Abstract: In an image provision apparatus (100), a decomposition unit (111) decomposes image data into pieces of unit image data (70). A storing unit (112) stores, in a memory unit (130), image management information (131) including each piece of unit image data (70) of the pieces of unit image data and position information. An acquisition unit (121) accepts a provision request (52). The provision request (52) includes range information representing a range of a partial image in the image. The acquisition unit (121) acquires a unit image data set (711) representing unit images each including at least part of the partial image from the image management information (131), based on the range information and the position information. A generation unit (122) generates the partial image (63) based on the unit image data set (711).

Abstract: In an image provision apparatus (100), a decomposition unit (111) decomposes image data into pieces of unit image data (70). A storing unit (112) stores, in a memory unit (130), image management information (131) including each piece of unit image data (70) of the pieces of unit image data and position information. An acquisition unit (121) accepts a provision request (52). The provision request (52) includes range information representing a range of a partial image in the image. The acquisition unit (121) acquires a unit image data set (711) representing unit images each including at least part of the partial image from the image management information (131), based on the range information and the position information. A generation unit (122) generates the partial image (63) based on the unit image data set (711).

Abstract: The present invention is provided with a command acceptance unit (110) to accept a query command (201) to acquire data from a database, the query command including a value designation range to designate a range of a definition value corresponding to the data to be acquired, an acquisition definition storage unit (160) to store acquisition definition information (161) wherein a value definition range is associated with an acquisition method to acquire data from the database, and a command generation unit (120) to judge whether a range that overlaps with the value definition range exists in the value designation range, and when it is judged that the overlapping range exists, to generate a data acquisition command (202) to acquire data corresponding to a definition value included in the overlapping range in an acquisition method corresponding to the value definition range.

Abstract: The present invention is provided with a command acceptance unit (110) to accept a query command (201) to acquire data from a database, the query command including a value designation range to designate a range of a definition value corresponding to the data to be acquired, an acquisition definition storage unit (160) to store acquisition definition information (161) wherein a value definition range is associated with an acquisition method to acquire data from the database, and a command generation unit (120) to judge whether a range that overlaps with the value definition range exists in the value designation range, and when it is judged that the overlapping range exists, to generate a data acquisition command (202) to acquire data corresponding to a definition value included in the overlapping range in an acquisition method corresponding to the value definition range.

Abstract: In a document classification device 100, a sample document extraction condition storage unit 160 stores sample document extraction conditions 160-1 set for each of classification categories for extracting partial text according to the classification categories from an input document 301 input by a document input unit 110. A document matching unit 120 matches the input document 301 against the sample document extraction conditions 160-1. Based on a result of matching by the document matching unit 120, a document extraction unit 130 extracts the partial text from the input document 301 according to the classification categories. A learning unit 140 performs predetermined machine learning using as a sample document the partial text extracted by the document extraction unit 120, and thereby generates classification rules 150-1.

Abstract: A data receiving part receives data transmitted from a plurality of measuring devices. A time determining part distinguishes management time from date and time of the data. The record generating part stores data of the same management time which are received during a predetermined acceptance time, in the same record. When un-arrived data belonging to the same management time exists at the end of the acceptance time, arrival of the un-arrived data is waited for during a predetermined wait time. Even if the data is arrived with some delay, arrival-delayed data can be stored in the same record. Thus, the missing data occurrence frequency can be suppressed.

Abstract: A prediction error calculation part calculates a prediction error for each input data. A prediction error encoding part generates a prediction error code by encoding the value of the prediction error. A run-length counting part counts the run-length of the prediction error. When the value of the prediction error changes, a run-length encoding part generates a run-length code by encoding the run-length counted. A code connecting part generates a connected code by connecting the run-length code to the prediction error code of a corresponding prediction error. When the value of the prediction error is a particular value, a prediction error checking part selects a connected code for the prediction error, as an output code. When the value of the prediction error is a different value, the prediction error checking part selects a prediction error code for the prediction error, as an output code. A code output part outputs the output code selected.

Abstract: In a document classification device 100, a sample document extraction condition storage unit 160 stores sample document extraction conditions 160-1 set for each of classification categories for extracting partial text according to the classification categories from an input document 301 input by a document input unit 110. A document matching unit 120 matches the input document 301 against the sample document extraction conditions 160-1. Based on a result of matching by the document matching unit 120, a document extraction unit 130 extracts the partial text from the input document 301 according to the classification categories. A learning unit 140 performs predetermined machine learning using as a sample document the partial text extracted by the document extraction unit 120, and thereby generates classification rules 150-1.

Abstract: A string matching unit 110 specifies a category of an input document 801 by string matching of the input document 801 and a classifying keyword shown by matching condition information 109. Learning data 209 shows statistic information of each category. A classifying unit 220 specifies the category of the input document 801 based on a correspondence ratio of the input document 801 and the statistic information shown by the learning data 209. A correct answer comparing unit 120 compares the category specified by the string matching unit 110 and a category of correct answer information 803. A learning unit 210 compares the category specified by the classifying unit 220 and the category of the correct answer information 803. An error rate calculating unit 310 calculates a classifying error rate of a string matching filter unit 100 and a learning filter unit 200 based on the comparison result of the correct answer comparing unit 120 and the comparison result of the learning unit 210.

Abstract: A data receiving part receives data transmitted from a plurality of measuring devices. A time determining part distinguishes management time from date and time of the data. The record generating part stores data of the same management time which are received during a predetermined acceptance time, in the same record. When un-arrived data belonging to the same management time exists at the end of the acceptance time, arrival of the un-arrived data is waited for during a predetermined wait time. Even if the data is arrived with some delay, arrival-delayed data can be stored in the same record. Thus, the missing data occurrence frequency can be suppressed.

Abstract: A prediction error calculation part calculates a prediction error for each input data. A prediction error encoding part generates a prediction error code by encoding the value of the prediction error. A run-length counting part counts the run-length of the prediction error. When the value of the prediction error changes, a run-length encoding part generates a run-length code by encoding the run-length counted. A code connecting part generates a connected code by connecting the run-length code to the prediction error code of a corresponding prediction error. When the value of the prediction error is a particular value, a prediction error checking part selects a connected code for the prediction error, as an output code. When the value of the prediction error is a different value, the prediction error checking part selects a prediction error code for the prediction error, as an output code. A code output part outputs the output code selected.

Abstract: A behavior index indicates a development of occurrence of events included in log data by a connection of a plurality of nodes, and includes a branching node connecting to two or more subsequent nodes and an integrating node connected to two or more preceding nodes. When a regular expression converting part inputs a search condition indicating an event appearance order from a client, a search automaton retaining part generates a state transition table in accordance with the search condition. While a node type determining part determines the node types of the respective nodes of the behavior index, a state evaluating part analyzes the respective nodes to determine the states of the respective nodes, and determines whether or not a state transition pattern matches the state transition table. Regarding a branching node, the respective nodes of each of branching routes branching from the branching node are analyzed.

Abstract: A string matching system comprises a state transition table generator for generating a state transition table based on a matching condition described in a regular expression, and an automaton for including a state that makes a transition according to the state transition table generated by the state transition table generator; if, in the state transition table generated based on the matching condition, there exists no next transition destination state with respect to a current-state and input-characters tuple, the automaton makes a transition to the initial state without proceeding to read input characters.

Abstract: When an instance targeted for tracking is divided or replicated to generate a new instance, ready and continuous tracking of a history of an event related to the original instance and the new instance is allowed. A behavior ID is assigned to an event for each instance, and an occurrence order of the event is managed for each instance. When a new instance has been generated from an instance due to a certain event, a new behavior ID is assigned to the new instance, with respect to that event. Then, the new identifier and an identifier assigned to the instance that is a generation source for the event are associated.

Abstract: A string matching unit 110 specifies a category of an input document 801 by string matching of the input document 801 and a classifying keyword shown by matching condition information 109. Learning data 209 shows statistic information of each category. A classifying unit 220 specifies the category of the input document 801 based on a correspondence ratio of the input document 801 and the statistic information shown by the learning data 209. A correct answer comparing unit 120 compares the category specified by the string matching unit 110 and a category of correct answer information 803. A learning unit 210 compares the category specified by the classifying unit 220 and the category of the correct answer information 803. An error rate calculating unit 310 calculates a classifying error rate of a string matching filter unit 100 and a learning filter unit 200 based on the comparison result of the correct answer comparing unit 120 and the comparison result of the learning unit 210.

Abstract: A string matching system comprises a state transition table generator for generating a state transition table based on a matching condition described in a regular expression, and an automaton for including a state that makes a transition according to the state transition table generated by the state transition table generator; if, in the state transition table generated based on the matching condition, there exists no next transition destination state with respect to a current-state and input-characters tuple, the automaton makes a transition to the initial state without proceeding to read input characters.

Abstract: A data management apparatus and method provide for a virtual conversion by repeatedly reading at least one record from an input file having a plurality of records, each record including a plurality of fields, and adding the at least one record to a buffer. The records in the buffer are then converted into a post-conversion block on a field-by-field basis until data size of post-conversion blocks for each kind of field of the records in the buffer exceeds a threshold size. The number of records in the post-conversion blocks can be determined by counting the records in the buffer during a process immediately preceding a time when the data size exceeds the threshold size, and storing that number. The conversion is completed by reading out records of the stored number of records from the input file, converting the records read out into post-conversion blocks on a field-by-field basis, and storing the post-conversion blocks.

Abstract: A file management system performs flexible and efficient file management with a transposed file. First, variable-length logical fields constituting the records in an original file are converted to variable-length internal fields with field-to-field correspondence being maintained and fields aligned with word boundaries. Next, the records consisting of internal fields are divided into multiple record groups which may comprise a variable number of records. For each record group, one or a plurality of internal fields of all records are then sorted into blocks and blocks are set in a row as a group. Groups thus generated from all records are assembled into a transposed file. When requested to retrieve field data from the original file, the file management system accesses the transposed file and reads only blocks comprising target field data.

Abstract: The invention prevents data transfer in a computer network from being sent via a path incapable of transfer or of poor data transfer efficiency. A central processing unit, a main memory unit, and storage devices for storing data and data processing devices are connected in the network, and the data is transferred mutually between the storage devices and the data processing devices without passing through the central processing unit or the main memory unit. The apparatus includes a data transfer control module that identifies the capability/incapability of data transfer based on the physical positions of the storage devices, the physical positions of the data processing devices, and the transfer paths incapable of data transfer. The volume management module specifies a combination of storage devices capable of data transfer based on the physical positions of the storage devices, the physical positions of data processing devices, and the transfer paths capable of data transfer.

Abstract: A file managing method includes subdividing an original file including records, each record including fields, for storing blocks, each block including fields subdivided from more than one of the records; coupling the blocks to into groups of blocks; rearranging the groups of blocks, and, after rearranging the groups of blocks, coupling the groups of blocks to each other to produce a transposed file; and accessing the fields from the transposed file, at random.