KR20080096004A - Data storage and inquiry method for time series analysis of weblog and system for executing the method - Google Patents

Abstract

A data storage and an inquiry method for time series analysis of weblog are provided to classify the weblog to field name, floating field, floating field tuple, and floating field relation data, thereby performing weblog analysis easily, and efficiently. A floating field relation data is generated based on weblog and the generation time of weblog in the preprocess phase(S310). The floating field relation data is processed by the data operator inputted through a user's terminal(S320). The preprocess phase comprises followings. A data is extracted from the weblog by weblog parsing(S311). The data is classified by the user login identifier contained in the weblog(S312). The floating field relation data is generated by arranging the data by the generation time order about the same user login identifier(S313).

Description

DATA STORAGE AND INQUIRY METHOD FOR TIME SERIES ANALYSIS OF WEBLOG AND SYSTEM FOR EXECUTING THE METHOD}

1 is an example for explaining a problem of a relational database based on a relational data model in the related art.

2 is an example showing an overview of a data storage and inquiry system according to the first embodiment of the present invention.

3 is a flowchart illustrating a weblog-based data storage and retrieval method according to a first embodiment of the present invention.

4 is an example for explaining a join operation according to the present invention.

5 is an example for explaining a split operation according to the present invention.

6 is a flowchart illustrating a data storage and inquiry method according to a second embodiment of the present invention.

7 is a block diagram for explaining an internal configuration of a data storage and inquiry system according to a third embodiment of the present invention.

<Explanation of symbols for the main parts of the drawings>

700: data storage and inquiry system

701: flow field relationship data generation unit

702: Floating Field Relationship Database

703: Data Operator Processing Unit

The present invention relates to a data storage and retrieval method for time series analysis of a weblog and a system for performing the method. More particularly, the time series analysis of the weblog in data, in particular in a weblog The present invention relates to a data storage and retrieval method and system suitable for use.

Many of the current commercial database products employ a relational data model. Although there was a hierarchical data model and a network data model used as the existing data model, the structure of the relational data model was more flexible than the existing data model, so it was implemented in many database systems because it could reflect the real world more realistically. As a result, relational database management system (RDBMS) products that support relational data models dominate the database market.

This relational data model is basically composed of three core components as follows, and it is the concept that all the work systems of the real world can be expressed in all three below.

1.Entity: An event or thing to be systemized.

2. Relationship: Association between objects and properties.

3. Attribute: A unit of information that can no longer be broken down to indicate the nature of the entity or relationship.

However, in the relational database developed based on the relational data model, the number of fields is fixed in advance. Therefore, if additional information to be expressed according to a situation occurs, the relational database cannot effectively express it. Thus, to solve this problem, relational databases sometimes use extra methods, such as creating a temporary field and using it temporarily.

Also, relational databases do not represent the repetition of multiple values in a field. For example, a list of products purchased by a customer cannot be represented directly in the relational data model. Therefore, it is necessary to divide the information into separate tables and use the joins to make the information available later. It is common and recommended by relational data models.

1 is an example for explaining a problem of a relational database based on a relational data model in the related art.

The relational data model depends only on the value bound per associated record, as shown at 110. In other words, the order between the listed records has no meaning. That is, time series analysis is not possible for the values 'b' 112 and 'c' 113 corresponding to the same person 'Park' 111. Since there is no indexing mechanism that effectively expresses the relationship between attributes because of the use of predefined and fixed order of attributes, there is a need for a process of reconstructing the entire record as shown by reference numeral 120.

The development method using the relational data model like this makes the analysis process difficult in time series analysis of the weblog. In attempting to record a person's behavioral patterns, limitations in the relational data model require that a person's behavioral patterns be recorded separately in different tables and in different records.

Therefore, in order to track the relationship between behavior patterns recorded separately, it is necessary to use operations such as joins, which are very expensive, and it is very difficult to describe Structured Query Language (SQL), and even written SQL is very difficult to process the SQL. There is a problem of having a complicated structure.

The present invention proposes a new method for a data storage and retrieval method for time series analysis of weblogs and a system for performing the method to solve the above problems of the prior art.

The present invention relates to a floating field that is a collection of field names and field value pairs, a floating field tuple that is a time series listing of the flow field, and a flow field relationship that is a collection of floating field tuples. An object of the present invention is to easily and simply perform time series analysis of the weblog by configuring, storing, and inquiring into floating field relation data.

It is another object of the present invention to provide a data model capable of generating, storing, and retrieving the flow field relationship data for all data requiring time series analysis as well as the weblog.

In order to achieve the above object and to solve the above problems of the prior art, weblog (weblog) based data storage and retrieval method according to an embodiment of the present invention, the weblog and the generation time of the weblog And a pre-processing step of generating and maintaining floating field relation data based on the data, and processing the floating field relation data according to a data operator input through a user terminal.

According to one aspect of the invention, the pre-processing step, parsing a weblog (parsing) to extract data from the weblog, classifying the data according to the user login identifier that the weblog and the same user And generating the floating field relation data by sorting the data in the order of occurrence time with respect to a login identifier.

According to another aspect of the invention, the flow field relationship data may comprise at least one floating field tuple, wherein the flow field tuple is a flow field that is a collection of pairs of field names and field values. The occurrence time may be arranged in order. In this case, the field name may define an operation or state of a user corresponding to a user login identifier, and the field value may include an actual value corresponding to the operation or state.

According to another aspect of the present invention, the data operator may include at least one of a join operator, a split operator, and a select-and-project operator. The step of processing the flow field relationship data according to the data operator input through (1) combining the flow field tuples included in the flow field relationship data according to the join operator, or (2) in accordance with the split operator The flow field tuple may be divided into a plurality of flow field tuples, or (3) a value may be extracted from the flow field relation data according to the selection and project operator and provided to the user terminal.

In another embodiment of the present invention, the data storage and retrieval method includes a pre-processing step of classifying data by identifier and generating flow field relationship data by sorting the data in order of occurrence time of the data for the same identifier. do.

Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

2 is an example showing an overview of a data storage and inquiry system according to the first embodiment of the present invention.

The log collecting unit 201 receives the web logs transmitted from the respective web servers, and the preprocessing unit 202 integrates the web logs to extract data from the web logs. In this case, the preprocessor 202 may generate flow field relationship data that is a set of at least one flow field tuple through the data.

In this example of FIG. 2, the flow field relationship data is stored in the distribution system 203. That is, the flow field relationship data may be stored in the flow field relationship database of this distributed system 203.

As such, the stored flow field relationship data is inquired through data operators 204 such as a join operator, a split operator, a selection and a project operator input from a user terminal, and the result of the inquiry is processed and visualized (205). It may be provided as a user terminal.

3 is a flowchart illustrating a weblog-based data storage and retrieval method according to a first embodiment of the present invention.

The data storage and retrieval system for performing weblog-based data storage and retrieval at step S310 generates and maintains flow field relationship data based on the weblog and the occurrence time of the weblog. In this case, the flow field relationship data may include at least one flow field tuple, and the flow field tuple may be a flow field, which is a set of pairs of field names and field values, in the order of occurrence time. In addition, the field name may define an operation or state of a user corresponding to a user login identifier, and the field value may include an actual value corresponding to the operation or state.

For example, in the case of having 'id' as the field name and 'Kim' as a field value corresponding to the field name, the data storage and inquiry system may know that a user login identifier of any user is 'Kim'. have. The pair of the field name and the field value may be represented as <id = Kim> as the floating field. As another example of the floating field, in the case of having another floating field in addition to the <id = Kim>, that is, 'node' indicating the web page visited by the user as the field name and the web page as the field value. In the case of having a 'home' meaning an actual value of, the data storage and inquiry system may know that the user login identifier 'Kim' has visited 'home'.

As such, the floating fields can be sorted in the order of occurrence time for the same user login identifier, and the sequence of sorted floating fields can be defined as the floating field tuple. That is, the floating field tuple may include data about a state or an operation of a user having the same user login identifier in the order of occurrence of the state or the action.

In other words, the flow field relationship data, which is a set of the flow field tuples through the flow field tuples, may mean data on the operation and status of each user included in the weblog over time. This enables time series analysis of the weblog.

For example, using a floating field tuple such as <id = Kim> <node = Main Home> <node = Mail> <node = Main Home> <node = Game A> allows the user to log in the data storage and retrieval system. It can be seen that the user using the identifier 'Kim' accesses the 'mail' web page through 'main home' and accesses the web page for 'game A' through 'main home'. In other words, after checking the 'mail' through the 'main home' it is possible to query the user accessing the 'game A' through the 'main home' again.

In order to generate and maintain the flow field relationship data, the data storage and retrieval system may include steps S311 to S313 as shown in FIG. 3 in step S310.

In step S311, the data storage and retrieval system parses a weblog to extract data from the weblog. In other words, the data storage and retrieval system extracts structured information, ie, the data, to generate the flow field relationship data from the weblog.

In step S312, the data storage and inquiry system classifies the data according to the user login identifier included in the weblog. In general, the visit log by several people is loaded in chronological order. Also, since the visit log is typically loaded through a plurality of web servers, records for the same user may be distributed and stored in different web servers. Therefore, after all the web logs distributed and stored in the entire web server is combined, it is necessary to collect the data corresponding to each user and classify them according to the user login identifier.

In step S313, the data storage and inquiry system sorts the data in the order of occurrence time for the same user login identifier to generate the floating field relationship data. That is, the data storage and inquiry system may generate the floating field relationship data by sorting the data classified according to the user login identifier in the order of occurrence time according to the user login identifier. In this case, each of the data may correspond to each of the flow fields described above.

In step S320, the data storage and retrieval system processes the floating field relationship data according to a data operator input through a user terminal. In this case, the data operator may include at least one of a join operator, a split operator, and a select-and-project operator.

In this case, the data storage and retrieval system combines (1) the flow field tuples included in the flow field relationship data according to the join operator to process the flow field relationship data according to the data operator, or (2) the According to the split operator, the flow field tuple may be divided into a plurality of flow field tuples, or (3) a value may be extracted from the flow field relation data according to the selection and project operator and provided to the user terminal. Here, the value may mean a set of actual values included in each of the plurality of flow fields.

In addition, the data operator may include a plurality of operators among the join operator, the split operator, and the selection and project operators. That is, it is also possible to extract a value after separating one flow field tuple into a plurality of flow field tuples or to extract a value after combining a plurality of flow field tuples into one flow field tuple.

4 is an example for explaining a join operation according to the present invention.

As described above, the floating field tuple is a sequence of the floating fields, which is a set of information corresponding to one subject, ie one user. When analyzing the flow field tuple, there may be a case where one user wants to analyze the behavioral pattern for one week or for more than one month. In this case, the flow field must be collected and created as a flow field tuple for a period of one month or more for the user, rather than for the sake of storage or technical reasons, creating the entire flow field as a single flow field tuple. It is convenient to dynamically generate the floating field tuples for any period.

That is, the flow field for the behavior pattern of the user during the desired time unit by generating a plurality of flow field tuples for one user in a short time unit, and combining the flow field tuples using the join operator as needed You can get a tuple. For example, by generating the flow field tuple on a daily basis, by combining the flow field tuple corresponding to January 2 and the flow field tuple corresponding to January 3 through the join operator, from January 2 to 1 It is possible to create a floating field tuple for a period up to 3 days per month.

When the join operator is input through a user terminal, the data storage and retrieval system may perform a join operation according to a condition included in the join operator. That is, the join operation may include an operation of generating one flow field tuple by combining a plurality of flow field tuples corresponding to the condition.

That is, the data storage and retrieval system performs a join operation 403 on the first flow field tuple 401 and the second flow field tuple 402 as shown in FIG. 4 to generate a third flow field tuple 404. ) Can be created. At this time, as can be seen in the third flow field tuple 404, the flow fields may be arranged in order of occurrence time.

5 is an example for explaining a split operation according to the present invention.

The data storage and retrieval system may perform a split operation according to a split operator input through a user terminal and a condition included in the split operator. The split operation may be an operation of separating the flow field tuple selected as the operation opposite to the join operation described with reference to FIG. 4 into flow field tuples of a meaningful unit.

In general, visits from meaningful users are counted every 30 minutes. That is, if any action of the user does not occur for 30 minutes, it is common to analyze that any action of the user is lost. Therefore, even if the flow field tuple is configured on a daily basis, it may be necessary to separate the flow field tuple on a 30-minute basis, and the data storage and inquiry system needs to perform the split operation.

That is, the data storage and retrieval system may perform a split operation 502 on the first flow field tuple 501 in which the flow fields are arranged in order of occurrence time, as shown in FIG. 5. 5, the first flow field tuple 501 is divided into a plurality of flow field tuples 503. The first flow field tuple 501 is separated by 30 minutes. This unit of time may be included as the condition in the split operator. In addition, the condition may include selecting the first flow field tuple 501.

Lastly, among the data operators described above, the selection and project operators are for finding a specific pattern in the floating field relation data and extracting a value within the specific pattern. The syntax for the selection and project operators is a general regular expression ( regular expression).

For example, for the selection and project operators entered in the form of <id = Kim> (<node = (\ w *)>) * via a user terminal, the data storage and inquiry system is a user login identifier as' It is possible to find all nodes visited by a user using Kim ', extract the actual values corresponding to all the nodes, and provide them to the user terminal.

As another example, when a selection and project operator such as <id = Lee> <node = home> (<node = (\ w *)>) * is inputted, the data storage and inquiry system may use 'Lee' as the user login identifier. A user using 'finds' all nodes visited immediately after visiting 'home', and extracts and provides actual values corresponding to all the nodes. In this case, the node may mean a web page.

As described above, according to the present invention, the weblog consists of a flow field, which is a set of field name and field value pairs, a flow field tuple, which is a time series listing of the flow field, and flow field relationship data, which is a set of the flow field tuples. By storing and querying, time series analysis of the weblog can be easily and conveniently performed.

6 is a flowchart illustrating a data storage and inquiry method according to a second embodiment of the present invention.

The data storage and retrieval system for storing and retrieving data at step S601 classifies the data by identifier, and sorts the data in the order of occurrence time of the data for the same identifier to generate the flow field relationship data. In this case, the flow field relationship data may include at least one flow field tuple, and the flow field tuple may include a flow field, which is a set of pairs of field names and field values, in the order of occurrence time. . In this case, the field name may define an operation or state of a user corresponding to the identifier, and the field value may include an actual value corresponding to the operation or state.

Such data may include all data for which time series analysis is required. That is, the flow field relationship data may be generated by assigning the same identifier to the data requiring time series analysis and sorting the data having the same identifier in the order of occurrence time. For example, the data may be generated by parsing a weblog, and the identifier may include a user login identifier included in the weblog. In this case, one data may have the field name and the field value as the floating field, and data having the same identifier may be configured as the floating field tuple.

In step S602, the data storage and retrieval system stores and maintains the flow field relationship data in a flow field relationship database. The flow field relationship data may be changed according to a data operator to be described later and stored again in the flow field relationship database or used to retrieve and extract a corresponding actual value.

In step S603, the data storage and retrieval system changes the flow field relation data or extracts the value of the flow field relation data according to a data operator input through a user terminal. Here, the value may mean a set of actual values included in each of the plurality of floating fields, and the data operator may include at least one of a join operator, a split operator, and a selection and project operator.

The join operator may correspond to a join operation for changing the floating field relationship data, and the join operation combines different floating field tuples of the same identifier specified by the join operator to generate one floating field tuple. Can be. That is, floating field tuples containing the same identifier can be combined by the join operation.

In addition, the split operator may correspond to a split operation for changing the floating field relationship data, and the split operation may include a plurality of floating field tuples according to a time unit in which the split operator includes one floating field tuple designated by the split operator. Operation can be separated by. In other words, one floating field tuple may be separated by the split operation into a plurality of floating field tuples having the same identifier according to the time unit.

Finally, the selection and project operator may correspond to a selection and project operation for extracting the value of the flow field relationship data, wherein the selection and project operation is based on a condition included in the selection and project operator in the flow field relationship database. It may be an operation of extracting a value in the searched specific pattern by searching for a specific pattern according to the search. Such selection and project operators can use common regular expressions.

Here, the data operator may include a plurality of operators among the join operator, the split operator, and the selection and project operators. That is, it is also possible to extract a value after separating one flow field tuple into a plurality of flow field tuples or to extract a value after combining a plurality of flow field tuples into one flow field tuple.

That is, according to the present invention, the weblog is stored and viewed by configuring a flow field as a set of field name and field value pairs, a flow field tuple as a time series listing of the flow field, and a flow field relation data as a set of the flow field tuples. By doing so, it is possible to easily and simply perform time series analysis of the weblog, and provide a data model for generating, storing, and retrieving the flow field relationship data for all data requiring time series analysis.

7 is a block diagram for explaining an internal configuration of a data storage and inquiry system according to a third embodiment of the present invention. As shown in FIG. 7, the data storage and retrieval system 700 includes a flow field relationship data generator 701, a flow field relationship database 702, and a data operator processor 703.

The flow field relationship data generation unit 701 classifies the data by identifier, and generates the flow field relationship data by sorting the data in the order of occurrence time of the data for the same identifier. In this case, the flow field relationship data may include at least one flow field tuple, and the flow field tuple may include a flow field, which is a set of pairs of field names and field values, in a chronological order. In addition, the field name may define an operation or state of a user corresponding to the identifier, and the field value may include an actual value corresponding to the operation or state.

The fluid field relationship database 702 stores and maintains the fluid field relationship data.

The data operator processor 703 changes the flow field relation data or extracts the value of the flow field relation data according to a data operator input through a user terminal. Here, the value may mean a set of actual values included in each of the plurality of floating fields, and the data operator may include at least one of a join operator, a split operator, and a selection and project operator.

Here, the join operator may correspond to a join operation for changing the floating field relationship data, and the join operation combines different floating field tuples of the same identifier designated by the join operator to generate one floating field tuple. It can be an operation. That is, floating field tuples containing the same identifier can be combined by the join operation.

The split operator may correspond to a split operation for changing the flow field relationship data, and the split operation may include a plurality of flow fields according to a time unit in which the split operator includes one flow field tuple designated by the split operator. It can be an operation that separates into tuples. In other words, one floating field tuple may be separated by the split operation into a plurality of floating field tuples having the same identifier according to the time unit.

Finally, the selection and project operator may correspond to a selection and project operation for extracting the value of the flow field relationship data, wherein the selection and project operation is based on a condition included in the selection and project operator in the flow field relationship database. It may be an operation of extracting a value in the searched specific pattern by searching for a specific pattern according to the search. Such selection and project operators can use common regular expressions.

Here, the data operator may include a plurality of operators among the join operator, the split operator, and the selection and project operators. That is, it is also possible to extract a value after separating one flow field tuple into a plurality of flow field tuples or to extract a value after combining a plurality of flow field tuples into one flow field tuple.

That is, according to the present invention, the weblog is stored and viewed by configuring a flow field as a set of field name and field value pairs, a flow field tuple as a time series listing of the flow field, and a flow field relation data as a set of the flow field tuples. By doing so, it is possible to easily and simply perform time series analysis of the weblog, and provide a data model for generating, storing, and retrieving the flow field relationship data for all data requiring time series analysis.

Embodiments according to the present invention can be implemented in the form of program instructions that can be executed by various computer means can be recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

In the present invention as described above has been described by the specific embodiments, such as specific components and limited embodiments and drawings, but this is provided to help a more general understanding of the present invention, the present invention is not limited to the above embodiments. For those skilled in the art, various modifications and variations are possible from these descriptions.

Therefore, the spirit of the present invention should not be limited to the described embodiments, and all the things that are equivalent to or equivalent to the claims as well as the following claims will belong to the scope of the present invention. .

According to the present invention, a weblog is a floating field that is a set of field name and field value pairs, a floating field tuple that is a time series listing of the flow field, and a flow field that is a collection of the flow field tuples. By configuring and storing and querying the floating field relation data, time series analysis of the weblog can be easily and conveniently performed.

According to the present invention, it is possible to provide a data model capable of generating, storing, and retrieving the flow field relation data for all data requiring time series analysis as well as the web log.

Claims (22)

In the weblog-based data storage and retrieval method, A preprocessing step of generating and maintaining floating field relation data based on the weblog and the time of occurrence of the weblog; And Processing the floating field relationship data according to a data operator input through a user terminal Data storage and query method comprising a. The method of claim 1, The pretreatment step, Parsing a weblog to extract data from the weblog; Classifying the data according to a user login identifier included in the weblog; And Generating the floating field relationship data by sorting the data in the order of occurrence time for the same user login identifier. Data storage and query method comprising a. The method of claim 1, The flow field relationship data includes at least one floating field tuple, And the floating field tuple is a set of pairs of field names and field values. The method of claim 3, The field name defines an operation or state of a user corresponding to a user login identifier, And the field value includes an actual value corresponding to the operation or the state. The method of claim 1, The data operator includes at least one of a join operator, a split operator, and a select-and-project operator. The step of processing the floating field relationship data according to a data operator input through a user terminal, (1) combine the flow field tuples contained in the flow field relation data according to the join operator, or (2) separate the flow field tuples into a plurality of flow field tuples according to the split operator, or (3) the selection and A data storage and retrieval method for extracting a value from the flow field relation data according to a project operator and providing the value to the user terminal. In the data storage and inquiry method, A preprocessing step of classifying data by identifier and generating floating field relation data by sorting the data in the order of occurrence time of the data for the same identifier. Data storage and query method comprising a. The method of claim 6, The flow field relationship data includes at least one flow field tuple, And the floating field tuple includes a floating field, which is a set of pairs of field names and field values, in the order of occurrence time. The method of claim 7, wherein The field name defines an operation or state of a user corresponding to the identifier, And the field value includes an actual value corresponding to the operation or the state. The method of claim 6, Storing and maintaining the flow field relationship data in a flow field relationship database; And Changing the floating field relation data or extracting the value of the floating field relation data according to a data operator input through a user terminal; Data storage and query method further comprising. The method of claim 9, The data operator includes at least one of a join operator, a split operator, and a selection and project operator. The method of claim 10, The join operator corresponds to a join operation that changes the floating field relationship data, And said join operation is an operation of generating one floating field tuple by combining different floating field tuples of the same identifier designated by the join operator. The method of claim 10, The split operator corresponds to a split operation for changing the floating field relationship data, The split operation is an operation of dividing one floating field tuple designated by the split operator into a plurality of floating field tuples according to a time unit included in the split operator. The method of claim 10, The selection and project operators correspond to selection and project operations for extracting values of the flow field relationship data, The selection and project operation is an operation of extracting a value in the searched specific pattern by searching for a specific pattern according to the conditions included in the selection and project operator in the flow field relation database. The method of claim 6, The data is generated by parsing a weblog, And the identifier includes a user login identifier included in the weblog. A computer-readable recording medium in which a program for executing the method of any one of claims 1 to 14 is recorded. In the data storage and inquiry system, A flow field relation data generation unit for classifying data by identifier and generating floating field relation data by sorting the data according to an occurrence time of the data with respect to the same identifier; A flow field relationship database for storing and maintaining the flow field relationship data; And A data operator processor for changing the floating field relation data or extracting the value of the floating field relation data according to a data operator input through a user terminal. Data storage and inquiry system comprising a. The method of claim 16, The flow field relationship data includes at least one flow field tuple, And the floating field tuple includes a floating field, which is a set of pairs of field names and field values, arranged in chronological order. The method of claim 17, The field name defines an operation or state of a user corresponding to the identifier, And the field value includes an actual value corresponding to the operation or the state. The method of claim 16, The data operator includes at least one of a join operator, a split operator, and a selection and project operator. The method of claim 19, The join operator corresponds to a join operation that changes the floating field relationship data, And said join operation is an operation of combining different floating field tuples of the same identifier designated by the join operator to generate one floating field tuple. The method of claim 19, The split operator corresponds to a split operation for changing the floating field relationship data, And the split operation is an operation of separating one floating field tuple designated by a split operator into a plurality of floating field tuples according to a time unit included in the split operator. The method of claim 19, The selection and project operators correspond to selection and project operations for extracting values of the flow field relationship data, And the selection and project operation retrieves a specific pattern according to a condition included in the selection and project operator from the flow field relation database and extracts a value within the searched specific pattern.

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