Abstract: The acquisition unit 52B acquires sales request information S1, which is request information regarding a sale of data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53B determines, on a basis of the sales request information S1, whether or not there is a customer who demands the data. The notification unit 54B notifies, in a case where the determination unit 53B determines that there is the customer, the owner terminal 2 of sales response information S2 indicating information regarding the customer for the data.

Abstract: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering processing for performing co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Prediction model generation means 72 performs prediction model generation processing for generating a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

Abstract: An input unit 81 inputs an annotation result that is data to which a label is added based on an annotator's answer, and label addition information that indicates an inter-label structure. An answer integration unit 82 integrates the annotation results and estimates the label of the data. A skill estimation unit 83 estimates a skill of the annotator based on a difference between the estimated label and the labels included in the annotation results. An update unit 84 updates, based on the estimated skill of the annotator, the feature of a task for adding a label the inter-label structure of which is specified based on the label addition information to the data, the update being performed so that the feature conforms to the annotation results. An output unit 85 outputs the label estimated by the answer integration unit 82. The answer integration unit 82 estimates the label based on a weight calculated in accordance with closeness of the skill of the annotator and the feature of the task to the label.

Abstract: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Each of the first IDs serves as an ID of a record in the first master data. Each of the second IDs serves as an ID of a record in the second master data. Prediction model generation means 72 generates a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

Abstract: An information processing apparatus (1) includes a storage unit (11) that stores knowledge information (111) containing a relationship regarding the correspondence between a plurality of types of element information (1111 to 111n) to be used for referring to a specified element value and conceptual information (1110) indicating a concept of the element value, an adding unit (12) that adds, to each of a plurality of graphs representing a processing structure in each of a plurality of source codes where any one of the element information (1111 to 111n) is described, the conceptual information (1110) identified from the element information corresponding to each node in the graph based on the knowledge information (111) as attribute information related to the node, and an extraction unit (13) that extracts a subgraph common to the graphs after the adding based on the conceptual information (1110).

Abstract: A learning means 71 learns, based on learning data containing the meaning of a column in a table and the meaning of the table, a model indicating regularity between the meaning of the column in the table and the meaning of the table. An estimation means 72 estimates the meaning of the table based on the meaning of a column of a table to be input and the model.

Abstract: A column meaning candidate selection means 303 selects a candidate for meaning of a column whose meaning is to be inferred. A column similarity computation means 304 computes, for each candidate for meaning selected by the column meaning candidate selection means 303, a score indicating a similarity between the selected candidate for meaning and meaning of each column other than the column whose meaning is to be inferred contained in a table. A column meaning identification means 305 identifies meaning of the column whose meaning is to be inferred from the candidates for meaning of the column with use of the score computed by the column similarity computation means 304.

Abstract: A table meaning candidate selection means 503 selects a candidate for meaning of a table whose meaning is to be inferred. A table similarity computation means 504 computes, for each candidate for meaning selected by the table meaning candidate selection means 503, a score indicating a similarity between the selected candidate for meaning and meaning of each table, other than the table whose meaning is to be inferred, related to the table whose meaning is to be inferred. A table meaning identification means 505 identifies meaning of the table whose meaning is to be inferred from the candidates for meaning of the table with use of the score computed by the table similarity computation means 504.

Abstract: A subgraph extraction means 71 extracts, from a document structure graph indicating a document structure, a subgraph as a part of the document structure graph on the basis of inter-word relationship information indicating a relationship between a word and a word. A rule creation means 72 creates a rule for extracting a subgraph having the same structure as the subgraph from the document structure graph. A knowledge addition means 73 extracts a subgraph from the document structure graph in accordance with the rule and adds the information indicated by the subgraph to the inter-word relationship information.

Abstract: An information processing device includes a statistical value holding unit configured to hold a statistical value of data included in each of two or more blocks into which a data set is divided; a query history holding unit configured to hold information about a past query on the data set as a query history; an estimation unit configured to, based on the query history, estimate a block size that minimizes an average cost of deriving an answer to a query by using the statistical value of at least one of the blocks; and a block creation unit configured to, based on the block size estimated by the estimation unit, create two or more blocks by dividing the data set, calculate the statistical value for each of the created blocks and cause the statistical value holding unit to hold the calculated statistical values.

Abstract: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Each of the first IDs serves as an ID of a record in the first master data. Each of the second IDs serves as an ID of a record in the second master data. Prediction model generation means 72 generates a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

Abstract: A data processing device according to the present invention includes: a partition unit that horizontally partitions records included in table data into a plurality of blocks, the horizontal partitioning indicating partitioning that uses records as a unit; a statistical value calculation unit that calculates, for each of the blocks, a statistical value of an attribute included in the records of the block; a determination unit that determines, when processing a query for performing aggregation processing after record selection processing with respect to the table data, based on the statistical value, for each of the blocks, whether all records in the block are selected or not based on the selection processing; and a query execution unit that uses, for a block determined that all records are selected based on the determination unit, the statistical value of the determined block as a result of the query for the determined block.

Abstract: An object is to provide a clustering system capable of performing clustering of a plurality of types of items to be able to recommend an item whose corresponding textual data exists but relational data with another type of item does not exist, to the other type of item. A first clustering means 3001 performs clustering of first IDs, based on the relational data. A second clustering means 3002 performs clustering of second IDs, based on the relational data and textual data associated with the second IDs. A topic assignment means 3003 assigns a topic for each word included in textual data corresponding to each second ID. A parameter decision means 3004 decides a parameter used for first clustering processing, a parameter used for second clustering processing, and a parameter used for topic assignment processing. The processing described above is repeated until it is determined that a predetermined condition is satisfied.

Abstract: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering processing for performing co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Prediction model generation means 72 performs prediction model generation processing for generating a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

Abstract: An object is to provide a clustering system capable of performing clustering of a plurality of types of items to be able to recommend an item whose corresponding textual data exists but relational data with another type of item does not exist, to the other type of item. A first clustering means 3001 performs clustering of first IDs, based on the relational data. A second clustering means 3002 performs clustering of second IDs, based on the relational data and textual data associated with the second IDs. A topic assignment means 3003 assigns a topic for each word included in textual data corresponding to each second ID. A parameter decision means 3004 decides a parameter used for first clustering processing, a parameter used for second clustering processing, and a parameter used for topic assignment processing. The processing described above is repeated until it is determined that a predetermined condition is satisfied.

Abstract: A learning means 71 learns, on the basis of learning data including a table including a meaning of a column, and a meaning of the table, a model indicating regularity between a distribution of attribute values according to the meaning of the column in the table and the meaning of the table. An estimating means 72 estimates, on the basis of a distribution of attribute values according to a meaning of a column in an input table and the model, a meaning of the table.

Abstract: A co-clustering system capable of further improving prediction accuracy of a prediction model for each cluster is provided. Based on first master data, second master data, and fact data indicating a relation between a first ID which is an ID of a record in the first master data and a second ID which is an ID of a record in the second master data, the co-clustering means 71 executes co-clustering processing of co-clustering the first IDs and the second IDs. The prediction model generation means 72 executes prediction model generation processing of generating a prediction model for each cluster of at least the first ID. The determination means 73 determines whether or not a predetermined condition is satisfied. The prediction model generation processing and the co-clustering processing are repeated until it is determined that the predetermined condition is satisfied.

Abstract: A learning means 71 learns, based on learning data containing the meaning of a column in a table and the meaning of the table, a model indicating regularity between the meaning of the column in the table and the meaning of the table. An estimation means 72 estimates the meaning of the table based on the meaning of a column of a table to be input and the model.

Abstract: A prediction system capable of predicting an unknown value of an attribute with high accuracy is provided. Based on first master data, second master data, and fact data indicating a relation between a first ID which is an ID of a record in the first master data and a second ID which is an ID of a record in the second master data, the co-clustering means 81 co-clusters the first IDs and the second IDs. The prediction model generation means 82 generates a prediction model for each cluster of the first ID output from the co-clustering means 81. When the first ID and the objective variable which is one of the attributes included in the first master data are specified, the prediction means 83 predicts the value of the objective variable corresponding to the first ID based on the prediction model and the belonging probability that the first ID belongs to each cluster.

Abstract: This information processing device is provided with: a statistical value storing means that stores a statistical value for each block, said block being a division of a data set; a query history storing unit that stores information related to a past query as a query history; an estimating means that, on the basis of the query history, estimates a block size when dividing the data set into at least two blocks so as to minimize the cost of statistical processing carried out using a statistical value for each block; and a block creating means that divides the data set using the block size estimated by the estimating means, and, for each divided block, calculates a statistical value related to the data included in the block and causes the statistical value storing means to store the statistical value.