Abstract: Different virtual labels, for example, like +1 and ?1, are assigned to two data sets. A change analysis problem for the two data sets is reduced to a supervised learning problem by using the virtual labels. Specifically, a classifier such as logical regression, decision tree and SVM is prepared and is trained by use of a data set obtained by merging the two data sets assigned the virtual labels. A feature selection function of the resultant classifier is used to rank and output both every attribute contributing to classification and its contribution rate.

Abstract: A method and system is provided for assigning programs in a workflow to one or more nodes for execution. Prior to the assignment, a priority of execution of each program is calculated in relation to its dependency upon data received and transmitted data. Based upon the calculated priority and the state of each of the nodes, the programs in the workflow are dynamically assigned to one or more nodes for execution. In addition to the node assignment based upon priority, preemptive execution of the programs in the workflow is determined so that the programs in the workflow may not preemptively be executed at a selected node in response to the determination.

Abstract: Different virtual labels, for example, like +1 and ?1, are assigned to two data sets. A change analysis problem for the two data sets is reduced to a supervised learning problem by using the virtual labels. Specifically, a classifier such as logical regression, decision tree and SVM is prepared and is trained by use of a data set obtained by merging the two data sets assigned the virtual labels. A feature selection function of the resultant classifier is used to rank and output both every attribute contributing to classification and its contribution rate.

Abstract: A method and system is provided for assigning programs in a workflow to one or more nodes for execution. Prior to the assignment, a priority of execution of each program is calculated in relation to its dependency upon data received and transmitted data. Based upon the calculated priority and the state of each of the nodes, the programs in the workflow are dynamically assigned to one or more nodes for execution. In addition to the node assignment based upon priority, preemptive execution of the programs in the workflow is determined so that the programs in the workflow may not preemptively be executed at a selected node in response to the determination.

Abstract: A method and system is provided for assigning programs in a workflow to one or more nodes for execution. Prior to the assignment, a priority of execution of each program is calculated in relation to its dependency upon data received and transmitted data. Based upon the calculated priority and the state of each of the nodes, the programs in the workflow are dynamically assigned to one or more nodes for execution. In addition to the node assignment based upon priority, preemptive execution of the programs in the workflow is determined so that the programs in the workflow may not preemptively be executed at a selected node in response to the determination.

Abstract: A spatial data mining apparatus for calculating an optimal distance from a database, wherein spatial information, such as addresses, is stored, includes an input mechanism, for the input of an objective function required for the optimization of a distance, an intermediate table generator 30, for employing in the database starting point data and query point data for calculating the distances between each starting point and each query point and for generating an intermediate table, and an optimal distance calculator for calculating a distance based on the intermediate table generated by the intermediate table generator, in order to optimize the value of the objective function that is entered by the input mechanism.

Abstract: A spatial data mining apparatus for calculating an optimal distance from a database, wherein spatial information, such as addresses, is stored, includes an input mechanism, for the input of an objective function required for the optimization of a distance, an intermediate table generator 30, for employing in the database starting point data and query point data for calculating the distances between each starting point and each query point and for generating an intermediate table, and an optimal distance calculator for calculating a distance based on the intermediate table generated by the intermediate table generator, in order to optimize the value of the objective function that is entered by the input mechanism.