Abstract: A clustering method performed by a computer for clustering on a plurality of elements given relationship data concerning the relationship between some elements, the method includes: calculating relevance between the plurality of elements by using the attributes of the plurality of elements; calculating a threshold value for identifying link attributes between the elements in accordance with the relevance and the relationship data concerning each set of elements given the relationship data; determining link types between the plurality of elements in accordance with the threshold value; and performing clustering in accordance with the result of determination.

Abstract: A control unit calculates a first document count indicating the number of documents that are retrieved based on a first keyword, a second document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a second keyword belonging to one item, and a third document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a third keyword belonging to the one item, from the set of documents. The control unit calculates an evaluation value of the one item, based on the first, second, and third document counts. When the evaluation value satisfies a predetermined condition, the control unit outputs recommendation information recommending use of the one item to narrow down documents that are retrieved.

Abstract: A test selection method includes: generating, by a computer, relationship information that includes information indicating whether there is a relationship between each pair of one of a plurality of first tests and one of a plurality of second tests, and information on the number of relationships that indicates the number of pairs having the relationship from among a plurality of the pairs, by use of a result of performing the plurality of first tests and a result of performing the plurality of second tests; and when a specific test included in the plurality of first tests is designated, extracting by the computer, from among the plurality of second tests, a related test that relates to the specific test, on the basis of the relationship information and the information on the number of relationships.

Abstract: A method for reconfiguration of a database, the method includes extracting a plurality of entity candidates based on a plurality of attribute names and first information about a relation degree between the attribute name and the entity, first identifying a plurality of entity candidate groups in which each is comprised of extracted the plurality of entity candidates and has a correspondence relation with all the attribute names and the number of the entity candidates is minimum, second identifying the entity candidate group, in which a total of relation degrees between the entities in the entity candidate group is maximum, based on second information about the relation degree between the entities, and assigning each of the plurality of attribute names to either one of the plurality of entity candidates so as to have the correspondence relation of the attribute name and the entity candidate based on the first information.

Abstract: A calculation unit calculates, for each of a plurality of systems in which a countermeasure is taken, a maturity index of the system, indicating the degree of operational stability of the system, based on a value related to a non-functional requirement of the system. An evaluation unit evaluates usefulness of the countermeasure for a particular system based on similarity of configuration between the particular system and the system, timing that the countermeasure is taken, effects of the countermeasure, and the calculated maturity index.

Abstract: A control unit calculates a first document count indicating the number of documents that are retrieved based on a first keyword, a second document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a second keyword belonging to one item, and a third document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a third keyword belonging to the one item, from the set of documents. The control unit calculates an evaluation value of the one item, based on the first, second, and third document counts. When the evaluation value satisfies a predetermined condition, the control unit outputs recommendation information recommending use of the one item to narrow down documents that are retrieved.

Abstract: A test selection method includes: generating, by a computer, relationship information that includes information indicating whether there is a relationship between each pair of one of a plurality of first tests and one of a plurality of second tests, and information on the number of relationships that indicates the number of pairs having the relationship from among a plurality of the pairs, by use of a result of performing the plurality of first tests and a result of performing the plurality of second tests; and when a specific test included in the plurality of first tests is designated, extracting by the computer, from among the plurality of second tests, a related test that relates to the specific test, on the basis of the relationship information and the information on the number of relationships.

Abstract: Data relevance calculation program for; extracting topics from a group of individual data items and a group of target data items, each item including an index part and a content part, and at least a part of the target data items is related to any of the individual data items, based on words included in the individual data items and the target data items; setting an attribute of each topic based on a degree at which the topic is characterized by words included in the index part or included in the content part; and calculating relevance between any of the individual data items and each of the target data items based on the strength of a relationship between a topic included in an individual data item and a topic included in a target data item related to the individual data item and on the attribute of each topic.

Abstract: A method of analyzing a masked task log obtained by masking part of a task log, which is a record of a task performed in a workflow system, the method includes: acquiring a plurality of pieces of disclosed information that are viewable to a plurality of users based on unmasked information in the masked task log; specifying relevance between the plurality of pieces of disclosed information and a plurality of tasks performed in the workflow system, the plurality of tasks including the task; and calculating an index based on the relevance by a processor, the index indicating a possibility that content of the masked task log is determined from the plurality of pieces of disclosed information.

Abstract: The right for correlating a region or an existence with a virtual region in a virtual world structured in for example the Internet is sold, bought, lent, or rented. For example, when a buyer bought a virtual region from a seller, the virtual region of the seller is correlated with a virtual region of the buyer in a link method, a paste method, or a projection method.

Abstract: A calculation unit calculates, for each of a plurality of systems in which a countermeasure is taken, a maturity index of the system, indicating the degree of operational stability of the system, based on a value related to a non-functional requirement of the system. An evaluation unit evaluates usefulness of the countermeasure for a particular system based on similarity of configuration between the particular system and the system, timing that the countermeasure is taken, effects of the countermeasure, and the calculated maturity index.

Abstract: A method of managing a business description, the method includes: monitoring an editing state of each of a plurality of business description information pieces stored in a storage device, each of the plurality of business description information pieces describing information indicating each of a plurality of items of work according to a work order; detecting currently edited business description information of which the editing state is in an editing process; detecting edited content of the currently edited business description information; generating, by a processor, related information which is related to specific business description information based on information indicating edited content of the specific business description information and business description information different from the specific business description information among the currently edited business description information pieces; and presenting the generated information.

Abstract: A device determines an additional component added to a second model, and a modification component modified in a third model, wherein a condition includes a wild card, and a first model are described in a module, the first model serving as a model when the module is applied to a model satisfying the condition and including a variable in which a string in a model satisfying the condition is stored, and the third model is a model when the module is applied to the second model satisfying the condition and where a string in the second model, which corresponds to the wild card, is stored in a variable, based on a word in a fourth model, and a word in the additional component and the modification component, calculates a degree regarding numbers of the word in the third model and the fourth, and creates a tag corresponding to the module.

Abstract: A method for reconfiguration of a database, the method includes extracting a plurality of entity candidates based on a plurality of attribute names and first information about a relation degree between the attribute name and the entity, first identifying a plurality of entity candidate groups in which each is comprised of extracted the plurality of entity candidates and has a correspondence relation with all the attribute names and the number of the entity candidates is minimum, second identifying the entity candidate group, in which a total of relation degrees between the entities in the entity candidate group is maximum, based on second information about the relation degree between the entities, and assigning each of the plurality of attribute names to either one of the plurality of entity candidates so as to have the correspondence relation of the attribute name and the entity candidate based on the first information.

Abstract: A consensus building support method includes: acquiring information related to one or more elements that affect a plurality of evaluations for each of a plurality of candidates for an alternative plan which includes at least one element and corresponds to a target of a consensus building; calculating, by a computer, an influence degree of the plurality of evaluations for each of the one or more elements based on the plurality of evaluations and the information; and creating corrected candidates by adding or removing a selected element which is selected based on the influence degree for each of the one or more elements to or from one of the plurality of candidates.

Abstract: An information processing method that is executed by a processor included in an information processing device, the information processing method includes obtaining request histories that respectively correspond to a plurality of operations, each of the plurality of operations including a plurality of works and each of the plurality of operations being executed based on a work request; extracting one or more request side works related to a request side of each of the plurality of operations, from among the plurality of works, based on the request histories; and generating process definition indicating definition of formalization of a process included in the one or more request side works.

Abstract: A computer analyzes a statement written in a natural language to determine the types and structure of phrases that constitute the statement. A memory stores a plurality of translation rules for different types of phrases, each rule describing how a phrase is to be translated into graphical symbol datasets and relationship link datasets. Each graphical symbol dataset specifies a graphical symbol having a content label representing its content. The content label is or includes a word contained in the pertinent phrase. Each relationship link dataset specifies a relationship link that represents a relationship between graphical symbols and has a particular end shape to indicate what type of relationship it is. The computer translates each phrase of the statement into two or more graphical symbol datasets and one or more relationship link datasets, according to the translation rules pertinent to the determined phrase types.

Abstract: In a tool selection method, a computer refers to information of work models each associating an attribute of a work tool with a pair of an object character string related to a work object and an activity character string including verbs related to work activities, the information being stored in a memory, detects a pair of an object character string and an activity character string from an e-mail, specifies a work model corresponding to the detected pair, and specifies a work tool having an attribute corresponding to the specified work model.

Abstract: A device determines an additional component added to a second model, and a modification component modified in a third model, wherein a condition includes a wild card, and a first model are described in a module, the first model serving as a model when the module is applied to a model satisfying the condition and including a variable in which a string in a model satisfying the condition is stored, and the third model is a model when the module is applied to the second model satisfying the condition and where a string in the second model, which corresponds to the wild card, is stored in a variable, based on a word in a fourth model, and a word in the additional component and the modification component, calculates a degree regarding numbers of the word in the third model and the fourth, and creates a tag corresponding to the module.

Abstract: A computer analyzes a statement written in a natural language to determine the types and structure of phrases that constitute the statement. A memory stores a plurality of translation rules for different types of phrases, each rule describing how a phrase is to be translated into graphical symbol datasets and relationship link datasets. Each graphical symbol dataset specifies a graphical symbol having a content label representing its content. The content label is or includes a word contained in the pertinent phrase. Each relationship link dataset specifies a relationship link that represents a relationship between graphical symbols and has a particular end shape to indicate what type of relationship it is. The computer translates each phrase of the statement into two or more graphical symbol datasets and one or more relationship link datasets, according to the translation rules pertinent to the determined phrase types.