WO2023037399A1 - 情報処理装置、情報処理方法及びプログラム - Google Patents

情報処理装置、情報処理方法及びプログラム Download PDF

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Publication number
WO2023037399A1
WO2023037399A1 PCT/JP2021/032767 JP2021032767W WO2023037399A1 WO 2023037399 A1 WO2023037399 A1 WO 2023037399A1 JP 2021032767 W JP2021032767 W JP 2021032767W WO 2023037399 A1 WO2023037399 A1 WO 2023037399A1
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Prior art keywords
evaluation
data
insight
information processing
context
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English (en)
French (fr)
Japanese (ja)
Inventor
拓磨 野澤
于洋 董
昌文 榎本
昌史 小山田
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NEC Corp
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NEC Corp
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Priority to US18/685,351 priority Critical patent/US12554727B2/en
Priority to PCT/JP2021/032767 priority patent/WO2023037399A1/ja
Priority to JP2023546585A priority patent/JP7619470B2/ja
Publication of WO2023037399A1 publication Critical patent/WO2023037399A1/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs
    • G06F16/24578—Query processing with adaptation to user needs using ranking
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present invention relates to an information processing device, an information processing method, and a program.
  • instance data is generated by visualizing data to be visualized based on template data having a keyword that expresses a method of visualizing a data analysis result, and the instance data is evaluated as instance metadata. A method for regeneration based on values is described.
  • Patent Literature 1 has a problem that when the template data does not capture the user context, the presented visualization candidate is not necessarily the visualization result desired by the user.
  • One aspect of the present invention has been made in view of the above problems, and one example of its purpose is to provide a technique for displaying information that gives the insight desired by the user.
  • An information processing apparatus includes acquisition means for acquiring an evaluation data set and context data, and relevance calculation means for calculating a relevance between the context data and components of the evaluation data set. , evaluation means for evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of association; and display means for displaying information related to the insight subjects. .
  • An information processing method comprises at least one processor acquiring an evaluation data set and context data, and calculating a degree of association between the context data and components of the evaluation data set. , evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of relevance, and displaying information related to the insight subjects.
  • a program provides a computer with a process of acquiring an evaluation data set and context data, a process of calculating the degree of association between the context data and the components of the evaluation data set, and the evaluation
  • a plurality of insight subjects generated by referring to the data set for use and the degree of association are subjected to a process of evaluating and a process of displaying information related to the insight subjects.
  • FIG. 1 is a block diagram showing the configuration of an information processing device according to exemplary Embodiment 1 of the present invention
  • FIG. FIG. 3 is a flow diagram showing the flow of an information processing method according to exemplary embodiment 1 of the present invention
  • FIG. 7 is a block diagram showing the configuration of an information processing apparatus according to exemplary Embodiment 2 of the present invention
  • FIG. 7 is a flow diagram showing the flow of an information processing method according to exemplary embodiment 2 of the present invention
  • FIG. 5 is a diagram showing an example of input data according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram illustrating an example of context generation according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of a method for calculating the degree of association between context and input data according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of a method for calculating the degree of association between context and input data according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of an evaluation process according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing a display example of a visualization result according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of displaying insight subjects with evaluation results according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of displaying visualization information together with evaluation results according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of displaying insight subjects with evaluation results according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of feature vector generation according to exemplary embodiment 2 of the present invention
  • FIG. 5 is a diagram showing an example of aggregated data and statistics according to exemplary embodiment 2 of the present invention
  • FIG. 10 is a diagram showing an example of an evaluation model according to exemplary embodiment 2 of the present invention
  • FIG. 11 is a block diagram showing the configuration of an information processing apparatus according to exemplary Embodiment 3 of the present invention; It is a figure which shows an example of the computer which executes the instruction
  • FIG. 1 is a block diagram showing the configuration of an information processing device 1.
  • the information processing device 1 is a device that visualizes and displays data.
  • the information processing device 1 includes an acquisition unit 11 , a degree-of-association calculation unit 12 , an evaluation unit 13 and a display unit 14 .
  • the acquisition unit 11 acquires an evaluation data set and context data.
  • the relevance calculator 12 calculates the relevance between the context data and the components of the evaluation data set.
  • the evaluation unit 13 evaluates a plurality of insight subjects generated by referring to the evaluation data set and the degree of association.
  • the display unit 14 displays information related to the insight subject.
  • the evaluation data set is data used by the information processing apparatus 1 to evaluate visualization candidates of data.
  • the evaluation data set includes at least one of evaluation data, which is data to be visualized, and related data related to the evaluation data.
  • the data included in the evaluation data set is not limited to the examples described above, and the evaluation data set may include other information.
  • the evaluation data is data to be visualized, and is, for example, multidimensional data including multiple records. Examples of the evaluation data include data indicating monthly sales records of a certain store, data indicating the size and area of the store, data indicating product codes, product names and unit prices of products sold at the store, and/or It includes data that indicates the customer's gender, age, place of residence, occupation, etc. However, the evaluation data is not limited to this, and may be other data.
  • the evaluation data is visualized, for example, as a chart (a pie chart, a bar graph, a line graph, etc.) representing the contents of the evaluation data.
  • Related data is data related to the evaluation data.
  • the related data includes, for example, aggregated data indicating the aggregation result of the evaluation data, statistics of the aggregated data, and/or related information that is a set of various information used for visualizing the evaluation data.
  • the related information includes, for example, a part or all of the name of the data used for visualization of the evaluation data, the data type, the type of aggregation method, and the type of chart design. Note that the data included in the related data is not limited to the examples described above, and the related data may include other data.
  • Context data is data that represents what kind of insight a user seeks.
  • the context data includes, for example, at least one of a context, which is data related to the insight desired by the user, and a feature vector representing the context in a vector space. Note that the data included in the context data is not limited to the example described above, and the context data may include other data.
  • Context is data about the insight that a user seeks, an example being linguistic information extracted from a user query or metadata.
  • the context is the words “product A” and “customer” extracted from the user query "about the customer of product A.”
  • the context is the words “sales” and “transition” extracted from the user query “about sales transition”.
  • the context is, for example, the words “product A” and “customer” extracted from the metadata whose "search history” is "customer of product A”.
  • the context is, for example, the words “sales” and “transition” extracted from the metadata whose "search history” is "sales transition”.
  • the context is not limited to language information, and may be other information.
  • the context may be, for example, location information that indicates the user's location, information that indicates the degree of association between words, or information that indicates the browsing history of the site.
  • the degree of association between the context data and the constituent elements of the evaluation data set is information indicating the degree of association between the context data and the constituent elements of the evaluation data set.
  • the degree of association may be the degree of similarity between a character string that is a context and a character string that is a component included in the evaluation data set. Hamming distance, Levenshtein distance, and Jaro-Winkler distance, for example, may be used as the degree of similarity between strings.
  • the degree of association may be information representing the degree of semantic similarity of character strings.
  • the semantic similarity of character strings for example, Euclidean distance, inner product, cosine similarity, etc.
  • the degree of association may be information representing the degree of co-occurrence of character strings.
  • the degree of co-occurrence of character strings for example, the Euclidean distance, inner product, cosine similarity, etc. when the co-occurrence relationship of character strings is expressed in a vector space may be used.
  • the degree of association may be information representing the degree of similarity between the data pattern corresponding to the context and the data pattern of the constituent elements of the evaluation data set.
  • the insight subject is data generated by referring to the evaluation data set and the degree of association.
  • the insight subject includes, for example, at least one of data representing the visualization result of the evaluation data and data used to visualize the evaluation data.
  • a visualization result obtained by visualizing the evaluation data is, for example, a chart (a pie chart, a bar graph, a line graph, etc.) representing the contents of the evaluation data.
  • the insight subject may be, for example, a part of the above-described related data, such as related information included in the related data. In other words, the insight subject may be part of the evaluation data set.
  • the insight subject is not limited to the above example, and may be other data.
  • an insight refers to a visualization result that a person recognizes as useful, and data representing such a visualization result.
  • an insight is an insight subject that a person finds useful.
  • the method by which the acquisition unit 11 acquires the evaluation data set and the context data is not particularly limited.
  • the acquisition unit 11 may acquire the evaluation data set and the context data by reading them from an external storage device or an internal storage device, and may acquire the evaluation data set and the context data via the communication IF or the input/output IF. You can get context data.
  • the method for calculating the degree of association by the degree-of-association calculation unit 12 is not particularly limited.
  • the degree-of-association calculation unit 12 calculates a degree of association indicating the degree of similarity between a character string that is a context and a character string that is a component included in the evaluation data set.
  • the degree-of-association calculating unit 12 may calculate a degree of association indicating a degree of semantic similarity between a character string that is a context and a character string that is a component included in the evaluation data set.
  • the degree-of-association calculation unit 12 may calculate the degree of association representing the degree of co-occurrence between a character string that is a context and a character string that is a component included in the evaluation data set.
  • the degree-of-association calculation unit 12 may calculate the degree of association representing the degree of similarity between the data pattern corresponding to the context data and the data pattern of the constituent elements of the evaluation data set.
  • the method by which the evaluation unit 13 evaluates multiple insight subjects is not particularly limited.
  • the evaluation unit 13 calculates, for each of a plurality of insight subjects, an evaluation value that is an evaluation result of whether or not the insight desired by the user is provided.
  • this evaluation value is also called an insight score. Insight scores are a great help in discovering insight subjects that give users the insights they want even if they are output as is.
  • the insight score it is also possible to automatically detect an insight subject with a high insight score, that is, an insight subject that is likely to provide the insight desired by the user.
  • the evaluation unit 13 evaluates a plurality of insight subjects using an evaluation model in which related data and context data are input and an evaluation value is output.
  • the evaluation model may be a predefined score function, or may be a learned model constructed by machine learning.
  • the evaluation unit 13 evaluates a plurality of insight subjects using a score function that outputs a higher evaluation value as the relationship between the related data and the context data is higher, for example.
  • the methods of evaluation performed by the evaluation unit 13 are not limited to these, and other methods may be used.
  • the visualization results obtained by visualizing the evaluation data differ depending on the content of the related information used for visualization.
  • Each of a plurality of visualization results obtained by visualizing the evaluation data with a plurality of different patterns is hereinafter also referred to as a “visualization candidate”.
  • the visual features given to the user by the plurality of visualization candidates of the evaluation data are different for each of the plurality of visualization candidates.
  • the evaluation unit 13 evaluates a plurality of insight subjects according to the context data, so that a plurality of visualization candidates are evaluated according to the context data.
  • the display mode of the information on the insight subject displayed by the display unit 14 is not particularly limited.
  • the display unit 14 may preferentially display an insight subject with a relatively high evaluation by the evaluation unit 13 over an insight subject with a relatively low evaluation.
  • the display unit 14 may display the related information included in the related data and the evaluation result by the evaluation unit 13 in association with each other.
  • the acquisition unit 11 that acquires the evaluation data set and the context data calculates the degree of association between the context data and the components of the evaluation data set.
  • an evaluation unit 13 that evaluates a plurality of insight subjects generated by referring to the evaluation data set and the association degree, and information related to the insight subject is displayed.
  • a configuration including a display unit 14 is adopted. Therefore, according to the information processing apparatus 1 according to the present exemplary embodiment, it is possible to display information that provides insight desired by the user.
  • a program according to the present exemplary embodiment includes, in a computer, a process of acquiring an evaluation data set and context data, a process of calculating the degree of association between the context data and the components of the evaluation data set, and an evaluation data set.
  • a plurality of insight subjects generated by referring to and relevance are subjected to a process of evaluating and a process of displaying information related to the insight subjects. Therefore, according to the program according to the present exemplary embodiment, it is possible to display information that gives insight desired by the user.
  • FIG. 2 is a flow diagram showing the flow of the information processing method S1.
  • At step S11 at least one processor acquires an evaluation dataset and context data. Then, in step S12, at least one processor calculates the degree of association between the context data and the components of the evaluation data set. In step S13, at least one processor evaluates a plurality of insight subjects generated by referring to the evaluation data set and the degree of association. At step S14, at least one processor displays information related to the insight subject. Thus, the information processing method S1 of FIG. 2 ends.
  • one processor may be caused to execute the processes of S11 to S14, or the processes of S11 to S14 may be divided and executed by a plurality of processors. In the latter case, each processor may be provided in one information processing apparatus, or may be provided in different information processing apparatuses. At least one processor that executes the processes of S11 to S14 may be included in the information processing apparatus 1. FIG.
  • At least one processor acquires the evaluation data set and the context data, and the relationship between the context data and the constituent elements of the evaluation data set. calculating the degree, evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of relevance, and displaying information related to the insight subject, configuration is adopted. Therefore, according to the information processing method S1 according to the present exemplary embodiment, it is possible to display information that provides insight desired by the user.
  • FIG. 3 is a block diagram showing the configuration of the information processing device 1A.
  • the information processing apparatus 1A includes a control section 10A that controls all the sections of the information processing apparatus 1A, and a storage section 17 that stores various data used by the information processing apparatus 1A.
  • the information processing apparatus 1A also receives an input to the information processing apparatus 1A, a communication section 18 for the information processing apparatus 1A to communicate with other apparatuses, a display section 19 for the information processing apparatus 1A to display and output data, and the information processing apparatus 1A.
  • An input unit 20 is provided.
  • the display unit 19 displays and outputs data will be described below, the information processing apparatus 1A may output data in a form such as print output or voice output.
  • the display unit 19 and the input unit 20 may be devices external to the information processing apparatus 1A, which are externally attached to the information processing apparatus 1A.
  • the control unit 10A includes an acquisition unit 11, a degree-of-association calculation unit 12, an evaluation unit 13, a display unit 14, a first generation unit 15-1, and a second generation unit 15-2.
  • the storage unit 17 also stores an evaluation data set DS, context data CD, evaluation model parameters EMP, evaluation results ER, and display data DD.
  • evaluation data set DS includes evaluation data and related data VD related to the evaluation data.
  • Evaluation data is data to be visualized, and examples include data indicating monthly sales records of a store, data indicating the size and area of the store, product codes and product names of products sold at the store. and data indicating the unit price, and/or data indicating the sex, age, place of residence, occupation, etc. of the customer.
  • the related data VD is data related to the evaluation data.
  • Related data VD includes ⁇ Relevant information V related to evaluation data ⁇ Feature vector d V representing related information V in vector space - Aggregate data s V obtained by aggregating the data included in the evaluation data and corresponding to the related information V, and ⁇ Statistics t V of total data s V includes at least one of
  • the related information V is, for example, a set of various information used for visualization of the evaluation data, and includes the following information, for example.
  • ⁇ Attribute information of each data included in the evaluation data ⁇ Information on the aggregation method (filter, aggregation function, column name that is the key for aggregation, etc.) (information on the filter applied to the evaluation data, etc.)
  • ⁇ Information on chart design x-axis, y-axis, chart type, plot type, etc.
  • the related information feature vector dV is a representation of the related information V in a vector space. Any vectorization method may be used, but for example, distributed representation of words may be used.
  • Total data s V is data obtained by aggregating numerical values corresponding to related information V from evaluation data. Aggregated data sV is plotted on a chart as a visualization result of related information V.
  • the statistic tV of the total data sV is an array of various statistics about the total data sV . Any statistic can be used, but for example, the following can be used as the statistic tV . ⁇ Maximum value, minimum value, median value ⁇ Mean value, standard deviation, variance ⁇ Cardinality ⁇ Percentage of zero values, percentage of missing values ⁇ Kurtosis, skewness ⁇ Entropy ⁇ Gini coefficient
  • Context data CD contains - Context C, and ⁇ Feature vector d C representing context in vector space includes at least one of
  • Context C is data about the insight that the user seeks.
  • the context C is, for example, data expressing the insight sought by the user in natural language, and includes data relating to the quality and quantity of the insight sought by the user.
  • Context C may be extracted from user query Q and/or metadata M described below.
  • Context C includes, as an example, the words "merchandise A" and "customer.”
  • a feature vector d C of context C is a representation of context C in a vector space. Any vectorization method may be used, but as an example, a distributed representation of words may be used.
  • a user query Q is a query about an insight that a user seeks and is provided by the user in natural language.
  • the user query Q includes, for example, the following information. ⁇ Information about the data to be analyzed (Example: “Product A”, “Sales”) ⁇ Hypotheses about insights ⁇ Characteristics of assumed charts (e.g. aggregation by region, pie chart)
  • Metadata M is information from which insight desired by the user can be estimated. Metadata M is, for example, automatically collected by a predetermined system.
  • the metadata M includes, for example, the following information. ⁇ User's search history (eg, searching for "product A, customer”) ⁇ User's analysis history (Example: customer analysis of product A was performed in the past) - User's evaluation history (e.g., the chart about the customer of product A was highly evaluated) ⁇ User's action history (eg, stayed at the site or store selling product A for xx minutes)
  • the evaluation model parameter EMP is a parameter that defines the evaluation model f.
  • the evaluation model f is a model that inputs the related data VD and the context data CD and quantitatively evaluates the insight subject corresponding to the input related data VD. Any model can be used as the evaluation model f as long as it can be used to estimate the evaluation result of the insight subject. For example, a rule-based model to be described later, a model constructed by machine learning, or the like can be used as the evaluation model f.
  • the output of the evaluation model f is, for example, a score representing the evaluation result or a label probability. The evaluation model f will be described later.
  • the evaluation result ER is data indicating the evaluation result of the insight subject by the evaluation unit 13 .
  • the evaluation result ER is, for example, an insight score y ⁇ representing an evaluation result for each of a plurality of insight subjects.
  • the insight score ⁇ is a quantitative index of goodness of visualization calculated based on the output value of the evaluation model f.
  • the insight score ⁇ may be, for example, an output value of the evaluation model f, or may be a value obtained by applying processing such as normalization and/or weighting to the output value of the evaluation model f.
  • a specific example of the method for calculating the insight score y ⁇ will be described later.
  • the display data DD is information about an insight subject displayed by the display unit 14 .
  • the display data DD is information representing an insight object associated with the context C, for example.
  • the display data DD may include, for example, the evaluation result ER of the insight object.
  • the acquisition unit 11 acquires the evaluation data set DS and the context data CD.
  • the acquisition unit 11 acquires the evaluation data set DS and the context data CD by reading them from the storage unit 17 .
  • the method of obtaining the evaluation data set DS and the context data CD is not particularly limited.
  • the acquisition unit 11 may acquire the evaluation data set DS and the context data CD input by the user of the information processing device 1A via the input unit 20 .
  • the acquisition unit 11 may acquire the evaluation data set DS and the context data CD from an external device through communication via the communication unit 18 .
  • the relevance calculator 12 calculates the relevance between the context data and the components of the evaluation data set.
  • the evaluation unit 13 evaluates a plurality of insight subjects generated by referring to the evaluation data and the degree of association. As an example, the evaluation unit 13 calculates an insight score y ⁇ for each of a plurality of insight subjects, generates an evaluation result ER indicating the calculation result, and stores the evaluation result ER in the storage unit 17 .
  • Display unit 14 The display unit 14 displays information about the insight subject on the display unit 19 using the display data DD generated by the first generation unit 15-1.
  • the first generation unit 15-1 generates a plurality of insight subjects by referring to the evaluation data set and the degree of association. The insight subject generation processing will be described later. Also, the first generator 15-1 generates display data DD. As an example, the first generation unit 15-1 generates display data DD listing insight subjects related to the context C based on the degree of association calculated by the degree-of-association calculation unit 12. FIG.
  • the second generation unit 15-2 generates context data by referring to the reference information.
  • Reference information is, for example, user queries or metadata.
  • the second generator 15-2 generates an evaluation data set DS.
  • FIG. 4 is a flow diagram showing the flow of the information processing method.
  • the related information V is the visualization information used for visualization of the evaluation data
  • the visualization information which is an example of the related information V is also called "visualization information V.”
  • Step S101 the acquisition unit 11 acquires the input data D and the data for context generation.
  • Input data D is an example of evaluation data according to the present specification.
  • the input data D only needs to include data to be plotted on the chart, and any format can be used as the input data D format.
  • the acquisition unit 11 acquires the input data D via the input unit 20 or the communication unit 18 .
  • FIG. 5 is a diagram showing an example of input data D.
  • the input data D includes sales data, store data, product data, and customer data.
  • Sales data, store data, product data, and customer data are all data sets of multidimensional data including multiple records.
  • Sales data is multi-dimensional data including data items of "date”, “merchandise code”, “customer code”, “store code”, and "sales”.
  • the store data is multi-dimensional data including data items of "store code”, "store name”, "area”, and “scale”.
  • the product data is multi-dimensional data including data items of "product code", “product name”, “classification”, and "unit price”.
  • the customer data is multi-dimensional data including data items of "customer code", “age”, “sex”, “place of residence", "occupation”, and "income”.
  • the context generation data is data for generating context C and is an example of reference information according to this specification.
  • the data for context generation includes one or both of user query Q and metadata M, as an example.
  • the context-generating data may include multiple user queries and may include multiple metadata.
  • context generation data is not limited to user queries and metadata, and may be other data.
  • the context generation data may be data that can be used as the context C as it is.
  • the acquisition unit 11 may acquire the context generation data via the input unit 20 or the communication unit 18 , or may acquire the context generation data by reading the context generation data from the storage unit 17 .
  • step S102 the second generator 15-2 generates the evaluation data set DS and the context data CD.
  • the evaluation data set DS and the context data CD A specific example of generating the evaluation data set DS and generating the context data CD will be described below.
  • the second generator 15-2 first acquires the visualization information V.
  • the second generation unit 15-2 may acquire the visualization information V by reading it from a predetermined storage area of the storage unit 17, or acquire the visualization information V via the input unit 20 or the communication unit 18. may At this time, the second generator 15-2 acquires a plurality of pieces of visualization information V.
  • FIG. The visualization information V includes, for example, attribute information of each data included in the input data D, information on the relationship between each axis of the chart and the item, a filter applied to the input data D, a chart type, an aggregation method, and the like. Contains information.
  • the second generation unit 15-2 also uses an arbitrary language model to generate a feature vector dV that expresses the acquired visualization information V in a vector space.
  • a feature vector dV is generated for each of a plurality of pieces of visualization information V.
  • FIG. the second generation unit 15-2 generates aggregated data s V obtained by aggregating numerical values corresponding to the visualization information V from the input data D, and a statistic t V that is a set of various statistics for the aggregated data s V to generate
  • the second generation unit 15-2 generates the acquired visualization information V, the related data VD including the generated feature vector d V , the total data s V , and the statistic t V , and the An evaluation data set DS including input data D is generated.
  • the related data VD may include multiple visualizations V and multiple feature vectors dV , or may include a pair of visualizations V and feature vector dV .
  • the second generation unit 15-2 generates a context C by executing arbitrary natural language processing on the context generation data acquired by the acquisition unit 11 in step S101.
  • the second generation unit 15-2 may use the context generation data as the context C as it is.
  • FIG. 6 is a diagram showing an example of context generation.
  • the second generating unit 15-2 executes natural language processing on the user query Q1 "customer of product A” to generate context C11 of "product A” and “customer”. . Also, the second generating unit 15-2 performs natural language processing on the user query Q2 of "sales transition” to generate a context C12 of "sales” and "transition”. The second generation unit 15-2 also performs natural language processing on the metadata M1 whose “search history” is “customer of product A” to generate context C11 of “product A” and “customer”. . The second generation unit 15-2 also generates a context C12 of "sales” and “transition” by performing natural language processing on the metadata M2 whose "search history” is "sales transition".
  • the second generation unit 15-2 uses an arbitrary language model to generate a feature vector d C expressing the generated context C in a vector space, and context data including the generated feature vector d C and the context C Generate a CD.
  • Step S103 the degree-of-association calculator 12 calculates the degree of association between the context data CD and the constituent elements of the evaluation data set DS.
  • 7 and 8 are diagrams showing an example of a method of calculating the degree of association between the context data CD and the evaluation data set DS.
  • FIG. 7 shows that the context data CD includes the contexts C11 of "merchandise A" and "customer", and the input data D included in the evaluation data set DS are sales data, merchandise data, store data, and store data as shown in FIG. An example containing data and customer data is shown.
  • the degree-of-association calculation unit 12 calculates the degree of association between each of the “merchandise A” and “customer” of the context C11 and each of the plurality of components of the input data D.
  • the components of the input data D include, for example, sales data, store data, product data, customer data, and data items of each data.
  • the column of "product code” of the sales data and the row of "product A” in the item of "product name” of the product data indicate the degree of similarity of the character string with "product A" of the context C11. is high, the degree of association calculated by the degree-of-association calculator 12 is higher than that of the other components.
  • the degree of association calculated by the degree of association calculation unit 12 is higher than that of the other components. will also grow.
  • the context data CD includes context C12 of "sales” and “transition”
  • the input data D included in the evaluation data set DS are sales data, product data, and store data as shown in FIG. and customer data.
  • the degree-of-association calculating unit 12 calculates the degree of association between each of the “sales” and “transition” of the context C12 and each of the plurality of constituent elements of the input data D.
  • the "sales" column of the sales data has a high degree of similarity in the character string with "sales” in the context C12, so the degree of association calculated by the degree-of-association calculation unit 12 is higher than that of the other components. growing.
  • the degree of association calculated by the degree-of-association calculator 12 is higher than that of the other components.
  • Step S104 the first generation unit 15-1 generates a plurality of insight subjects by referring to the evaluation data set DS and the degree of association calculated in step S103.
  • the first generation unit 15-1 generates an insight subject that includes a component whose relevance degree is not zero among the components of the evaluation data set DS.
  • the insight subject generation method performed by the first generation unit 15-1 is not limited to the one described above.
  • the first generation unit 15-1 may generate an insight subject that includes components whose degree of association satisfies a predetermined condition (the degree of association is equal to or greater than a threshold).
  • step S104 the first generation unit 15-1 generates all possible visualization information in the data table if no context is given or if there is no related data component.
  • An insight subject may be generated for V.
  • the first generation unit 15-1 generates an insight subject representing a visualization result obtained by plotting aggregated data SV included in related data VD on a chart having a display mode represented by visualization information V. do. At this time, the first generating unit 15-1 generates an insight subject for each of the plurality of visualization information V, thereby generating a plurality of insight subjects. Also, since one insight subject is generated for one piece of visualization information V, the visualization information V and the insight subject correspond one-to-one. Note that the insight subject is not limited to the data representing the visualization candidate, and for example, the visualization information V may be treated as it is as the insight subject.
  • step S105 the evaluation unit 13 evaluates the insight subject.
  • the evaluation unit 13 evaluates a plurality of insight subjects according to the context data CD.
  • the evaluation unit 13 evaluates each of a plurality of insight subjects with reference to the related data VD and the context data CD. At this time, since the plurality of insight subjects correspond to the related information V on a one-to-one basis, the evaluation unit 13 evaluates each of the visualization information V. FIG. In other words, the evaluation unit 13 evaluates each of the plurality of insight subjects for each related information V included in the related data VD.
  • the evaluation unit 13 receives at least part of the relevant data and the context data, and uses an evaluation model that outputs an evaluation value to evaluate a plurality of insight subjects.
  • FIG. 9 is a diagram showing an example of evaluation processing performed by the evaluation unit 13. As shown in FIG. In the example of FIG. 9, the evaluation unit 13 receives visualization information V, context data CD, and input data D, and uses an evaluation model f that outputs an insight score y ⁇ to evaluate a plurality of insight subjects. I do.
  • the evaluation model f may be a predefined score function or a learned model constructed by machine learning. An evaluation method using the evaluation model f will be described later.
  • the insight score y ⁇ for each visualization information V based on the evaluation model f is stored in the storage unit 17 as the evaluation result ER.
  • step S106 the display unit 14 displays information related to the insight subject.
  • the display unit 14 displays information listing the visualization information V related to the context based on the degree of association calculated by the degree-of-association calculation unit 12 .
  • the display unit 14 may display information listing all insight subjects generated by the first generation unit 15-1 when there is no context or when there are no related insight subjects.
  • the display unit 14 displays at least one of the plurality of insight subjects together with the evaluation result by the evaluation unit 13 or in a display mode according to the evaluation result by the evaluation unit 13.
  • the display mode according to the evaluation result includes, for example, display order or display size.
  • the display unit 19 may preferentially display an insight subject with a relatively high evaluation by the evaluation unit 13 over an insight subject with a relatively low evaluation by the evaluation unit 13 .
  • FIG. 10 is a diagram showing a display example 1 of visualization results.
  • Charts C101 to C103 are displayed in descending order of insight score y ⁇ .
  • information V101 to V103 including the insight score y ⁇ and the visualization information V corresponding to each of the charts C101 to C103 are displayed in association with the charts C101 to C103.
  • evaluation buttons C111 to C113 for the user to evaluate the visualization results are displayed for each of the charts C101 to C103.
  • a search window C114 and a re-evaluation button C115 for re-evaluating the input data D are also displayed. Re-evaluation of the input data D will be described later.
  • the user can grasp information such as "the number of customers for product A is decreasing year by year”. Further, by displaying the chart C102, the user can grasp information such as "the age of the main customer of the product A is in their twenties”.
  • FIG. 11 is a diagram showing an example of displaying insight subjects together with evaluation results.
  • insight subjects V7, V3, V8, .
  • the insight score y ⁇ of each insight subject is displayed adjacent to each of the insight subjects V7, V3, V8, .
  • a plurality of insight subjects V7, V3, V8, . . . are displayed in descending order of insight score ⁇ .
  • a plurality of insight subjects are displayed in descending order of insight score ⁇ , so that the user can easily grasp which insight subject has a high evaluation.
  • FIG. 12 is a diagram showing an example of displaying the visualization information V together with the evaluation results.
  • the display unit 14 displays each related information V included in the related data and the evaluation by the evaluation unit 13 in association with each other.
  • the display unit 19 displays the visualized information V11 to V18 and the insight score y ⁇ corresponding to each of the visualized information V11 to V18 in association with each other.
  • FIG. 13 is a diagram showing an example of displaying insight subjects together with evaluation results.
  • the display unit 14 displays a chart (bar graph) that is a visualization result of the input data D, and also displays an insight score y ⁇ corresponding to the displayed chart together with the chart.
  • the user can preferentially view visualization results that are highly likely to provide the insight the user desires.
  • step S105 (Specific example of evaluation in step S105) Next, a specific example of evaluation performed by the evaluation unit 13 in step S105 will be described with reference to FIGS. 14 to 16.
  • FIG. For example, the evaluation unit 13 evaluates the insight subject using the feature vector d C , the feature vector d V , the total data s V , and the statistic t V .
  • FIG. 14 is a diagram showing an example of generation of the feature vector dC and the feature vector dV .
  • a feature vector dV is generated from the visualization information V
  • a feature vector dC is generated from the context C.
  • FIG. 14 is a diagram showing an example of generation of the feature vector dC and the feature vector dV .
  • a feature vector dV is generated from the visualization information V
  • a feature vector dC is generated from the context C.
  • FIG. 15 is a diagram showing an example of total data s V and statistics t V generated by the second generation unit 15-2.
  • the aggregated data sV is data obtained by aggregating the data included in the input data D and corresponding to the visualization information V.
  • the statistic tV is data representing the statistic of the aggregated data sV .
  • rule-based evaluation and learning-based evaluation will be described as specific examples of the evaluation performed by the evaluation unit 13 .
  • the evaluation unit 13 uses the related data VD to calculate the score y 0 ⁇ , and uses the score y 0 ⁇ to calculate the insight score ⁇ . At this time, the evaluation unit 13 may use the score y 0 ⁇ as it is as the insight score y ⁇ , or may calculate the insight score y ⁇ by adding processing such as normalization or weighting to the score y 0 ⁇ . good too.
  • the method of calculating the score y 0 ⁇ is not limited, but the evaluation unit 13 may use, for example, a score function defined on a rule basis for each type of insight, or learn the feature amount of the chart that provides the insight.
  • the score y 0 ⁇ may be calculated using a model that
  • the score function is, for example, a function that outputs a higher evaluation value as the relationship between the related data VD and the context data CD is higher.
  • the evaluation unit 13 evaluates a plurality of insight subjects using a score function defined in advance that outputs a higher evaluation value as the relationship between the related data VD and the context data CD is higher. to evaluate.
  • the evaluation unit 13 sets the insight score ⁇ for the related data VD having low relevance to the context data CD to zero or a negative value so as to lower the evaluation result.
  • the method of calculating the degree of association (similarity) between the context data CD and the related data VD is not limited, the evaluation unit 13 may, for example, calculate the similarity of sets (Jaccard, Dice, Simpson, etc.), the similarity of character strings, (Hamming distance, Levenshtein distance, Jaro-Winkler distance, etc.) and distributed representation (word2vec, fastText, BERT, etc.).
  • the evaluation unit 13 may also calculate the insight score y using a score weighted by the similarity between the context data CD and the related data VD. More specifically, for example, the insight score y ⁇ may be the product of the score y0 ⁇ calculated using the related data VD and the similarity sim(CD, Dv ).
  • the evaluation unit 13 uses an evaluation model f that is a pre-learned evaluation model, to which the related data VD and the context data CD are input, and which outputs an evaluation value, to a plurality of insight subjects. evaluation.
  • the machine learning method of the evaluation model f is not limited, and as an example, a decision tree-based, linear regression, or neural network method may be used, or one or more of these methods may be used. good.
  • Decision tree bases include, for example, LightGBM (Light Gradient Boosting Machine) and XGBoost.
  • Linear regression includes, for example, support vector regression, Ridge regression, Lasso regression, and ElasticNet.
  • Neural networks include, for example, deep learning.
  • any teacher data that can be considered to have insight can be used.
  • charts created by data analysts in the past may be considered to contain features that give insight, and their visualization information V may be used for learning as positive samples.
  • chart visualization information V that is considered to have no insight may be used as a negative sample for learning.
  • FIG. 16 is a diagram showing an example of the evaluation model f.
  • the input of the evaluation model f includes the feature vector dV , the feature vector dC, the summary data Sv , and the statistic tv .
  • the output of the evaluation model f is an evaluation result, for example, a label probability indicating whether the insight desired by the user is provided.
  • Example 1 of learning-based evaluation model When a teacher label y regarding an insight of the visualization information V is given, an evaluation model can be learned as a classification model. For example, when y ⁇ ⁇ 0, 1 ⁇ is 1, there is insight, and when it is 0, there is no insight, as a two-class classification task, for example, by the following equation (1) A machine learning model that minimizes the given loss function E( ⁇ ) should be learned.
  • Equation (1) N is the number of learning data.
  • Example 2 of learning-based evaluation model
  • an evaluation model can be learned as a regression model. For example, if y is the score given by the teacher data, a machine learning model that minimizes the loss function E( ⁇ ) given by the following equation (2) may be trained.
  • Equation (2) N is the number of learning data.
  • the output of the machine learning model that minimizes the above loss function is a score that expresses the goodness of visualization in the same way as the score of the training data, and may be used as the insight score y ⁇ .
  • FIG. 17 is a block diagram showing the configuration of an information processing device 1B according to this exemplary embodiment.
  • the information processing device 1B includes a control unit 10B instead of the control unit 10A of the information processing device 1A according to the second exemplary embodiment.
  • the control unit 10B includes a learning unit 16 in addition to the obtaining unit 11, the degree-of-association calculation unit 12, the evaluation unit 13, the display unit 14, the first generation unit 15-1 and the second generation unit 15-2.
  • the input unit 20 receives feedback from the user on the evaluation results of the evaluation unit 13.
  • the evaluation unit 13 refers to the feedback from the user and evaluates the multiple insight subjects again.
  • the acquisition unit 11 acquires context data reflecting the feedback.
  • the relevance calculator 12 calculates the relevance between the context data reflecting the feedback and the components of the evaluation data set.
  • the evaluation unit 13 evaluates a plurality of insight subjects generated by referring to the evaluation data set and the degree of association.
  • the context and user query can be updated at any time.
  • the context and user query are updated, for example, by the user entering a character string into the search window C114 of FIG. 10 and selecting the re-evaluate button C115.
  • the information processing device 1B After updating the context and the user query, the information processing device 1B again executes the context data acquisition process and the information display process related to the insight subject, thereby switching the information displayed on the display unit 19 .
  • the unit 11 generates context data based on the user's operation content, and executes the information processing method S1A shown in FIG. 4 using the generated context data. As a result, information reflecting the user's feedback is displayed.
  • the learning unit 16 may relearn the evaluation model f with reference to feedback from the user.
  • the learning unit 16 stores, for example, the user's operation history regarding information (insight score y ⁇ , visualization information V, chart, etc.) related to the insight subject displayed by the display unit 19 as feedback from the user. Recorded in part 17 or the like.
  • the user's operation history includes, for example, the display time of the information related to the insight subject, the pressing of the evaluation button for the information related to the insight subject, and the like.
  • the learning unit 16 re-learns the evaluation model f reflecting the feedback from the user. For example, the learning unit 16 performs re-learning of the evaluation model f by using high-evaluation visualization information V as a positive sample and low-evaluation visualization information as a negative sample.
  • the input unit 20 receives feedback from the user on the evaluation result
  • the evaluation unit 13 refers to the feedback from the user and evaluates a plurality of insight subjects. , evaluate again. Therefore, according to the information processing apparatus 1B according to the present exemplary embodiment, in addition to the effects of the information processing apparatus 1 according to the first exemplary embodiment, it is possible to further improve the accuracy of the evaluation of the insight subject. effect is obtained.
  • the processing performed by one information processing apparatus 1 may be shared by a plurality of information processing apparatuses. In other words, part of the processing performed by the information processing device 1 may be performed by at least one other information processing device. In other words, when at least one processor performs each of the processes described above, the at least one processor may be provided in one information processing apparatus 1, or may be provided in different information processing apparatuses. It may be something that is This also applies to the information processing device 1A in the second exemplary embodiment and the information processing device 1B in the third exemplary embodiment described above.
  • Some or all of the functions of the information processing apparatuses 1, 1A, and 1B may be implemented by hardware such as integrated circuits (IC chips), or may be implemented by software.
  • the information processing apparatuses 1, 1A, and 1B are implemented by computers that execute program instructions, which are software that implements each function, for example.
  • An example of such a computer (hereinafter referred to as computer C) is shown in FIG.
  • Computer C comprises at least one processor C1 and at least one memory C2.
  • a program P for operating the computer C as the information processing apparatuses 1, 1A, and 1B is recorded in the memory C2.
  • the processor C1 reads the program P from the memory C2 and executes it, thereby realizing each function of the information processing apparatuses 1, 1A, and 1B.
  • processor C1 for example, CPU (Central Processing Unit), GPU (Graphic Processing Unit), DSP (Digital Signal Processor), MPU (Micro Processing Unit), FPU (Floating point number Processing Unit), PPU (Physics Processing Unit) , a microcontroller, or a combination thereof.
  • memory C2 for example, a flash memory, HDD (Hard Disk Drive), SSD (Solid State Drive), or a combination thereof can be used.
  • the computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and temporarily storing various data.
  • Computer C may further include a communication interface for sending and receiving data to and from other devices.
  • Computer C may further include an input/output interface for connecting input/output devices such as a keyboard, mouse, display, and printer.
  • the program P can be recorded on a non-temporary tangible recording medium M that is readable by the computer C.
  • a recording medium M for example, a tape, disk, card, semiconductor memory, programmable logic circuit, or the like can be used.
  • the computer C can acquire the program P via such a recording medium M.
  • the program P can be transmitted via a transmission medium.
  • a transmission medium for example, a communication network or broadcast waves can be used.
  • Computer C can also obtain program P via such a transmission medium.
  • (Appendix 1) Acquisition means for acquiring the evaluation data set and the context data; relevance calculation means for calculating a relevance between the context data and the components of the evaluation data set; evaluation means for evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of association; display means for displaying information related to the insight subject; Information processing device.
  • the first generation means is The information processing apparatus according to appendix 2, which generates an insight subject including a component whose degree of association is not zero among components of the evaluation data set.
  • the evaluation data set includes evaluation data and related data related to the evaluation data, 4.
  • the information processing apparatus according to any one of appendices 1 to 3, wherein the evaluation unit evaluates each of the plurality of insight subjects for each related information included in the related data.
  • the insight subject can be evaluated for each related information.
  • Appendix 6 The information processing apparatus according to appendix 4 or 5, wherein the display means associates and displays each related information included in the related data and the evaluation result by the evaluation means.
  • the user can grasp the evaluation of each of the plurality of insight subjects from the information displayed by the display means.
  • the display means is Information according to any one of Appendixes 1 to 6, wherein at least one of the plurality of insight subjects is displayed together with the evaluation result by the evaluation means or in a display mode according to the evaluation result by the evaluation means. processing equipment.
  • the insight subject displayed by the display means makes it easier for the user to grasp the evaluation of the insight subject.
  • Appendix 8 The information processing apparatus according to any one of appendices 1 to 7, wherein the evaluation means performs evaluation on a plurality of insight subjects according to the context data.
  • Appendix 9 The information processing apparatus according to any one of appendices 1 to 8, further comprising second generation means for generating the context data with reference to reference information.
  • Appendix 10 further comprising receiving means for receiving feedback from the user on the evaluation result of the evaluation means; 10.
  • the information processing apparatus according to any one of appendices 1 to 9, wherein the evaluation unit refers to feedback from the user and performs re-evaluation on the plurality of insight subjects.
  • the obtaining means obtains context data reflecting the feedback
  • the relevance calculating means calculates a relevance between the context data reflecting the feedback and the components of the evaluation data set, 11.
  • the information processing apparatus according to appendix 10, wherein the evaluation means evaluates a plurality of insight subjects generated by referring to the evaluation data set and the degree of association.
  • Appendix 12 at least one processor obtaining an evaluation dataset and context data; calculating the degree of association between the context data and the components of the evaluation data set; evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of relevance; and displaying information related to the insight subject; Information processing method including.
  • the processor performs acquisition processing for acquiring an evaluation data set and context data; association degree calculation processing for calculating a degree of association between the context data and components of the evaluation data set; executing an evaluation process of evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of association, and a display process of displaying information related to the insight subjects.
  • Information processing equipment performs acquisition processing for acquiring an evaluation data set and context data; association degree calculation processing for calculating a degree of association between the context data and components of the evaluation data set; executing an evaluation process of evaluating a plurality of insight subjects generated by referring to the evaluation data set and the degree of association, and a display process of displaying information related to the insight subjects.
  • this information processing apparatus may further include a memory, and this memory stores information for causing the processor to execute the acquisition process, the degree-of-association calculation process, the evaluation process, and the display process.
  • program may be stored. Also, this program may be recorded in a computer-readable non-temporary tangible recording medium.

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