Abstract: In a job management apparatus, a reception part receives an input of definition information that defines a running sequence and a running condition of a plurality of job steps. If an abnormal end occurs in a job step included in a job run by the apparatus in accordance with the definition information, the apparatus runs a retry job including a restarting step, which is the job step in which the abnormal end has occurred, and a job step posterior to the restarting step in the running sequence defined by the definition information and satisfying the running condition defined by the definition information at the occurrence of the abnormal end in the restarting step. The apparatus stops running the restarting step and the job step posterior to the restarting step in the running sequence defined by the definition information at least until the run of the retry job is completed.

Abstract: In a job management apparatus (200), a reception part (201) receives an input of definition information (111) that defines a running sequence and a running condition of a plurality of job steps. If an abnormal end occurs in a job step included in a job run by an execution part (202) in accordance with the definition information (111), a control part (203) makes the execution part (202) run a retry job (221) including a restarting step being the job step in which the abnormal end has occurred and a job step posterior to the restarting step in the running sequence defined by the definition information (111) and satisfying the running condition defined by the definition information (111) at the occurrence of the abnormal end in the restarting step. The control part (203) makes the execution part (202) stop running the restarting step and the job step posterior to the restarting step in the running sequence defined by the definition information (111) at least until the run of the retry job (221) is completed.

Abstract: A data compression apparatus of the invention includes a data acquisition unit to acquire n integers from encoding data, an integer division unit to divide each integer of the n integers into a second integer represented by low-order bits whose number of divided bits is b and a first integer represented by high-order bits obtained by excluding the low-order bits from each integer of the n integers and to output n first integers and n second integers, a first encoding unit to encode and output the n first integers as a first code represented by binary data having a number of bits that is a natural-number times the number of unit bits of L, and a second encoding unit to encode and output the n second integers as a second code.

Abstract: A data compression apparatus of the invention includes a data acquisition unit to acquire n integers from encoding data, an integer division unit to divide each integer of the n integers into a second integer represented by low-order bits whose number of divided bits is b and a first integer represented by high-order bits obtained by excluding the low-order bits from each integer of the n integers and to output n first integers and n second integers, a first encoding unit to encode and output the n first integers as a first code represented by binary data having a number of bits that is a natural-number times the number of unit bits of L, and a second encoding unit to encode and output the n second integers as a second code.

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 predicted value generating unit generates a predicted value (data predicted value) for original data intended to be encoded, based on a history of original data which is floating-point data. A data predicted value modifying unit adjusts a mantissa value of the data predicted value by aligning an exponent value of the data predicted value with an exponent value of the original data. A first residual generating unit generates a residual (first residual) between new original data and the data predicted value after being adjusted. A first residual predicted value generating unit generates a predicted value for the first residual (first residual predicted value), based on a history of first residuals. A second residual generating unit generates a residual (second residual) between the first residual and the first residual predicted value. A residual encoding unit generates encoded data by encoding the second residual.

Abstract: A data predicted value generating unit generates a predicted value (data predicted value) for original data intended to be encoded, based on a history of original data which is floating-point data. A data predicted value modifying unit adjusts a mantissa value of the data predicted value by aligning an exponent value of the data predicted value with an exponent value of the original data. A first residual generating unit generates a residual (first residual) between new original data and the data predicted value after being adjusted. A first residual predicted value generating unit generates a predicted value for the first residual (first residual predicted value), based on a history of first residuals. A second residual generating unit generates a residual (second residual) between the first residual and the first residual predicted value. A residual encoding unit generates encoded data by encoding the second residual.

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.