WO2008133396A1

WO2008133396A1 - Data storage and inquiry method for time series analysis of weblog and system for executing the method

Info

Publication number: WO2008133396A1
Application number: PCT/KR2008/000610
Authority: WO
Inventors: Dongwook Kim; Handol Park; Joo-Won Jung; Youn Sik Lee
Original assignee: Nhn Corporation
Priority date: 2007-04-26
Filing date: 2008-02-01
Publication date: 2008-11-06
Also published as: JP2010525477A; JP5535062B2; KR20080096004A; KR100898465B1

Abstract

A method and system for storing and reading data base on a weblog is provided. The method includes: generating and maintaining floating field relational data based on a weblog and time of occurrence of the weblog; and processing the floating field relational data according to a data operator being inputted via a user terminal.

Description

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

Technical Field The present invention relates to a method for storing and reading data for a time series analysis of a weblog and a system for implementing the method, and more particularly, with regard to data, especially with regard to a weblog, to a method for storing and reading data suitable for a time series analysis of a weblog and a system for implementing the method.

Background Art

Currently, a relational data model is adopted in a large number of database products. Although there have been a hierarchical data model and a network data model used for an existing data model, the relational data model is embodied in a larger number of database systems than the other two models since the relational data model can flexibly reflect an actual life with realism. For this reason, a relational database management system (RDBMS) supporting the relational data model becomes dominant in a database market.

The relational data model basically consists of necessary three components as followings, and the following three ideas may include all working systems in the real world:

1. Entity: cases and objects to systemize

2. Relationship: relationship between objects and attributes of the objects

3. Attribute: unparsable unit of information representing features of the objects and relationships

However, in a relational database developed based on the relational data model, a number of data fields is fixed in advance, therefore additional information to display cannot be effectively displayed in the relational database when additional information to display occurs depending on situations. In order to solve the above problem, a spare field is prepared in advance and is temporarily used in the relational database when the additional information occurs.

The relational database may not display that a plurality of values are repeated in a single field. Specifically, it is general to separate a product list of purchases by a single customer to store the separated product list in a separate table, and the product list is accessed later and information in the product list is used via a join operation since the product list purchased by the single customer cannot be directly displayed in the relational data model. This is a recommended method in the relational data model.

FIG. 1 is a diagram illustrating a problem of a relational database based on a relational data model.

As illustrated in a reference numeral 110 of FIG. 1, the relational data model is only dependent on bounded values for each related record, that is sequences between arranged records do not have any meaning in the relational data model. Referring to FIG. 1, it is impossible to perform a time series analysis with respect to values b 112 and c 113 corresponding to an identical person Park 111. Since sequences of attributes which are defined and fixed in advance are used, an indexing mechanism effectively expressing relations between attributes does not exist, therefore an operation for reconfiguring all records is requested as shown in a reference numeral 120 in FIG. 1.

As described above, the method using the relational data model may cause a problem in that, an analysis is difficult when performing a time series analysis of a weblog. When recording a single person's behavior pattern, the single person's behavior pattern should be separately recorded in different tables and in different records due to the above described limit of the relational data model.

Therefore, when detecting relations between behavior patterns being separately recorded, an operation such as a join operation having high operation cost should be used, and it is difficult to describe with structure query language (SQL). Further to this, even when it is described in SQL, the described SQL has a complex configuration to process.

Disclosure of Invention Technical Goals

In order to solve the above described problems of conventional arts, the present invention provides a method for storing and reading data for a time series analysis of a weblog and a system for implementing the method.

The present invention is intended to easily perform a time series analysis of a weblog by configuring the weblog to include a floating field which is a set of a field name and a field value, a floating field tuple which is a time series array of the floating field, and a floating field relation which is a set of the floating field tuples, and storing and reading data. The present invention is intended to provide a data model that can store and read data by generating floating field relational data with respect to all data requiring a time series analysis, as well as with respect to the weblog.

Technical solutions In order to achieve goals of the present invention and to solve the above described problems of conventional arts, according to an embodiment of the present invention, there is provided a method for storing and reading data based on a weblog including: generating and maintaining floating field relational data based on a weblog and time of occurrence of the weblog; and processing the floating field relational data according to a data operator being inputted via a user terminal.

In an aspect of the present invention, the generating of the floating field relational data based on the weblog and maintaining the generated floating field relational data including: extracting data from the weblog by parsing the weblog; classifying the data according to a user login identifier included in the weblog; and generating the floating field relational data by arranging the data chronologically by occurrence with respect to an identical user login identifier.

In another aspect of the present invention, the floating field relational data may include at least one floating field tuple, and, in the floating field tuple, a floating field which is a set of a field name and field value, is arranged chronologically by occurrence. In still another aspect of the present invention, the data operator may include at least one operator of join, split, and select-and-project operators, and the processing of the floating field relational data may include any one of the following (a), (b), and (c): (a) joining floating field tuples included in the floating field relational data according to the join operator; (b) splitting a single floating field tuple into a plurality of floating field tuples according to the split operator; and (c) extracting a value from the floating field relational data according to the select-and-project operator and providing the user terminal with the extracted value. According to another embodiment of the present invention, there is provided a method for storing and reading data including: classifying data according to each identifier, and generating floating field relational data by arranging the data chronologically by occurrence with respect to an identical identifier.

Brief Description of Drawings

FIG. 1 is a diagram illustrating a problem of a relational database based on a relational data model;

FIG. 2 is a schematic diagram illustrating a system for storing and reading data according to a first embodiment of the present invention;

FIG. 3 is a flowchart illustrating a method for storing and reading data based on a weblog according to a first embodiment of the present invention;

FIG. 4 is a diagram illustrating a join operation according to the present invention; FIG. 5 is a diagram illustrating a split operation according to the present invention;

FIG. 6 is a flowchart illustrating a method for storing and reading data according to a second embodiment of the present invention; and

FIG. 7 is a block diagram illustrating an inner configuration of a system for storing and reading data according to a third embodiment of the present invention.

Best Mode for Carrying Out the Invention

Hereinafter, various embodiments according to the present invention will be described in detail by referring to accompanied drawings. FIG. 2 is a schematic diagram illustrating a system for storing and reading data according to a first embodiment of the present invention.

A log collection unit 201 receives weblogs transmitted from each web server, a pre-processing unit 202 integrates the weblogs to extract data from integrated weblogs. In this instance, the pre-processing unit 202 may generate floating field relational data which is a set of at least one floating field tuple via the extracted data.

In FIG. 2, it is illustrated that the floating field relational data is stored in a distributed system 203. That is, the floating field relational data may be stored in a floating field relational database of the distributed system 203.

As described above, the stored floating field relational data may be read via a data operator 204 such as join, split, and select-and-project operators being inputted from a user terminal, and a result of the reading may be processed and displayed to be provided to the user terminal in 205.

FIG. 3 is a flowchart illustrating a method for storing and reading data based on a weblog according to a first embodiment of the present invention.

In operation S310, a system for storing and reading data based on the weblog generates and maintains floating field relational data based on the weblog and time of occurrence of the weblog. Specifically, the floating field relational data may include at least one floating field tuple, a floating field, which is a set of a field name and a field value may be arranged chronologically by occurrence in the floating field tuple. Also, the field name may define a user's operation or state corresponding to a user login identifier, and the field value may include an actual value corresponding to the user's operation or state.

As an example, when 'id' is used for a field name and 'Kim' is used for a field value corresponding to the field name, the system for storing and reading data may understand a user login identifier of a predetermined user is 'Kim'. A set of the field name and the field value may be represented by <id = Kim> for the floating field. As another example of the floating field, another floating field is added in addition to <id = Kim>, that is, when 'node' indicating a web page visited by the user is used for a field name and when 'home' indicating an actual value of the web page is used for a field value, the system for storing and reading data may understand that the user login identifier 'Kim' has visited 'home'. As described above, floating fields may be arranged chronologically by occurrence with regard to an identical user login identifier, and sequences of the arranged floating fields may be defined as the floating field tuple. Namely, the floating field tuple may chronologically include data regarding user's state and operation having an identical user login identifier according to time of occurrence of the user's state and operation.

In other words, via the floating field tuple, the floating field relational data may indicate data regarding states and operations according to time sequences of each of all users included in the weblog, through this, a time series analysis of the weblog is possible.

As an example, when floating field tuples such as <id = Kim> <node = mail>

<node = main home> <node = game A> are used, the system for storing and reading data may understand that the user using the user login identifier 'Kim' accesses to a web page of 'game A' via 'main home'. That is, it is possible to read a user who has accessed 'game A' via 'main home' after 'mail' is read via 'main home'.

In order to generate and maintain the floating field relational data, the system for storing and reading data may perform operation S310 by including operations S311 through S313 as illustrated in FIG. 3.

In operation S311, the system for storing and reading data extracts data from the weblog by parsing the weblog. Specifically, the system for storing and reading data extracts the data which is standardized information to generate the floating field relational data. In operation S312, the system for storing and reading data classifies the data according to a user login identifier included in the weblog. Generally, visit-logs by a plurality of people are sequentially stored in the weblog. Also, a record for an identical person may be stored in different web servers since the visit-logs are generally stored via a plurality of web servers. Therefore, the classification of the data is required to be performed by collecting the data corresponding to each user after collecting all weblogs which are separately stored in all web servers.

In operation S313, the system for storing and reading data generates the floating field relational data by arranging the data chronologically by occurrence with respect to an identical user login identifier. Specifically, the system for storing and reading data may generate the floating field relational data by arranging the data being classified chronologically according to time of occurrence of the user login identifier. In this instance, each of the data may correspond to each of the above described floating fields.

In operation S320, the system for storing and reading data processes the floating field relational data according to a data operator being inputted via the user terminal. The data operator may include at least one operator of join, split, and select- and-project operators.

In this instance, in order to process the floating field relational data according to the data operator, the system for storing and reading data may include any one of the following (a), (b), and (c): (a) joining floating field tuples included in the floating field relational data according to the join operator; (b) splitting a single floating field tuple into a plurality of floating field tuples according to the split operator; and (c) extracting a value from the floating field relational data according to the select-and-project operator and providing the user terminal with the extracted value. The value may indicate a set of actual values included by the plurality of floating fields.

Also, the data operator may include a plurality of operators from among the join, split, and select-and-project operators. Namely, both ways of a single floating field tuple is split into a plurality of floating field tuples and a value is extracted, and a plurality of floating field tuples are joined to a single floating field tuple and a valued is extracted, are possible.

FIG. 4 is a diagram illustrating a join operation according to the present invention. As described above, a floating field tuple is an array of floating fields, that is consecutive information about a single subject of a single user. When the floating field tuple is needed to be analyzed, a single user's behavior pattern for a week or for a month is analyzed. In this instance, floating fields of the user needs to collect for over a month to generate a floating field tuple. For some technical or storage problems, it is more convenient to dynamically generate the floating field tuple for a predetermined time period than to generate all floating fields as the single floating field tuple.

Namely, a plurality of floating field tuples of a single user are generated with respect to a short time period, and the plurality of floating field tuples of the single user are joined using the join operator when needed, thereby obtaining a floating field tuple of the user's behavior pattern. As an example, a floating field tuple is generated by a day unit, for example, a floating field tuple corresponding to January 2 and a floating field tuple corresponding to January 3 are joined via the join operator, thereby obtaining a floating field tuple corresponding to from January 2 to January 3.

The system for storing and reading data may perform join operation according to a condition included in a join operator when the join operator is inputted via the user terminal. Specifically, the join operation may include an operation that generates a single floating field tuple by joining a plurality of floating field tuples corresponding to the condition.

As illustrated in FIG. 4, the system for storing and reading data may generate a third floating field tuple 404 by performing join operation 403 with respect to a first floating field tuple 401 and a second floating field tuple 402. In this instance, floating fields may be arranged chronologically by occurrence of the floating fields as shown in the third floating field tuple 404.

FIG. 5 is a diagram illustrating a split operation according to the present invention.

A system for storing and reading data may perform split operation according to a split operator being inputted via a user terminal and a condition included in the split operator. The split operation may be the opposite operation of the join operation, and split a selected floating field tuple into a floating field tuples with a meaningful unit.

Generally, when a weblog is analyzed, a meaningful user's visit is recognized each thirty minute. Namely, it is generally determined that a predetermined user's behavior is completed once, when the predetermined user's behavior does not occur for thirty minutes. Therefore, although a floating field tuple is configured by a day-unit, the floating field tuple may need to be split into thirty minute-units, and for this, the system for storing and reading data needs to perform the split operation.

The system for storing and reading data may perform split operation 502 with respect to a first floating field tuple 501 chronologically by occurrence of floating fields as illustrated in FIG. 5. Referring to FIG. 5, the first floating field is split into a thirty minute-units, and the first floating field tuple 501 is split into a plurality of floating field tuples 503. The time units may be included in the condition of the split operator. Also, the condition may include contents selecting the first floating field tuple 501. Finally, the select-to-project operator is to search the floating field relational data for a specific pattern and extract a value within the specific pattern, and a regular expression may be used for syntax of the select-to-project operator.

As an example, the system for storing and reading data may search a select-to- project operator <id = kim> (<node = (\w*)>)* being inputted via the user terminal for all nodes visited by a user using a user's login identifier 'Kim', extract an actual value corresponding to the all nodes, and provide the user terminal with the extracted value.

As another example, when a select-to-project operator <id = Lee> <node = home> (<node = (\w*)>)* is inputted, the system for storing and reading data may search the select-to-project operator <id = Lee> <node = home> (<node = (\w*)>)* for all nodes visited by a user using a user's login identifier 'Lee' after visiting 'home', extract an actual value corresponding to the all nodes, and provide the user terminal with the extracted value. In this instance, the all nodes may indicate web pages.

As described above, according to the present invention, a weblog is configured with a floating field of a set of a field name and a field value, a floating field tuple of a sequential array of the floating field, and floating field relational data of a set of the floating field tuple, and the data is stored and read, thereby easily performing a time series analysis of the weblog.

FIG. 6 is a flowchart illustrating a method for storing and reading data according to a second embodiment of the present invention.

In operation S601, a system for storing and reading data classifies data according to each identifier, and generates floating field relational data by arranging the data chronologically by occurrence with respect to an identical identifier. In this instance, the floating field relational data may include at least one floating field tuple, and, in the floating field tuple, a floating field which is a set of a field name and field value, is arranged chronologically by occurrence. The field name may define a user's operation or state corresponding to a user login identifier, and the field value may include an actual value corresponding to the user's operation or state.

The data may include all data requiring a time series analysis. That is, an identical identifier is allocated to data requiring a time series analysis, data having an identical identifier may generate a floating field relational data by arranging the data chronologically by occurrence. Specifically, the data may be generated by parsing a weblog, and the identifier may include a user login identifier included in the weblog.

In this instance, single datum is a floating field and has a field name and a field value, and the data having the identical identifier may be configured with a floating field tuple.

In operation S602, the system for storing and reading data stores and maintains the floating field relational data in the floating field relational database. The floating field relational data may be changed according to a data operator which will be described later, stored in a floating field relational database, or may be used for searching for and extracting an actual value. In operation S603, the system for storing and reading data changes the floating field relational data according to a data operator being inputted via a user terminal or extracts a value from the floating field relational data. The value may indicate a set of actual values included in each of the plurality of floating fields, and the data operator may include at least one operator of join, split, and select-to-project operators.

The join operator may correspond to join operation that changes the floating field relational data, and the join operation may be an operation that generates a single floating field tuple by joining different floating field tuples of an identical identifier designated by the join operator. Namely, the floating field tuples including the identical identifier may be j oined via the j oin operation.

The split operator may correspond to split operation that changes the floating field relational data, and the split operation may be an operation that splits a single 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. Namely, the single floating field tuple may be split into a plurality of floating field tuples having an identical identifier according to a time unit via the split operation.

Finally, the select-to-project operator may correspond to a select-to-project operation that extracts a value from the floating field relational data, and the select-to- project operation may be an operation that searches the floating field relational database for a specific pattern according to a condition included in the select-and-project operator to extract a value within the retrieved specific pattern. The select-to-project operator may use a regular expression.

The data operator may include a plurality of operators from among the join, split, and select-and-project operators. Namely, both ways of a single floating field tuple is split into a plurality of floating field tuples and a value is extracted, and a plurality of floating field tuples are joined to a single floating field tuple and a valued is extracted, are possible.

According to the present invention, a weblog is configured with a floating field of a set of a field name and a field value, a floating field tuple of a sequential array of the floating field, and floating field relational data of a set of the floating field tuples, and the data is stored and read, thereby easily performing a time series analysis of the weblog, and providing a data model that can generate, store, and maintain the floating field relational data with respect to all data requiring the time analysis.

FIG. 7 is a block diagram illustrating an inner configuration of a system 700 for storing and reading data according to a third embodiment of the present invention. As illustrated in FIG. 7, the system 700 for storing and reading data includes a floating field relational data generation unit 701, a floating field relational database 702, and a data operator processing unit 703.

The floating field relational data generation unit 701 generates floating field relational data by arranging the data chronologically by occurrence with respect to an identical identifier. In this instance, the floating field relational data may include at least one floating field tuple, and the floating field tuple may include a floating field, which is a set of a field name and a field value, is arranged chronologically by occurrence. Also, the field name may define a user's operation or state corresponding to a user login identifier, and the field value may include an actual value corresponding to the user's operation or state. The floating field relational database 702 stores and maintains the floating field relational data.

The data operator processing unit 703 changes the floating field relational data according to the data operator being inputted via a user terminal or extracts a value from the floating field relational data. In this instance, the value may indicate a set of actual values included in each of the plurality of floating field fields, and the data operator may include at least one operator of join, split, and select-to-project operators.

The join operator may correspond to join operation that changes the floating field relational data, and the join operation may be an operation that generates a single floating field tuple by joining different floating field tuples of an identical identifier designated by the join operator. Namely, the floating field tuples including the identical identifier may be joined via the join operation.

The split operator may correspond to split operation that changes the floating field relational data, and the split operation may be an operation that splits a single 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. Namely, the single floating field tuple may be split into a plurality of floating field tuples having an identical identifier according to a time unit via the split operation. Finally, the select-to-project operator may correspond to select-to-project operation that extracts a value from the floating field relational data, and the select-to- project operation may be an operation that searches the floating field relational database for a specific pattern according to a condition included in the select-and-project operator to extract a value within the retrieved specific pattern. The select-to-project operator may use a regular expression.

The data operator may include a plurality of operators from among the join, split, and select-and-project operators. Namely, both ways of a single floating field tuple is split into a plurality of floating field tuples and a value is extracted, or a plurality of floating field tuples are joined to a single floating field tuple and a valued is extracted are possible.

According to the present invention, a weblog is configured with a floating field of a set of a field name and a field value, a floating field tuple of a sequential array of the floating field, and floating field relational data of a set of the floating field tuple, and the data is stored and read, thereby easily performing a time series analysis of the weblog, and providing a data model that can generate, store, and maintain the floating field relational data with respect to all data requiring the time analysis.

The method for storing and reading data based on a weblog according to the above-described exemplary embodiments of the present invention may be recorded in computer-readable media including program instructions to implement various operations embodied by a computer. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. Examples of computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks and DVD; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, and the like Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described exemplary embodiments of the present invention. According to the present invention, it is possible to easily perform a time series analysis of a weblog since the weblog is configured with a floating field of a set of a field name and a field value, a floating field tuple of a sequential array of the floating field, and floating field relational data of a set of the floating field tuple, and the data is stored and read.

According to the present invention, it is possible to provide a data model that can store and maintain data by generating floating field relational data with respect to all data requiring a time analysis.

The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. Therefore, it is intended that the scope of the invention be defined by the claims appended thereto and their equivalents. Although a few embodiments of the present invention have been shown and described, the present invention is not limited to the described embodiments. Instead, it would be appreciated by those skilled in the art that changes may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for storing and reading data based on a weblog, the method comprising: generating and maintaining floating field relational data based on a weblog and time of occurrence of the weblog; and processing the floating field relational data according to a data operator being inputted via a user terminal.

2. The method of claim 1, wherein the generating of the floating field relational data based on the weblog and maintaining the generated floating field relational data comprises: extracting data from the weblog by parsing the weblog; classifying the data according to a user login identifier included in the weblog; and generating the floating field relational data by arranging the data chronologically by occurrence with respect to an identical user login identifier.

3. The method of claim 1, wherein the floating field relational data comprises at least one floating field tuple, and, in the floating field tuple, a floating field, which is a set of a field name and field value, is arranged chronologically by occurrence.

4. The method of claim 3, wherein the field name defines a user's operation or state corresponding to the user login identifier, and the field value comprises an actual value corresponding to the user's operation or state.

5. The method of claim 1, wherein the data operator comprises at least one operator of join, split, and select-and-project operators, and the processing of the floating field relational data comprises any one of the following (a), (b) and (c):

(a) joining floating field tuples included in the floating field relational data according to the join operator;

(b) splitting a single floating field tuple into a plurality of floating field tuples according to the split operator; and (c) extracting a value from the floating field relational data according to the select-and-project operator and providing the user terminal with the extracted value.

6. A method for storing and reading data, the method comprising: classifying data according to each identifier, and generating floating field relational data by arranging the data chronologically by occurrence with respect to an identical identifier.

7. The method of claim 6, wherein the floating field relational data comprises at least one floating field tuple, and, in the floating field tuple, a floating field that is a set of a field name and field value, is arranged chronologically by occurrence.

8. The method of claim 7, wherein the field name defines a user's operation or state corresponding to the user login identifier, and the field value comprises an actual value corresponding to the user's operation or state.

9. The method of claim 6, further comprising: storing and maintaining the floating field relational data in a floating field relational database; and changing the floating field relational data according to a data operator being inputted via a user terminal or extracting a value from the floating field relational data.

10. The method of claim 9, wherein the data operator comprises at least one operator of join, split, and select-and-project operators.

11. The method of claim 10, wherein the join operator corresponds to a join operation that changes the floating field relational data, and the join operation generates a single floating field tuple by joining different floating field tuples of an identical identifier designated by the join operator.

12. The method of claim 10, wherein the split operator corresponds to a split operation that changes the floating field relational data, and the split operation splits a single floating field tuple designated by the split operator into a plurality of floating field tuples according to a time unit of the split operator.

13. The method of claim 10, wherein the select-and-project operator corresponds to a select-and-project operation that extracts a value of the floating field relational data, and the select-and-project operation searches the floating field relational database for a specific pattern according to a condition included in the select-and-project operator to extract a value within the retrieved specific pattern.

14. The method of claim 6, wherein the data is generated by parsing a weblog, and the identifier comprises a user login identifier included in the weblog.

15. A computer-readable storage medium storing instructions for implementing the method of any one of claims 1 through 14.

16. A system for storing and reading data, the system comprising: a floating field relational data generation unit classifying data according to each identifier and generating floating field relational data by arranging the data chronologically by occurrence; a floating field relational database storing and maintaining the floating field relational data; and a data operator processing unit changing the floating field relational data according to a data operator being inputted via a user terminal or extracting a value of the floating field relational data.

17. The system of claim 16, wherein the floating field relational data comprises at least one floating field tuple, and, in the floating field tuple, a floating field that is a set of a field name and a field value, is arranged chronologically by occurrence.

18. The system of claim 17, wherein the field name defines a user's operation or state corresponding to the identifier, and the field value comprises an actual value corresponding to the user's operation or state.

19. The system of claim 16, wherein the data operator comprises at least one operator of join, split, and select-and-project operators.

20. The system of claim 19, wherein the join operator corresponds to a join operation that changes the floating field relational data, and the join operation generates a single floating field tuple by joining different floating field tuples of an identical identifier designated by the join operator.

21. The system of claim 19, wherein the split operator corresponds to a split operation that changes the floating field relational data, and the split operation splits a single 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.

22. The system of claim 19, wherein the select-and-project operator corresponds to a select-and project operation that extracts a value of the floating field relational data, and the select-and-project operation searches the floating field relational database for a specific pattern based on a condition included in the select-and-project operator to extract a value within the retrieved specific pattern.