Abstract: A descriptor generation unit 81 uses a first template prepared in advance to generate a feature descriptor, which generates a feature that may affect a prediction target from a first table including a variable of the prediction target and a second table. A feature generation unit 82 generates the feature by applying the feature descriptor to the first and second tables. A feature explanation generation unit 83 generates a feature explanation about the feature descriptor or the feature on the basis of a second template. An accepting unit 84 accepts values to be assigned to the first and second templates. The descriptor generation unit 81 generates the feature descriptor by assigning the accepted values to the first template, and the feature explanation generation unit 83 generates the feature explanation by assigning the values assigned to the first template to the second template.

Abstract: A table acquiring means 381 acquires a first table including prediction objects and first attributes, and a second table including second attributes. A receiving means 382 receives a similarity function and condition for similarity used to calculate the similarity between the first attribute and the second attribute. A feature generating means 383 generates feature candidates able to affect a prediction object using a combination condition for combining a record in the first table including the value of a first attribute satisfying the condition with a record in the second table including the value of a second attribute satisfying the similarity calculated with the value of the first attribute and the value of the second attribute using the similarity function, and using a reduction method for a plurality of records in the second table and a reduction condition represented by the column to be aggregated. A feature selecting means 384 selects an optimum feature for the prediction from the feature candidates.

Abstract: A prediction data input unit 91 inputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unit 92 generates an exposure pattern which is an explanatory variable indicating the content of a commercial message scheduled to be performed during a period from predicted time to future prediction target time. A component determination unit 93 determines the component used for predicting the sales, on the basis of a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure, gating functions for determining a branch direction in the nodes of the hierarchical latent structure, and the prediction data and the exposure pattern.

Abstract: An analysis process receiving unit 282 receives creation of an analysis process which is a series of processing operations for analyzing data using a column name defined by a schema to be applied to a table. A schema/analysis process storing unit 283 stores information in which the received analysis process is associated with a schema that can be applied to the analysis process. When selection of an analysis process has been received from the user, a table retrieval unit 284 outputs a list of tables used by the received analysis process on the basis of information stored in a table/schema storing unit and information stored in a schema/analysis process storing unit 283. An analysis process executing unit 285 receives selection of a table from the outputted list of tables, and executes the selected analysis process on the received table.

Abstract: An analysis process receiving unit 182 receives creation of an analysis process which is a series of processing operations for analyzing data using a column name defined by a schema to be applied to a table. A schema/analysis process storing unit 183 stores information in which the received analysis process is associated with a schema that can be applied to the analysis process. When selection of a table has been received from the user, an analysis process retrieval unit 184 outputs a list of tables used by the received analysis process on the basis of information stored in a table/schema storing unit and information stored in a schema/analysis process storing unit 183. An analysis process executing unit 185 receives selection of an analysis process from the outputted list, and executes the selected analysis process on the received table.

Abstract: A table storage unit 81 stores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unit 82 creates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unit 82 creates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

Abstract: An accepting unit 71 accepts a feature descriptor, which generates a feature, i.e. a variable that may affect a prediction target, from a first table including a variable of the prediction target and a second table. An extraction unit 72 extracts, from the feature descriptor, table information indicating a name of the second table, joint information indicating key columns when joining the first table and the second table, and aggregation information indicating an aggregation operation to be performed on a plurality of rows in the second table and a column as a target of the aggregation operation. A feature explanation generation unit 73 assigns the extracted information to a feature explanation template to generate a feature explanation of the feature, which is obtained by applying the feature generator to the first table and the second table.

Abstract: A descriptor generation unit 81 uses a first template prepared in advance to generate a feature descriptor, which generates a feature that may affect a prediction target from a first table including a variable of the prediction target and a second table. A feature generation unit 82 generates the feature by applying the feature descriptor to the first and second tables. A feature explanation generation unit 83 generates a feature explanation about the feature descriptor or the feature on the basis of a second template. An accepting unit 84 accepts values to be assigned to the first and second templates. The descriptor generation unit 81 generates the feature descriptor by assigning the accepted values to the first template, and the feature explanation generation unit 83 generates the feature explanation by assigning the values assigned to the first template to the second template.

Abstract: A table acquiring means 181 acquires a first table including prediction targets and first geographic attributes, and a second table including second geographic attributes. A receiving means 182 receives geographic relationships and degrees of geographic relationships. A combination condition generating means 183 generates a combination condition for combining a record included in the first table with a record included in the second table so that the relationship between the value of a first geographic attribute and the value of a second geographic attribute satisfies the degree of geographic relationship.

Abstract: A table acquiring means 381 acquires a first table including prediction objects and first attributes, and a second table including second attributes. A receiving means 382 receives a similarity function and condition for similarity used to calculate the similarity between the first attribute and the second attribute. A feature generating means 383 generates feature candidates able to affect a prediction object using a combination condition for combining a record in the first table including the value of a first attribute satisfying the condition with a record in the second table including the value of a second attribute satisfying the similarity calculated with the value of the first attribute and the value of the second attribute using the similarity function, and using a reduction method for a plurality of records in the second table and a reduction condition represented by the column to be aggregated. A feature selecting means 384 selects an optimum feature for the prediction from the feature candidates.

Abstract: An enumeration plan generation unit 81 generates a set of logical formula structures each representing a way of combining logical formula expressions each representing a combination of features by use of the features of learning data items and the maximum number of features to be combined, and generates partial logical formula structures by dividing a logical formula expression included in each of the generated logical formula structures into two, and generates an enumeration plan in which the partial logical formula structures are linked to the logical formula structure from which the partial logical formula structures are divided. The feature generation unit 82 generates a new feature that is a combination of the features corresponding to the generated partial logical formula structures.

Abstract: A feature design unit 81 designs, from relational data, a feature as a variable likely to affect an objective variable. A feature generating unit 82 generates the designed feature, from the relational data. A learning unit 83 learns a prediction model, on the basis of the generated feature.

Abstract: The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

Abstract: This invention provides a water-leakage state estimation system configured to estimate a state of a water leakage in a specific area of a water distribution network. A learning unit is configured to: receive labeled data, which is labeled so as to separate past flow rate data into abnormal values and normal values, and past environment state condition data; build a prediction model for predicting the normal values in the labeled data through learning; and determine a score parameter defining a length of a period involving data to be verified through learning as well. A water-leakage estimation unit is configured to: compare predicted flow rate data obtained by supplying current environment condition data into the prediction model and current flow rate data to produce error values; and calculate an average value of the error values in the period of a window width defined by the score parameter to estimate a water-leakage score representing a state of the water-leakage in the specific area.

Abstract: A table storage unit 81 stores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unit 82 creates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unit 82 creates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

Abstract: This invention provides a water-leakage state estimation system configured to estimate a state of a water leakage in a specific area of a water distribution network. A learning unit is configured to: receive labeled data, which is labeled so as to separate past flow rate data into abnormal values and normal values, and past environment state condition data; build a prediction model for predicting the normal values in the labeled data through learning; and determine a score parameter defining a length of a period involving data to be verified through learning as well. A water-leakage estimation unit is configured to: compare predicted flow rate data obtained by supplying current environment condition data into the prediction model and current flow rate data to produce error values; and calculate an average value of the error values in the period of a window width defined by the score parameter to estimate a water-leakage score representing a state of the water-leakage in the specific area.

Abstract: The information processing device 1 processes document collections having tags permitting semantic class identification appended to each document and comprises a search unit 2, which creates multiple semantic class units containing one, two, or more semantic classes based on a taxonomy that identifies relationships between semantic classes, and a frequency calculation unit 3 which, for each of the semantic class units, identifies documents that match that semantic class unit in the document collections and, for these matching documents, calculates a first frequency that represents the frequency of occurrence in a designated document collection and a second frequency that represents the frequency of occurrence in non-designated document collections. Once the calculations have been performed, the search unit 2 identifies any of the semantic class units based on the first frequency and the second frequency of the matching documents.

Abstract: A prediction data input unit 91 inputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unit 92 generates an exposure pattern which is an explanatory variable indicating the content of a commercial message scheduled to be performed during a period from predicted time to future prediction target time. A component determination unit 93 determines the component used for predicting the sales, on the basis of a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure, gating functions for determining a branch direction in the nodes of the hierarchical latent structure, and the prediction data and the exposure pattern.

Abstract: An enumeration plan generation unit 81 generates a set of logical formula structures each representing a way of combining logical formula expressions each representing a combination of features by use of the features of learning data items and the maximum number of features to be combined, and generates partial logical formula structures by dividing a logical formula expression included in each of the generated logical formula structures into two, and generates an enumeration plan in which the partial logical formula structures are linked to the logical formula structure from which the partial logical formula structures are divided. The feature generation unit 82 generates a new feature that is a combination of the features corresponding to the generated partial logical formula structures.

Abstract: In an inverted list of each node in a taxonomy, among each node, an inverted list of the highest node is a list of integer values indicating an identifier of search subject data, and an inverted list of a node other than the highest node, in place of the identifier, is a list of integer values indicating a position in an inverted list corresponding to a node that is higher by one than the node. Furthermore, a list of integer values in an inverted list of each node is divided into two or more blocks, and a differential value between an integer value and an integer value directly before the integer value in the block is converted into a bit string of a variable length integer code.