JP2007188343A - Schema integration support device, schema integration support method, and schema integration support program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To prepare mapping information of a table without using "domain information" and an "attribute name", in integration processing of two databases. <P>SOLUTION: A schema information extracting part 103 acquires schema information indicating data structure of the table, from the first database 100 and the second database 101 to be integrated, to be stored in a schema information storage device 104. A feature information generating part 105 calculates the number of respective data items constituting the each table, based on the scheme information, in every kind of defined attributes, to be stored in a feature information storage device 107 as feature information, in the each table. A similarity evaluating part 108 compares the feature information in the table of the first database 100 with the feature information in the table of the second database 101, and calculate similarity, to be stored in a mapping model storage device 110 as the mapping information, while correlating the compared tables with the similarity thereof. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a schema integration support apparatus, a schema integration support method, and a schema integration support program that support schema integration work between different databases.

Traditional support for mapping (mapping) schemas (database structures) between different databases used when integrating multiple different databases where data representing the same concept is defined in different representation formats There were the following methods.
One of the conventional methods is to use the domain information (actual data value set) of attributes used for mapping in the data, determine the degree of matching by an evaluation function, and derive mapping candidates (for example, Patent Document 1).
In another method, the name of the attribute used for mapping is standardized, and the mapping candidate is presented by calculating the similarity from the attribute name (for example, Patent Document 2).
Japanese Patent Laid-Open No. 2004-86782 JP-A-8-249338

Among the conventional schema integration support methods, the method of using “domain information” for determining mapping candidates (Patent Document 1) has a problem that not only schema information but also actual data values are required in the integration process. there were. Furthermore, since the data value is used for the determination of the degree of coincidence, there is a problem that it is necessary to standardize (unify) the data value between the databases to be integrated.
In addition, in the method of using “attribute name” for determining a mapping candidate (Patent Document 2), since the attribute name is information that is arbitrarily set in a free format, the similarity is calculated in each database to be integrated. There is a problem that a mapping candidate cannot be presented unless an appropriate attribute name is set in.

  An object of the present invention is to make it possible to create a mapping candidate without using “domain information” or “attribute name”.

  The schema integration support apparatus of the present invention outputs schema mapping information about tables mapped between the first database and the second database as information supporting the integration of the first database and the second database. A schema information storage device that stores the data structure information of the table A of the first database and the data structure information of the table B of the second database, and the first information stored in the schema information storage device. The data structure information of the table A included in the first database and the data structure information of the table B included in the second database are compared using the central processing unit, and the table A included in the first database and the table included in the second database Calculate the similarity with B and use the calculated similarity as mapping information It characterized by comprising a similarity evaluation unit for outputting to an output device.

  According to the schema integration support device of the present invention, for example, similarity can be calculated by paying attention to the data structure of a table such as constraint conditions and dependency relationships between data. Can be created.

Embodiment 1 FIG.
In the following, “relational database” is used as an example of a database system to be integrated (hereinafter referred to as database), and “table data structure information (schema information)” is used as information for supporting database integration. Schema integration support apparatus, schema integration support method, and schema integration support program for generating “similarity (mapping information)” will be described.

FIG. 1 is a diagram illustrating an appearance of the schema integration support apparatus 102 according to the first embodiment.
In FIG. 1, the schema integration support apparatus 102 includes a system unit 910, a display apparatus 901, a keyboard (K / B) 902, a mouse 903, a compact disk apparatus (CDD) 905, a printer apparatus 906, and a scanner apparatus 907. Connected with a cable.
Further, the schema integration support apparatus 102 is connected to a FAX machine 932 and a telephone 931 via a cable, and is connected to the Internet 940 via a local area network (LAN) 942 and a web server 941.

FIG. 2 is a hardware configuration diagram of the schema integration support apparatus 102 according to the first embodiment.
In FIG. 2, the schema integration support apparatus 102 according to the first embodiment includes a CPU (Central Processing Unit) 911 that executes a program. The CPU 911 includes a ROM 913, a RAM 914, a communication board 915, a display device 901, a K / B 902, a mouse 903, an FDD (Flexible Disk Drive) 904, a magnetic disk device 920, a CDD 905, a printer device 906, a scanner device 907, via a bus 912. It is connected to the optical disk device 908.
The display device 901 includes a liquid crystal display, a CRT (Cathode Ray Tube) display, and the like.
The RAM 914 is an example of a volatile memory. The ROM 913, the FDD 904, the CDD 905, the magnetic disk device 920, and the optical disk device 908 are examples of nonvolatile memories. These are examples of storage devices, storage devices, or storage units. “Schema information storage device 104”, “data type dictionary storage device 106”, “feature information storage device 107”, “weighting variable storage device” described below 109 ”and“ mapping model storage device 110 ”.
The communication board 915 is connected to a FAX machine 932, a telephone 931, a LAN 942, and the like.
For example, the communication board 915, the K / B 902, the scanner device 907, the FDD 904, and the like are examples of an input device, an input device, or an input unit.
Further, for example, the communication board 915, the display device 901, and the like are examples of an output device, an output device, or an output unit.

Here, the communication board 915 is not limited to the LAN 942 but may be directly connected to the Internet 940 or a WAN (Wide Area Network) such as ISDN. When directly connected to a WAN such as the Internet 940 or ISDN, the schema integration support apparatus 102 is connected to a WAN such as the Internet 940 or ISDN, and the web server 941 is unnecessary. The communication board 915 included in the schema integration support apparatus 102 transmits / receives data such as schema information to / from an integration target database.
The magnetic disk device 920 stores an operating system (OS) 921, a window system 922, a program group 923, and a file group 924. The program group 923 is executed by the CPU 911, the OS 921, and the window system 922.

The program group 923 stores a program for executing a function described as “˜unit” in the description of the embodiment. The program is read and executed by the CPU 911.
In the file group 924, in the description of the embodiment, data described as “˜information”, data indicating a determination result or a calculation result when a function described as “˜part” is executed, and described as “˜part”. Data to be passed between programs that execute the function to be performed is stored as “˜file”. For example, “schema information”, “data type dictionary”, “feature information”, “weighting variable”, and “mapping information” described below are stored as “˜file”.
Also, in the description of the embodiments, the arrows in the flowcharts and configuration diagrams mainly indicate input / output of data, and the data for the input / output of the data includes a magnetic disk device 920, an FD (Flexible Disk cartridge), and an optical disk. , CD (compact disc), MD (mini disc), DVD (Digital Versatile Disk), and other storage media. Alternatively, it is transmitted through a signal line or other transmission medium.

  Also, what is described as “˜unit” in the description of the embodiment may be realized by firmware stored in the ROM 913. Alternatively, it may be implemented by software alone, hardware alone, a combination of software and hardware, or a combination of firmware.

  The program for implementing the embodiment may be stored using a storage device such as a magnetic disk device 920, FD, optical disk, CD, MD, DVD, or other storage medium.

FIG. 3 is a functional configuration diagram showing functional elements of the schema integration support apparatus 102 according to the first embodiment.
In FIG. 3, the first database 100 and the second database 101 are schema integration target databases.
In FIG. 3, the schema integration support apparatus 102 has the following functions.
A schema information extraction unit 103 that acquires “schema information” used in the schema integration support process from a database to be integrated.
A schema information storage device 104 that stores “schema information” acquired by the schema information extraction unit 103.
A feature information generation unit 105 that generates “feature information” of a table based on “schema information”.
A data type dictionary storage device 106 that stores a “data type dictionary” for standardizing data type attributes used by each database to be integrated when generating “feature information”.
A feature information storage device 107 that stores the “feature information” generated by the feature information generation unit 105.
A similarity evaluation unit 108 that evaluates the similarity of tables based on “feature information”.
A weighting variable storage device 109 that stores definition values (hereinafter referred to as “weighting variables”) used when the similarity evaluation unit 108 evaluates the degree of similarity of a table.
The similarity evaluation unit 108 includes a mapping model storage device 110 that stores “mapping information” of tables between databases to be integrated derived based on the similarity.

  The first database 100 and the second database 101 are managed by a general database management system, and are connected to the schema integration support apparatus 102 via a network such as a LAN. Alternatively, both or one of the first database 100 and the second database 101 may exist on the same computer system as the schema integration support apparatus 102.

  In addition, the schema information storage device 104, the data type dictionary storage device 106, the feature information storage device 107, the weighting variable storage device 109, and the mapping model storage device 110 may be implemented together in one storage device, You may mount with a memory | storage device.

  Next, “schema information” stored in the schema information storage device 104, “data type dictionary” stored in the data type dictionary storage device 106, “feature information” stored in the feature information storage device 107, and weighting variable storage device 109 The “weighting variable” stored and the “mapping information” stored in the mapping model storage device 110 will be described.

The “schema information” acquired by the schema information extraction unit 103 from the integration target database and stored in the schema information storage device 104 is data representing the structure of the database, and includes “table data structure information” stored and managed in the database. It is.
The “table data structure information” includes a “data attribute” defined for each data item (indicating “column” or “data field”) constituting the table.
“Data attribute” is an attribute that uniquely identifies another data item in the same table, an attribute that indicates an association with another table, and other information related to the dependency (attribute indicating the dependency), data type / data Indicates “constraint condition information (attributes indicating constraint conditions)” of data set in data items such as size.
“Information about dependency (attribute indicating dependency)” includes “primary key attribute (attribute for uniquely identifying data of dependent attribute item)”, “subordinate attribute (related to data of primary key attribute item of own table) Attribute) ”,“ foreign key attribute (attribute to be associated with data of primary key attribute item of other table) ”and the like.

FIG. 4 is a data structure diagram showing an example of “schema information” in the first exemplary embodiment.
In FIG. 4, table information 140 indicates “table data structure information” for one table, and “schema information” has table information 140 corresponding to the number of tables. Each table information 140 has an ID, a table name, primary key column information 141, primary key reference destination information 142, dependent column information 143, and foreign key information 144.
The ID and table name are information for uniquely identifying the table.
The primary key column information 141 is information indicating “primary key attribute”, and has “column name” and “data type” for all data item columns constituting the primary key.
The dependent column information 143 is information indicating “dependent attribute”, and has “column name” and “data type” for all data item columns other than the primary key.
The foreign key information 144 is information indicating “foreign key attribute”, and has “foreign key ID” and “foreign key column information 145” for all data items that are foreign keys. The “foreign key ID” is information for uniquely identifying “foreign key column information 145”, and the “foreign key column information 145” indicates “column name” and “data type” for all data item columns constituting the foreign key. Have.
The primary key reference destination information 142 has “reference destination ID (ID of other table)” for all other tables having foreign keys associated with the primary key of the own table.
The ID and the foreign key ID in the table information 140 shown in FIG. 4 are information added when the schema information extraction unit 103 stores the schema information acquired from the database system to be integrated in the schema information storage device 104. .

FIG. 5 is a diagram illustrating an example of a “data type dictionary” in the first embodiment.
The data type dictionary storage device 106 includes a data type used by the schema integration support device 102 for schema integration support processing (hereinafter referred to as a standard data type) and a data type used by the database to be integrated (hereinafter referred to as unique data). The data is registered as a “data type dictionary” as shown in FIG. That is, the schema integration support apparatus 102 preliminarily stores the correspondence relationship between the standard data types and the specific data types of the first database 100 and the second database 101 as a preprocess of the schema integration support process. 106.
The “data type dictionary” shown in FIG. 5 associates specific data types of the first database 100 “DBMS1” and the second database 101 “DBMS2”, which are databases to be integrated, with standard data types. . The “data type dictionary” shown in FIG. 5 includes, for example, “char” type and “integer” type, which are intrinsic data types of “DBMS1”, and “character type” and “integer type”, which are standard data types, respectively. And “char” type and “nchar” type which are unique data types of “DBMS2” are associated with “character type” which is a standard data type.
When the first database 100 and the second database 101 use the same database management system, that is, when the same data type is used, the “data type dictionary” is one of the unique data types. The data may be associated with a standard data type.
For example, a character type, an exact number type, an approximate number type, a datetime type, and a binary type are defined as standard data types, and a “data type dictionary” is prepared in advance in the data type dictionary storage device 106 of the schema integration support device 102. Alternatively, the system administrator (user) can change the definition of the correspondence between the unique data type and the standard data type at the time of schema integration support processing by enabling the definition at any time using the input device. Also good.

FIG. 6 is a diagram illustrating an example of the item “feature information” in the first exemplary embodiment.
The “feature information” stored in the feature information storage device 107 is a collection of items having quantitative values that the feature information generation unit 105 derives for each table from the “schema information”. The table is a mapping candidate. Each item as shown in FIG. 6 constituting “feature information” is defined in advance in a storage device of the schema integration support device 102 (for example, the feature information storage device 107).
In FIG. 6, “ID”, “number of primary key attributes”, “data type configuration of primary key”, “number of primary key referenced tables”, “number of dependent attributes”, “data type configuration of dependent attributes”, “ “Number of foreign key attributes” and “Data type configuration of foreign key” are defined as items of “feature information”.
“ID” is information for uniquely identifying a table corresponding to the “feature information”.
“Number of primary key attributes” indicates the number of data items for which primary key attributes are defined, “Number of subordinate attributes” indicates the number of data items for which primary key attributes are not defined, and “Number of foreign key attributes” indicates external Indicates the number of data items for which key attributes are defined.
“Primary key data type composition” indicates the number of “primary key attributes” for each standard data type, that is, the number of each data item in which the primary key attribute is defined, classified by the attributes of the standard data type. “Data type configuration” indicates “number of subordinate attributes” for each standard data type, and “Data type configuration of foreign key” indicates “number of foreign key attributes” for each standard data type.
“Number of primary key reference destination tables” indicates the number of other tables having foreign keys associated with the primary key of the table corresponding to the “feature information”.
The items “ID” and “number of primary key reference destination tables” of “feature information” shown in FIG. 6 are information corresponding to the ID of the table information 140 and primary key reference destination information 142 shown in FIG.
6 correspond to the primary key column information 141 of the table information 140 shown in FIG. 4, and the “feature information” items “number of primary key attributes” and “data type configuration of primary key” shown in FIG. The items “number of dependent attributes” and “data type configuration of the dependent attributes” of the “information” correspond to the dependent column information 143 of the table information 140 shown in FIG. 4, and the item “number of foreign key attributes” of the “feature information” shown in FIG. "And" Data type configuration of the foreign key "correspond to the foreign key information 144 of the table information 140 shown in FIG.
Hereinafter, each item of “feature information” is referred to as a feature information item.

Next, the “weighting variable” will be described.
In the weighting variable storage device 109, a parameter (weighting variable) used when the similarity evaluation unit 108 evaluates the similarity is set.
The parameters (weighting variables) include “similar allowable range”, “variation rate”, and “specific gravity”, which are parameters for each “feature information item”, and “similarity determination threshold”, “parameters for“ feature information ”, “Similarity between standard data types”, which is a parameter for “data type”.
The “similar allowable range” is a boundary value for determining whether or not the values of the feature information items are compared between the tables of the integration target database. The similarity evaluation unit 108 determines that the feature information item A of the two tables is “similar” if the difference value of the feature information item A (for example, the number of primary key attributes) between the two tables is within the “similar allowable range”. If it exceeds the “similar allowable range”, the feature information item A is determined to be “not similar (dissimilar)”. The more “feature information items” that are “similar” are, the more similar “feature information” is, and two tables corresponding to similar “feature information” become mapping candidates.
The “variation rate” is the similarity of the feature information item for the feature information item determined by the similarity evaluation unit 108 as “similar” according to the difference value of the feature information item between the two tables. This is a value used to calculate the degree, and indicates the ratio of decreasing the degree of similarity (similarity reduction value per difference value). For example, when the similarity is 0 (completely unmatched) to 1 (completely matched) and the “variation rate” is 0.1, the similarity of the feature information item having a difference value of 2 is 0.8 (= 1−1). 0.1 × 2).
“Specific gravity” indicates a weight for the similarity of each feature information item used when the similarity evaluation unit 108 calculates the similarity of the entire feature information, that is, the similarity of two compared tables. For example, when the similarity weight is equally treated in all feature information items, the “specific gravity” of all feature information items is set to 1. Also, when handling a particular feature information item heavily, if the “specific gravity” of another feature information item is 1, the “specific gravity” of the feature information item to be handled heavily is set to a value larger than 1.
“Similarity between standard data types” indicates the similarity of feature information items corresponding to the data items when data items of different standard data types are associated between two tables. For example, FIG. 7 shows an example of “similarity between standard data types” when the similarity between standard data types is 0 (completely unmatched) to 1 (completely matched). In FIG. 7, the similarity between character-type data items is 1, and the similarity between a character-type data item and an integer data item is 0.4.
The “similarity determination threshold” is a boundary value for determining whether the entire feature information is similar, that is, whether the table is similar. The similarity evaluation unit 108 multiplies the similarity of each feature information item by “specific gravity” to “similar” if the total value is equal to or greater than the “similarity determination threshold”, and “dissimilar” if it is less than the “similarity determination threshold”. Is determined.
The setting value of each parameter is defined in advance by the schema integration support device 102, and can be changed by the system administrator (user) by the input device as necessary. The parameter may be added or deleted according to the content of the feature information item.

FIG. 8 is a diagram illustrating an example of “mapping information” in the first embodiment.
The similarity evaluation unit 108 stores “mapping information” as shown in FIG. 8 in the mapping model storage device 110 for the two tables that are determined to be mapping candidates with similar “feature information”. “Mapping information” indicates, for example, the name of each table determined as a mapping candidate in the first database 100 and the second database 101 and the degree of similarity.
In FIG. 8, the table name is represented by “schema name.table name”, and mapping candidates are shown in each row. In FIG. 8, mapping candidates (tables of the second database) of the second database 101 to the “URIAGE” table (an example of the table A of the first database) of the “HAN” schema of the first database 100. (Example B) includes three tables: a “sales record” table in the “sales” schema, a “sales details” table in the “sales” schema, and a “shipping record” table in the “stock” schema. Also, the respective similarities are “4.23”, “5.65”, and “3.72”, and the user who integrates the database and the schema integration support apparatus 102 refer to the similarity so that the first It can be determined that the first candidate of the table of the second database 101 mapped to the “URIAGE” table of the “HAN” schema of the database 100 is the “sales details” table of the “sales” schema having the highest similarity. .

FIG. 9 is a flowchart showing schema integration support processing (schema integration support method) in the first embodiment.
The schema integration support process (schema integration support method) executed by the schema integration support apparatus 102 will be described below with reference to the flowchart of FIG.
A schema integration support process (schema integration support method) described below can be executed by a computer, and a program that causes a computer to execute the schema integration support process (schema integration support method) is a schema integration support program.

<Step 120: Data structure information extraction process>
First, the schema information extraction unit 103 acquires “schema information” from any of the database systems to be integrated (the first database 100 or the second database 101). Then, the schema information extraction unit 103 generates table information 140 of each table based on the acquired “schema information”, and the table information 140 of all tables is converted into “schema information (schema information used in the schema integration support process thereafter). ”) In the schema information storage device 104.
“Schema information” to be acquired includes “table name list information” of tables existing in the database, “primary key attribute information” of each table, “subordinate attribute information” of each table, “foreign key attribute of each table Information ”.
The schema information extraction unit 103 collects “schema information” by using an access function to “schema information” provided by the database management system that manages the first database 100 and the second database 101. As a method of accessing “schema information”, a method of accessing data stored in “schema information” of a database called a system table or the like in the same way as normal data, or a dedicated method for accessing “schema information” There are methods using an API (application interface), and any method may be used.
The schema information extraction unit 103 collects “schema information” required depending on the “feature information” generated by the feature information generation unit 105, and generates table information 140.
For example, when the table information 140 shown in FIG. 4 is generated depending on the feature information generation unit 105 generating “feature information” having the feature information items shown in FIG. The table information 140 (part of schema information) is generated as follows.
A unique ID is generated and set in “ID” of the table information 140.
Further, the “table name” of the table information 140 is set based on the “table name list information” of the acquired “schema information”.
Further, “primary key column information 141” of the table information 140 is generated based on the “primary key attribute information” of the acquired “schema information”.
Further, “subordinate column information 143” of the table information 140 is generated based on the “subordinate attribute information” of the acquired “schema information”.
Further, “primary key reference destination information 142” and “foreign key information 144” of the table information 140 are generated based on the “foreign key attribute information” of the acquired “schema information”.

<Step 121: Feature information generation process>
Next, the feature information generation unit 105 generates “feature information” of each table based on the “schema information (collection of table information 140)” generated by the schema information extraction unit 103 and stored in the schema information storage device 104. And stored in the feature information storage device 107.
For example, the “feature information” generation process of the feature information generation unit 105 when generating the “feature information” having the feature information items shown in FIG. 6 based on the table information 140 of FIG. 4 is as follows.
The ID of the table information 140 is set in the “ID” of the feature information item.
In the feature information item “number of primary key attributes”, “number of primary key reference destination tables”, “number of dependent attributes”, and “number of foreign key attributes”, the number of primary key column information 141 of the table information 140, the primary key, respectively. The number of reference destination information 142, the number of dependent column information 143, and the number of foreign key information 144 are set.
Further, the feature information generation unit 105 converts the data types of the primary key column information 141, the dependent column information 143, and the foreign key column information 145 of the table information 140 into standard data types using a data type dictionary as shown in FIG. The number of data items for each standard data type is aggregated for each of the primary key column information 141, the dependent column information 143, and the foreign key column information 145. Then, the feature information generation unit 105 sets the number of data items of each standard data type for the data type of the primary key column information 141 in the “primary key data type configuration” of the feature information item. The number of data items of each standard data type aggregated for the data type of the dependent column information 143 is set in the “data type configuration of the dependent attribute”, and the data type configuration of the foreign key column information 145 is set in the “data type configuration of the external key” of the feature information item. Set the number of data items of each standard data type aggregated for the data type.

<Step 122>
The schema integration support apparatus 102 determines whether “feature information” has been generated for all databases to be integrated.
For example, the schema information extraction unit 103 confirms whether or not the above step 120 and the above step 121 have been performed on the two databases of the first database 100 and the second database 101. If there is an unexecuted database, the process moves to step 120.

<Step 123: Similarity Evaluation Process>
Then, the similarity evaluation unit 108 calculates the similarity of each table between the integration target databases based on the “feature information” stored in the feature information storage device 107. Then, “mapping information” is generated and stored in the mapping model storage device 110 (an example of an output device), and the schema integration support processing is terminated.
Further, the similarity evaluation unit 108 outputs “mapping information” to an output device such as the display device 901 or the printer device 906, a program for merging data of the first database 100 and the second database 101, or the like. By causing the user to perform database integration work based on the output “mapping information”, the schema integration support apparatus 102 can improve the efficiency of the user's database integration work.
In the similarity evaluation processing step, the similarity evaluation unit 108 combines the tables of the two databases between the databases to be integrated, and calculates the table similarity for all table combinations. Further, when calculating the similarity of the table, the similarity evaluation unit 108 calculates the similarity for each feature information item, and the sum of the similarities of each feature information item is used as the similarity of “feature information”, that is, Let the similarity of the table.

FIG. 10 is a flowchart showing the flow of the similarity evaluation process of the similarity evaluation unit 108 in the first embodiment.
The step 123 (similarity evaluation processing step) executed by the similarity evaluation unit 108 will be described below with reference to FIG.

<Similarity calculation process: Step 181 to Step 184, Step 187, Step 188>
<Step 181>
The similarity evaluation unit 108 selects one of the databases to be integrated, and uses the selected database as a comparison source database when evaluating the similarity. For example, the number of tables defined in the first database 100 and the second database 101 is compared, and the table with the larger number of tables is used as the comparison source database. Here, it is assumed that the first database 100 is selected. The selection method may be any method. For example, the comparison source and the comparison destination are selected based on the order designated as the database.

<Step 182>
The similarity evaluation unit 108 extracts one “feature information” corresponding to each table of the comparison source database (first database 100) from the feature information storage device 107.

<Step 183>
The similarity evaluation unit 108 extracts one “feature information” corresponding to each table of the comparison target database (second database 101) from the feature information storage device 107.

<Step 184: Similarity calculation processing>
The similarity evaluation unit 108 compares the “feature information” of the comparison source database (first database 100) acquired in step 182 and the “feature information” of the comparison destination database (second database 101) acquired in step 183. Are compared to calculate the similarity of “feature information”, that is, the similarity of tables.

<Mapping information generation process: Step 185 to Step 186>
<Step 185>
The similarity evaluation unit 108 compares the similarity calculated in step 184 with the “similarity determination threshold” stored in the weighting variable storage device 109. As a result of the comparison, if the similarity is equal to or higher than the “similarity determination threshold” (similarity), the process proceeds to step 186. If the similarity is less than the “similarity determination threshold” (not similar), the process proceeds to step 187.

<Step 186>
The similarity evaluation unit 108 generates mapping candidates for the set of “feature information” determined to be similar in step 185 and sets the mapping candidates in the “mapping information” stored in the mapping model storage device 110.
At this time, the similarity evaluation unit 108 searches the “schema information” stored in the schema information storage device 104 for each of the set of “feature information” determined to be similar in step 185 and matches the “ID”. The information 140 is acquired, and the table name of the acquired table information 140 and the similarity calculated in step 184 are stored as mapping candidates in the mapping model storage device 110 as shown in FIG.

<Step 187>
The similarity evaluation unit 108 checks whether there is any unevaluated “feature information” in the comparison target database (second database 101). If there is unevaluated “feature information”, the process proceeds to step 183. If there is no unevaluated “feature information”, the process proceeds to step 188.

<Step 188>
The similarity evaluation unit 108 checks whether there is any unevaluated “feature information” in the comparison source database (first database 100). If there is unevaluated “feature information”, the process proceeds to step 182. If there is no unevaluated “feature information”, the similarity evaluation process is terminated.

  Through the similarity evaluation process described above, the similarity evaluation unit 108 compares all the tables included in the comparison source database with all the comparison destination tables, calculates the similarity between the tables, and creates mapping information.

  The case where the similarity evaluation process has two databases to be integrated has been described. If there are three or more databases to be integrated, steps 183 to 187 are processed for the number of databases using each database as a comparison target database, and steps S182 to 188 are processed for the number of databases using each database as a comparison source database. To do.

FIG. 11 is a flowchart showing a flow of similarity calculation processing of the similarity evaluation unit 108 in the first embodiment.
The step 184 (similarity calculation process) executed by the similarity evaluation unit 108 will be described below with reference to FIG.
In step 184 (similarity calculation processing), the similarity evaluation unit 108 calculates the similarity for each feature information item, and calculates the total similarity of each feature information item as the similarity of “feature information” (in the table). Similarity).

<Step 201: Feature Information Item Similarity Calculation Processing>
The similarity evaluation unit 108 compares the “feature information” of the table included in the comparison source database (first database 100) acquired in step 182 and the comparison destination database (second database 101) acquired in step 183. One feature information item is selected from the “feature information” in the table of the table, the values of the selected feature information items are compared, and the similarity of the selected feature information items is calculated.

<Step 202>
The similarity evaluation unit 108 checks whether or not the similarity of all feature information items has been calculated. If there is a feature information item for which the similarity is not calculated, the process proceeds to step 201, where all the feature information items are similar. If the degree has been calculated, the process proceeds to step 203.

<Step 203>
The similarity evaluation unit 108 multiplies the similarity of each feature information item by the “specific gravity” of each feature information item stored in the weighting variable storage device 109 and adds up the total value to the similarity of the “feature information” (in the table). The similarity calculation process is terminated.

Next, step 201 (feature information item similarity calculation processing) executed by the similarity evaluation unit 108 will be described.
The similarity of feature information items is calculated by comparing “single item values (feature information item values indicating the number of data items in which attributes indicating each dependency are defined)” and “ There are two ways to compare “data type configurations (feature information item values indicating the number of data items for each data type attribute)”. In the case of the feature information item shown in FIG. 6, the feature information items to be compared in the similarity calculation by “single item value” are “number of primary key attributes”, “number of primary key reference destination tables”, and “number of dependent attributes”. , “Number of foreign key attributes”, and feature information items to be compared in the similarity calculation by “data type configuration” are “primary key data type configuration”, “subordinate attribute data type configuration”, “foreign key "Data type configuration of".
The feature information item similarity calculation process using “single item value” and the feature information item similarity calculation process using “data type configuration” will be described below.

FIG. 12 is a flowchart showing the flow of the feature information item similarity calculation process (single item value) in the first embodiment.
First, the similarity calculation processing of the feature information item by the “single item value” of the similarity evaluation unit 108 in step 201 will be described below with reference to FIG.
The feature information items to be compared in the similarity calculation using “single item value” correspond to “number of primary key attributes”, “number of primary key reference tables”, “number of dependent attributes”, and “number of foreign key attributes” .

<Step 150>
The similarity evaluation unit 108 checks whether or not the feature information item value of the comparison source is 0. If it is 0, the process proceeds to step 151; otherwise, the process proceeds to step 152.
<Step 151>
When the feature information item value of the comparison source is 0, the similarity evaluation unit 108 checks whether the feature information item value of the comparison destination is 0. If it is 0, the process proceeds to step 157. If it is not 0, the process proceeds to step 156.
<Step 157>
When both the comparison source and comparison target feature information item values are 0, the similarity evaluation unit 108 sets the similarity of the feature information item to 1 (similarity) and ends the feature information item similarity calculation processing.

<Step 152>
If the comparison-source feature information item value is not 0, the similarity evaluation unit 108 checks whether the comparison-target feature information item value is 0. If it is 0, the process proceeds to step 156, and if it is not 0, the process proceeds to step 153.
<Step 153>
If both the comparison source and comparison target feature information item values are not 0, the similarity evaluation unit 108 performs a subtraction process to calculate the absolute value of the difference between the comparison source feature information item value and the comparison destination feature information item value. .
<Step 154>
The similarity evaluation unit 108 acquires the “similar allowable range” corresponding to the feature information items compared in step 153 from the weighting variable storage device 109, and the difference between the feature information item values of the comparison source and the comparison destination calculated in step 153 Is compared with the acquired “similar allowable range”. If the difference is equal to or smaller than the “similar allowable range”, the process proceeds to step 155. If the difference exceeds the “similar allowable range”, the process proceeds to step 156.
<Step 155>
The similarity evaluation unit 108 acquires the “variation rate” corresponding to the feature information item compared in step 153 from the weighting variable storage device 109, and based on the difference calculated in step 153 and the acquired “variation rate”, the feature The similarity of information items is calculated, and the feature information item similarity calculation processing is terminated. The similarity of the feature information item is calculated by the formula “1- (variation rate × difference)”.
<Step 156>
When the difference is larger than the similarity allowable range (step 154), or when one feature information item value of the comparison source and the comparison destination is 0 and the other feature information item value is not 0 (step 151, step 152), The similarity evaluation unit 108 sets the similarity of the feature information item to 0 (dissimilarity), and ends the feature information item similarity calculation process.
Through the above processing, the similarity evaluation unit 108 calculates the similarity of feature information items by comparing “single item values”.

FIG. 13 is a flowchart showing the flow of the feature information item similarity calculation process (data type configuration) according to the first embodiment.
First, the similarity calculation processing of feature information items by the “data type configuration” of the similarity evaluation unit 108 in step 201 will be described below with reference to FIG.
The feature information items to be compared in the similarity calculation based on “data type configuration” correspond to “primary key data type configuration”, “subordinate attribute data type configuration”, and “foreign key data type configuration”.

  The similarity evaluation unit 108 determines that the data items having the same standard data type are “corresponding data items (similar data items)” in the feature information item similarity calculation processing based on the “data type configuration”. The similarity is calculated based on the number of “non-corresponding data items” other than “corresponding data items”. Also, the similarity evaluation unit 108 replaces the “non-corresponding data item” whose standard data type does not match with another standard data type (alternative-compatible data type), so that the original standard data type does not match “ "Data item not to be processed" is treated as "corresponding data item". At this time, the similarity evaluation unit 108 determines the number of “corresponding data items” and “non-corresponding data” according to the similarity between the original standard data type and the alternative corresponding data type (similarity between standard data types). Set the number of items and calculate the similarity.

<Step 160>
The similarity evaluation unit 108 calculates how many “corresponding data items” exist between the original standard data types.
Therefore, the similarity evaluation unit 108 subtracts the number of comparison-source data items from the number of comparison-destination data items (number of character-type attributes, number of true-number attributes, etc.) for each standard data type. Calculate the difference value of the type.
<Step 161>
The similarity evaluation unit 108 determines whether all the data items for all the standard data types are “corresponding data items”, that is, whether there is no “non-corresponding data item”.
Therefore, the similarity evaluation unit 108 checks whether or not the difference value calculated in step 160 is 0 for all standard data types. When the difference values of all standard data types are 0, it indicates that all data items are associated with the comparison source and the comparison destination. If all the difference values are 0, the process proceeds to step 162; otherwise, the process proceeds to step 163.
<Step 162>
When the difference values of all the standard data types are 0, the similarity evaluation unit 108 sets the similarity of the feature information item to 1 (similarity) and ends the feature information item similarity calculation processing.
<Step 163>
If the standard data type with a difference value of 0 is not all standard data types, the similarity evaluation unit 108 determines whether all the data items are “corresponding data items” for either the comparison source or the comparison destination, That is, it is determined whether the number of data items of each data type is larger in all data types than the number of data items in one of the comparison source and the comparison destination.
Therefore, the similarity evaluation unit 108 checks whether all the difference values calculated in step 160 are positive (+) or negative (−). When all the difference values are positive (+) or 0, it indicates that all the comparison source data items are associated. When all the difference values are negative (−) or 0, it indicates that all the comparison target data items are associated. If all the difference values are positive or negative, the process proceeds to step 164. If not, the process proceeds to step 167.
FIG. 14 shows an example of the number of data items when all the comparison source data items are associated, FIG. 15 shows an example when all the comparison destination data items are associated, and FIG. 16 shows the comparison source / comparison destination. An example in which there are unsupported data items is shown.
<Step 164>
If all the difference values are positive or negative, the similarity evaluation unit 108 sums up the absolute values of the difference values in the standard data types calculated in step 160. The total value is the number of “incompatible data items” between the comparison source and the comparison destination.
In the case of the difference values shown in FIG. 14 or FIG. 15, the total is “5 = 3 + 2 + 0” <Step 165>
The similarity evaluation unit 108 acquires the “similar allowable range” corresponding to the feature information item from the weighting variable storage device 109, the total calculated in step 164 (the number of “non-corresponding data items”), and the acquired “similar allowable range”. Compare with Range. If the total is equal to or smaller than the “similar allowable range”, the process proceeds to step 166. If the total exceeds the “similar allowable range”, the process proceeds to step 174.
<Step 166>
When the total is equal to or less than the “similar allowable range”, the similarity evaluation unit 108 acquires the “variation rate” corresponding to the feature information item from the weighting variable storage device 109, and the total calculated in step 164 and the acquired “variation rate” Based on the above, the similarity of the feature information item is calculated, and the feature information item similarity calculation processing is terminated. The similarity of the feature information item is calculated by the formula “1- (variation rate × total)”.

  Hereinafter, a case where the difference value calculated in step 160 is the value shown in FIG. 17 will be described as an example.

<Step 167>
If there is a positive difference value and a negative difference value (step 163), the number of data items that are “corresponding data items” in the original standard data type instead of the alternative corresponding data type is calculated.
Therefore, the similarity evaluation unit 108 first calculates the sum (A) of the values of each data type of the comparison source. That is, the similarity evaluation unit 108 obtains the total number of data items as comparison sources. In the case of FIG. 17, “total (A) = 5 + 5 + 2 + 2 + 1 = 15” (step 167a).
Next, the sum (B) of negative values among the difference values calculated in step 160 is calculated. At this time, since “difference value = number of comparison destination data items−number of comparison source data items”, the difference value having a negative value is the number of comparison source data items having no data item corresponding to the comparison destination. In the case of FIG. 17, “total (B) = (− 2) + (− 1) = − 3” (step 167b).
Then, the total A and the total B are added to determine the number of “corresponding data items” between the comparison source and the comparison destination. In the case of FIG. 17, “the number of corresponding data items = 15 + (− 3) = 12” (step 167c).

<Step 168 to Step 170>
When replacing the standard data type of “non-corresponding data item” with an alternative correspondence data type, the similarity evaluation unit 108 selects an alternative correspondence data type from other standard data types having “non-corresponding data item”. The alternative corresponding data type is selected based on “similarity between standard data types”.

  Hereinafter, a case where “similarity between standard data types” is the value shown in FIG. 7 will be described as an example.

<Step 168>
The similarity evaluation unit 108 checks whether there is an alternative corresponding data type for the data type with the negative difference value calculated in step 160. In the case of FIG. 17, it is checked whether there is an alternative corresponding data type for the true number type (difference value = −2) and the binary type (difference value = −1). Hereinafter, an explanation will be given by taking the true number type as an example.
The similarity evaluation unit 108 first checks a standard data type whose similarity with a standard data type with a negative difference value is not 0 in “similarity between standard data types” stored in the weighting variable storage device 109. . In the case of “similarity between standard data types” shown in FIG. 7, standard data types whose similarity to the true number type is not 0 are a character type, an approximate number type, and a date type. If there is no standard data type whose similarity is not 0, there is no alternative corresponding data type (step 168a).
Next, the data type with a positive difference value is examined. In the case of the difference value shown in FIG. 17, the data type having a positive difference value among the character type, the approximate number type, and the date type is the character type and the approximate number type. If there is no data type with a positive difference value, there is no alternative corresponding data type (step 168b).
Then, the alternative corresponding data types are selected in order from the standard data type having the largest “similarity between standard data types”. In the case of the “similarity between standard data types” shown in FIG. 7, it is the round type that has a large similarity between the character type and the round type, and the alternative data type is the round type (step 168c).
The similarity evaluation unit 108 moves the process to step 169 when there is an alternative corresponding data type, and moves the process to step 170 when there is no alternative corresponding data type (step 168d).

<Step 169>
The similarity evaluation unit 108 reflects the data item handled as “corresponding data item” in the difference value calculated in step 160 by selecting an alternative corresponding data type in step 168.
Therefore, the similarity evaluation unit 108 first adds the difference value of the original standard data type, which is a negative value and the data type is replaced with the alternative corresponding data type, and the difference value of the positive corresponding alternative data type. In the case of the difference value shown in FIG. 17, “(−2) {the true type difference value} +1 {the approximate number type difference value} = − 1” (step 169 a).
When the result of addition is a positive value, the absolute value of the difference value of the original standard data type is set as the number of data items that can be replaced, the difference value of the original standard data type is set to 0, and the difference value of the replacement corresponding data type is set to Set as the addition result of step 169a. That is, if the result of addition is a positive value n, “number of alternative supported data items = absolute value of difference value of original standard data type”, “difference value of original standard data type = 0”, “substitution support” Data type difference value = n ″. If the result of addition is a negative value, the difference value of the substitution corresponding data type is set as the number of data items corresponding to substitution, the difference value of the original standard data type is set as the addition result of step 169a, and the difference value of the substitution corresponding data type is Set as 0. That is, in the case of the differential values shown in FIG. 17, “the number of substitution-corresponding data items = 1”, “the true type differential value = (− 1)”, and “the approximate type differential value = 0”. When the result of addition is 0, the difference value of the alternative correspondence data type is set as the number of data items corresponding to the alternative correspondence, and the difference values of both the original standard data type and the alternative correspondence data type are set to 0. That is, if the result of addition is 0, “the number of substitution-supported data items = difference value of substitution-compatible data type”, “difference value of original standard data type = 0”, “difference value of substitution-compatible data type = 0” "(Step 169b).
Next, in step 167, the similarity between the original standard data type indicated by the “similarity between standard data types” in the weighting variable storage device 109 and the alternative correspondence data type is multiplied by the number of alternative correspondence data items. Is added to the number of “corresponding data items”. In the case of the difference value shown in FIG. 17, “1 {number of data items corresponding to substitution of exact number type} × 0.8 {similarity between exact number type and approximate number type} +12 {correspondence before replacement with substitution corresponding data type Number of data items to be performed} = 12.8 {number of corresponding data items after being replaced with alternative corresponding data type} "(step 169c)

<Step 170>
The similarity evaluation unit 108 determines whether or not “non-corresponding data items” that have not been searched for an alternative corresponding data type (step 168) remain.
Therefore, the similarity evaluation unit 108 checks whether there is a search for an alternative corresponding data type among the standard data types having a negative difference value calculated in step 160, and there is an unsearched standard data type. Moves the process to step 168, and if not, moves the process to step 171. In the case of the difference value shown in FIG. 17, the result of the first step 169 is shown in FIG. In the case of the result shown in FIG. 18, when there is a difference value that is negative and no alternative corresponding data type is searched, the ones with a negative difference value are the truth type and the binary type. Since the type has already been searched, the process moves to step 168 for the binary type. When the binary processing is completed, there is no unsearched one, and the processing is moved to step 171.

<Step 171>
The similarity evaluation unit 108 calculates the number of “non-corresponding data items”.
At this time, the similarity evaluation unit 108 compares the absolute value of the sum of the positive difference values and the sum of the negative difference values, and sets the larger value as the number of “non-corresponding data items”. When the difference value is 0 for all data types, the number of “non-corresponding data items” is 0. In the case of the difference value shown in FIG. 17, the result of step 170 is FIG. In the case of the result shown in FIG. 18, since “the sum of positive difference values = 1” and “the absolute value of the sum of negative difference values = 2”, “the number of non-corresponding data items = 2”. .

<Step 172>
The similarity evaluation unit 108 acquires the “similar allowable range” corresponding to the feature information item from the weighting variable storage device 109, the total calculated in step 171 (the number of “non-corresponding data items”), and the acquired “similar allowable range”. Compare with Range. If the total is equal to or smaller than the “similar allowable range”, the process proceeds to step 173. If the total exceeds the “similar allowable range”, the process proceeds to step 174. If the result of step 170 is FIG. 18 and the sum is 2, if “similar allowable range” = 3, then “2 {total} ≦ 3 {similar allowable range}”, so the process moves to step 173. If “similar permissible range” = 1, “2 {total}> 1 {similar permissible range}”, and the process proceeds to step 174.

<Step 173>
When the total is equal to or less than the “similar allowable range”, the similarity evaluation unit 108 acquires the “variation rate” corresponding to the feature information item from the weighting variable storage device 109, the sum calculated in step 171, and the acquired “variation” Based on the “rate”, the similarity of the feature information item is calculated, and the feature information item similarity calculation processing is terminated. The similarity of the feature information item is calculated by the formula “(1− (variation rate × total)) × (number of corresponding data items ÷ total number of data items)”.
At this time, the similarity evaluation unit 108 first calculates the sum of the values of the respective data types for each of the comparison source and the comparison destination, and calculates the smaller total number of data items, that is, the smaller number of data items as the total number of data items. To do. In the case where the result of step 170 is that shown in FIG. 18, “total number of data types of comparison source = 5 + 5 + 2 + 2 + 1 = 15” and “total of data type of comparison destination = 6 + 3 + 3 + 2 + 0 = 14”. = 14 (<15) "(step 173a).
Then, the similarity of the feature information item is calculated based on the number of “corresponding data items” calculated in step 167 and step 169, the total calculated in step 171 and the “variation rate” acquired from the weighting variable storage device 109. To do. When the “variation rate” is 0.1, the result of step 170 is FIG. 18, the total is 2, and the number of “corresponding data items” is 12.8, “similarity = 0.73≈ (1− (0. 1 × 2)) × (12.8 ÷ 14) ”(step 173b).

<Step 174>
When the total exceeds the “similar allowable range” (steps 165 and 172), the similarity evaluation unit 108 sets the similarity of the feature information item to 0 (non-similarity) and ends the feature information item similarity calculation processing.
With the above processing, the similarity evaluation unit 108 calculates the similarity of the feature information item based on the comparison of “data type configuration”.

  In the above embodiment, the schema integration support process between different databases includes a data type dictionary that associates the unique data type of the database with the standard data type, and a weighting variable that is used for similarity calculation and evaluation criteria. Means for extracting information (schema information extracting unit 103), means for generating feature information of data from the extracted schema information (feature information generating unit 105), and creating a mapping model by evaluating similarity from the generated feature information A schema integration support method characterized by having means (similarity evaluation unit 108) for performing the above has been described.

  In addition, a schema integration support method characterized by generating feature information mainly using constraint conditions and dependency relationships between data as schema information has been described.

As described above, the feature information of the table is created from the schema information of the database by paying attention to the constraint condition and dependency relationship between the data, and the similarity of the table is calculated by comparing the feature information, and the similarity of the fixed criteria Table mapping candidates having a degree (similarity greater than or equal to “similarity determination threshold”) can be generated.
Also, since the similarity is calculated for each mapping candidate, the mapping candidates can be ranked by comparing the similarities.
In addition, since the feature information includes information related to other tables such as foreign key attributes and the number of primary key reference tables, other information can be used even when there are multiple tables with similar structures. The difference in similarity can be determined from the relationship with the table.
In addition, attribute data types are associated with standard data types, and the similarity between different standard data types can be evaluated. Therefore, schemas in which data items having the same meaning are defined with different data types (for example, , Date data is defined as a single or character type and the other is defined as a date type).
Furthermore, the feature information is defined as a set of numerical items, and the similarity is evaluated by a common method for each item. Therefore, addition and deletion of feature information items can be easily handled.
Further, since the feature information is created from the schema information of the database, mapping candidates can be created without accessing the actual data of the database.
Furthermore, since actual data is not used for determining mapping candidates, preprocessing such as standardization (unification) of data contents between databases to be integrated is not required.
Since the attribute name of the data is not used as the feature information item, mapping candidates can be created even for a database in which the attribute name is not properly set.

  In the above embodiment, the “relational database” has been described as an example. However, other databases such as a hierarchical database, a network database, and an object-oriented database may be databases to be integrated. The “table” in the above embodiment means a set of data having a specific data structure, and in a database other than the “relational database”, the data structure (particularly, the dependency between data) is similar to the above embodiment. Similarity may be calculated based on the relationship and data type) to generate mapping information.

FIG. 3 is a diagram illustrating an appearance of a schema integration support device 102 according to the first embodiment. FIG. 2 is a hardware configuration diagram of the schema integration support apparatus according to the first embodiment. FIG. 3 is a functional configuration diagram showing functional elements of the schema integration support apparatus 102 according to the first embodiment. FIG. 4 is a data structure diagram showing an example of “schema information” in the first embodiment. FIG. 4 is a diagram showing an example of a “data type dictionary” in the first embodiment. FIG. 6 is a diagram illustrating an example of “feature information” items according to the first embodiment. FIG. 4 is a diagram showing an example of “similarity between standard data types” in the first embodiment. FIG. 6 shows an example of “mapping information” in the first embodiment. 5 is a flowchart showing schema integration support processing (schema integration support method) in the first embodiment. 5 is a flowchart showing a flow of similarity evaluation processing of a similarity evaluation unit 108 in the first embodiment. 6 is a flowchart showing a flow of similarity calculation processing performed by the similarity evaluation unit according to the first embodiment. 5 is a flowchart showing a flow of feature information item similarity calculation processing (single item value) in the first embodiment. 6 is a flowchart showing a flow of feature information item similarity calculation processing (configuration of data type) according to the first embodiment; The figure which shows an example of the data item number for every standard data type of the comparison origin and comparison destination in case all the difference values in Embodiment 1 are 0 or more. The figure which shows an example of the data item number for every standard data type of the comparison origin and comparison destination in case the difference values in Embodiment 1 are all 0 or less. The figure which shows an example of the number of data items for every standard data type of the comparison origin and comparison destination in case the positive value and the negative value are contained in the difference value in Embodiment 1. FIG. 6 is a diagram illustrating an example of a difference value calculated in step 160 in the first embodiment. FIG. 6 is a diagram illustrating an example of a difference value after step 169 and step 170 in the first embodiment.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 100 1st database, 101 2nd database, 102 Schema integration support apparatus, 103 Schema information extraction part, 104 Schema information storage apparatus, 105 Feature information generation part, 106 Data type dictionary storage apparatus, 107 Feature information storage apparatus, 108 Similarity evaluation unit, 109 weighting variable storage device, 110 mapping model storage device, 140 table information, 141 primary key column information, 142 primary key reference destination information, 143 dependent column information, 144 external key information, 145 external key column information, 901 display Device, 902 K / B, 903 mouse, 904 FDD, 905 CDD, 906 printer device, 907 scanner device, 908 optical disk device, 910 system unit, 911 CPU, 912 bus, 913 ROM, 914 RAM, 915 communication board, 920 magnetic disk device, 921 OS, 922 window system, 923 program group, 924 file group, 931 telephone, 932 FAX machine, 940 Internet, 941 Web server, 942 LAN.

Claims (9)

A schema integration support device that outputs mapping information about a table to be mapped between the first database and the second database as information for supporting integration between the first database and the second database;
A schema information storage device that stores data structure information of table A included in the first database and data structure information of table B included in the second database;
The first database is obtained by comparing the data structure information of the table A of the first database stored in the schema information storage device with the data structure information of the table B of the second database using the central processing unit. What is claimed is: 1. A schema integration support apparatus comprising: a similarity evaluation unit that calculates a similarity between a table A and a table B included in a second database and outputs the calculated similarity as mapping information to an output device. The schema information storage device stores an attribute indicating a dependency of data items included in the table in the data structure information,
The schema integration support apparatus according to claim 1, wherein the similarity evaluation unit calculates a similarity by comparing attributes indicating data item dependency included in the table included in the data structure information. The schema information storage device stores at least one of a primary key attribute, a subordinate attribute, and a foreign key attribute, and stores a subordinate relationship of data items of the table;
3. The schema according to claim 2, wherein the similarity evaluation unit compares at least one of a primary key attribute, a subordinate attribute, and a foreign key attribute included in the data structure information to calculate a similarity. Integrated support device. The similarity evaluation unit
The similarity is calculated by comparing the number of data items having at least one of the number of data items having a primary key attribute, the number of data items having a dependent attribute, and the number of data items having a foreign key attribute. The schema integration support apparatus according to claim 3. The schema information storage device stores an attribute indicating a data type of a data item included in the table in the data structure information,
The schema integration support apparatus according to claim 1, wherein the similarity evaluation unit compares the attribute indicating the data type of the data item included in the table included in the data structure information and calculates the similarity. The similarity evaluation unit
6. The schema integration support apparatus according to claim 5, wherein the similarity is calculated by comparing the number of data items for each data type. The schema information storage device stores at least one of a primary key attribute, a dependent attribute, and a foreign key attribute of a data item included in the table in the data structure information,
The similarity evaluation unit includes at least one of the number of data items having a primary key attribute for each data type, the number of data items having a dependent attribute for each data type, and the number of data items having a foreign key attribute for each data type. 7. The schema integration support apparatus according to claim 6, wherein the similarity is calculated by comparing the number of each data item for each data type. A schema integration support method of a schema integration support apparatus that outputs mapping information about tables mapped in the first database and the second database as information supporting the integration of the first database and the second database;
Storing the data structure information of the table A included in the first database and the data structure information of the table B included in the second database in the schema information storage device;
The similarity evaluation unit compares the data structure information of the table A included in the first database stored in the schema information storage device with the data structure information of the table B included in the second database using the central processing unit. Schema integration characterized by calculating similarity between table A of the first database and table B of the second database and performing similarity evaluation processing for outputting the calculated similarity to the output device as mapping information Support method.   A schema integration support program for causing a computer to execute the schema integration support method according to claim 8.

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