subject can be evaluated, and for example, the prediction of corporate growth and bankruptcy can be achieved efficiently.

Further, when the data analysis system is realized as a driving support system, the data analysis system uses target data (for example, data acquired from an in-vehicle sensor, a camera, a microphone, etc.) as a predetermined evaluation standard (for example, by an expert driver). When evaluating based on whether or not the skilled driver pays attention during driving, the behavior information of the action subject (for example, driver) can be used, so that the action of the specific action subject is predicted. However, it is possible to evaluate target data related to a specific action subject and efficiently achieve automatic extraction of useful information that can make driving safe and comfortable, for example.

Further, when the data analysis system is realized as a sales support system, the data analysis system uses the target data (for example, company / individual profile, product information, etc.) based on a predetermined evaluation standard (for example, the individual is male or female). The consumer ’s profile (for example, sales staff) can be used when evaluating based on whether or not the product is While predicting the behavior, it is possible to evaluate the target data related to the specific behavior subject, for example, to efficiently extract the evaluation of the market for a certain product.

Further, when the data analysis system is realized as a financial system (for example, a stock price prediction system), the data analysis system uses the target data (for example, the market price of the stock price) as a predetermined evaluation standard (for example, the stock price increases). When evaluating based on whether or not the specific action subject is predicting the action of the specific action subject, the profile information of the action subject (for example, the subject who buys and sells stocks) can be used. The target data related to the subject can be evaluated, and for example, prediction of future stock prices can be achieved efficiently.

Note that depending on the field to which the data analysis system is applied, in consideration of circumstances peculiar to the field, for example, preprocessing of the data (for example, extracting an important part from the data and only the important part is subject to data analysis) Etc.) or the manner in which the results of data analysis are displayed may be changed. It will be understood by those skilled in the art that a variety of such variations can exist, and all variations fall within the scope of the present invention.

[Summary]
A data analysis system for analyzing data related to a predetermined case that progresses through a plurality of phases in order, according to one aspect of the above-described disclosure, analyzes data related to a predetermined case that progresses through a plurality of phases in order A data analysis system comprising: a memory; an input control device; and a controller, wherein the controller generates an index for ranking a plurality of target data for the first phase, and the index is: It corresponds to the relationship between each target data and the predetermined case, and changes based on an input given by the user through the input control device, and the memory stores the plurality of target data. At least temporarily stored, and the input control device enables the user to input classification information for the sample data presented to the user. Is associated with the sample data based on the input to classify the sample data, and the controller uses the combination of the sample data and the classification information received from the user as reference data, Acquire a plurality of reference data, extract components from the plurality of reference data, the components constitute at least part of the reference data, and evaluate the degree of contribution of the components to the combination And evaluating the relevance between the plurality of target data and the predetermined case by generating the index for the first phase based on the evaluation result of the constituent element. Profile information is created based on the information, and the profile information is specific information of an action subject related to the first phase. Wherein the by actors identified by the profile information, to predict the behavior in a second phase following the first phase, it is characterized. Therefore, according to the data analysis system, the effect that the behavior related to the predetermined case of the action subject can be predicted is achieved regardless of the type of the predetermined case.

In the data analysis system according to another aspect, the first phase includes a step of fostering the predetermined case, and the second phase includes a step of executing the predetermined case, whereby the predetermined case is performed. Based on the evaluation result of the stage for fostering, the behavior of the action subject at the stage for executing the predetermined case can be predicted without going through the stage for preparing the predetermined case. The additional effect is achieved.

In the data analysis system according to another aspect, the controller obtains the profile information based on target data corresponding to indices that fall within a predetermined range from the top among the indices generated for the first phase. By creating, the additional effect that profile information can be created for an action subject having a high degree of participation in the first phase is achieved.

In the data analysis system according to another aspect, the controller further generates an additional index that ranks a plurality of additional target data for the second phase, and the additional index includes the plurality of additional target data. Of these, it corresponds to the relationship between each additional target data and the predetermined case, and changes based on an input given by the user through the input control device, and the memory includes the plurality of additional data The target data is stored at least temporarily, and the input control device enables the user to input classification information for the additional sample data presented to the user, and the classification information classifies the additional sample data. And the controller is associated with the additional sample data based on the input. A combination with the classification information received from the user is used as additional reference data, a plurality of the additional reference data are obtained, an additional component is extracted from the plurality of additional reference data, and the additional component is The plurality of additional target data, which constitute at least a part, evaluate the degree of contribution of the additional component to the combination, and generate the additional index based on the evaluation result of the additional component The profile information is updated based on the evaluation of the relationship, and the evaluation regarding the second phase is taken into consideration, and the relationship between the predetermined case and the predetermined case is considered. However, the additional effect that the behavior related to the predetermined case of the behavior subject can be predicted is achieved.

In the data analysis system according to another aspect, the profile information includes identification information of the action subject and numerical information of a degree related to the creation of the predetermined case of the action subject, thereby Based on the evaluation result, an additional effect that the action of the action subject at the stage for executing the predetermined case can be predicted is achieved.

In the data analysis system according to another aspect, the controller executes the predetermined case by generating notification information for predicting an action in the second phase by the action subject based on the numerical information. An additional effect is achieved in that the behavior of the behavior subject in the stage for performing can be easily predicted.

A method for controlling a data analysis system for analyzing data related to a predetermined case that sequentially progresses through a plurality of phases according to the above-described disclosure, wherein the data analysis system is an index for ranking a plurality of target data Is generated for the first phase, and the index corresponds to the relationship between each target data and the predetermined case, and is changed based on an input from a user. Target data is stored at least temporarily, and classification information for sample data presented to the user can be input to the user, the classification information based on the input to classify the sample data It is associated with the sample data, the reference data is a combination of the sample data and the classification information received from the user, Obtaining a plurality of the reference data, extracting constituent elements from the plurality of reference data, the constituent elements constitute at least a part of the reference data, and the degree of contribution of the constituent elements to the combination And evaluating the relevance between the plurality of target data and the predetermined case by generating the index for the first phase based on the evaluation result of the component, Profile information is created based on the evaluation, the profile information includes identification information of an action subject related to the first phase, and the first phase by the action subject specified by the profile information The control program for the data analysis system according to another disclosure is characterized by predicting the behavior in the second phase following Each step included in the control method of the data analysis system is caused to be executed by a computer, and a computer-readable recording medium according to another disclosure is characterized in that a control program of the data analysis system is recorded. Therefore, it is possible to achieve the effect that the behavior related to the predetermined case of the action subject can be predicted regardless of the type of the predetermined case.

[Additional Notes]
The present invention is not limited to the above-described embodiments, and various modifications can be made within the scope of the claims, and the technical means disclosed in different embodiments can be appropriately combined. Embodiments to be made are also included in the technical scope of the present invention. Furthermore, a new technical feature can be formed by combining the technical means disclosed in each embodiment.

The present invention can be widely applied to arbitrary computers such as a personal computer, a server device, a workstation, and a mainframe, and is particularly applicable to an artificial intelligence system.

DESCRIPTION OF SYMBOLS 1 ... Data analysis system, 2 ... Server apparatus, 3 ... Client apparatus, 4 ... Database, 5 ... Storage system, 6 ... Management computer, 10 ... Predictive coding part, 11 ... Data acquisition part, 12 …… Classification information acquisition unit, 13 …… Data classification unit, 14 …… Constituent element extraction unit, 15 …… Constituent element evaluation unit, 16 …… Constituent element storage unit, 17 …… Data evaluation unit, 18 …… Management , 19 …… Phase Analysis Department.

Claims

A data analysis system that analyzes data related to a predetermined case that progresses through multiple phases in sequence,
A memory, an input control device, and a controller;
The controller generates an index for ranking a plurality of target data for the first phase, the index corresponds to the relationship between each target data and the predetermined case, and the user It changes based on the input given through the input control device,
The memory stores the plurality of target data at least temporarily,
The input control device
The classification information for the sample data presented to the user can be input to the user, and the classification information is associated with the sample data based on the input to classify the sample data. ,
The controller is
A combination of the sample data and the classification information received from the user is used as reference data, and a plurality of the reference data are acquired.
A component is extracted from the plurality of reference data, and the component constitutes at least a part of the reference data.
Assessing the degree to which the component contributes to the combination;
Based on the evaluation result of the component, by generating the index for the first phase, to evaluate the relevance between the plurality of target data and the predetermined case,
Based on the evaluation of the relevance, create profile information, the profile information includes identification information of the action subject related to the first phase,
Predicting the action in the second phase following the first phase by the action subject identified by the profile information;
Data analysis system.
The first phase includes fostering the predetermined case;
The second phase includes executing the predetermined case.
The data analysis system according to claim 1.
The controller creates the profile information based on target data corresponding to indices that fall within a predetermined range from the top among the indices generated for the first phase.
The data analysis system according to claim 1 or 2.
The controller further generates an additional index that ranks a plurality of additional target data for the second phase, and the additional index includes each additional target data and the predetermined case among the plurality of additional target data. And changes based on the input given by the user via the input control device,
The memory stores at least temporarily the plurality of additional target data,
The input control device
Allows the user to input classification information for the additional sample data presented to the user, and the classification information is associated with the additional sample data based on the input to classify the additional sample data. Is,
The controller is
A combination of the additional sample data and the classification information received from the user is used as additional reference data, and a plurality of the additional reference data are acquired,
An additional component is extracted from the plurality of additional reference data, and the additional component constitutes at least a part of the additional reference data.
Assessing the degree to which the additional component contributes to the combination;
Evaluating the relevance between the plurality of additional target data and the predetermined case by generating the additional index based on the evaluation result of the additional component; and
Updating the profile information based on the evaluation of the relevance;
The data analysis system according to any one of claims 1 to 3.
The profile information includes identification information of the action subject and numerical information of a degree involved in fostering the predetermined case of the action subject.
The data analysis system according to claim 1.
The controller generates notification information for predicting an action in the second phase by the action subject based on the numerical information.
The data analysis system according to claim 5.
A method for controlling a data analysis system that analyzes data related to a predetermined case that progresses through a plurality of phases in order,
The data analysis system includes:
An index for ranking a plurality of target data is generated for the first phase, and the index corresponds to the relationship between each target data and the predetermined case, and is based on an input from a user Change,
Storing the plurality of target data at least temporarily;
The classification information for the sample data presented to the user can be input to the user, and the classification information is associated with the sample data based on the input to classify the sample data. ,
A combination of the sample data and the classification information received from the user is used as reference data, and a plurality of the reference data are acquired.
A component is extracted from the plurality of reference data, and the component constitutes at least a part of the reference data.
Assessing the degree to which the component contributes to the combination;
Based on the evaluation result of the component, by generating the index for the first phase, to evaluate the relevance between the plurality of target data and the predetermined case,
Based on the evaluation of the relationship, profile information is created, the profile information includes identification information of an action subject related to the first phase, and
Predicting the action in the second phase following the first phase by the action subject identified by the profile information;
Said method.
A data analysis system control program for causing a computer to execute each step included in the data analysis system control method according to claim 7.
A computer-readable recording medium on which the control program for the data analysis system according to claim 8 is recorded.