International Business Machines Corporation

Hisashi Kashima

Tsuyoshi Ide

Shohei Hido

Hiroaki Etoh

Ryo Hirade

Toshihiro Takahashi

Shoko Suzuki

Yuta Tsuboi

Akira Tajima

Kunikazu Yoda

Rikiya Takahashi

Hirofumi Matsuzawa

Tetsuo Shibuya

Teruo Koyanagi

Yasumasa Kajinaga

Kazutaka Yamasaki

Akihiro Inokuchi

Harunobu Kubo

Ysuyoshi Ide

A computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs.

Graph similarity calculation system, method and program

Different virtual labels, for example, like +1 and &#x2212;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.

Change analysis

Location estimation systems, methods, and non-transitory computer program products. The system includes: storage means provided in the computer, means for storing the vector datasets in the storage means of the computer, means for calculating the similarity between the vector dataset without any location label and each neighboring vector dataset with a location label, by using any one of a q-norm where 0&#x2266;q&#x2266;1 and an exponential attenuation function, and means for estimating the location label of the vector data without any location label from the calculated similarities.

Location estimation system, method and program

A location estimation method using label propagation. The achieved location estimation method is robust to variations in radio signal strengths and is highly accurate by using the q-norm (0&#x3c;q&#x3c;1), especially, for calculating the similarities among radio signal strength vectors. The accuracy in location estimation is further improved by putting more importance on the time-series similarities. Specifically, the time-series similarity is calculated by using time-series values indicating the temporal order of radio signal strengths during the measurement. If the time-series similarity is larger than the similarity between the radio signal strength vectors, the time-series similarity is preferentially used. The exponential attenuation function can also be used for calculating the similarities, instead of the q norm (0&#x3c;q&#x3c;1).

Methods, computing devices, and computer program products for regression from interval target values are provided. Training data having an interval output are read. An initial model is estimated. Representative values for the interval output are assigned using the initial model. A regression model is estimated using the representative values for the interval output. A determination is made whether the regression model converges. The step of assigning representative values for the interval output is iterated and the step of estimating the regression model using the representative values for the interval output iterated, in response to the regression model not converging. In response to the regression model converging, the regression model is output.

Method for regression from interval target values by alternating linear gaussian and expectation-maximization

A workforce analysis method for solving L-based clustering problem of multinomial distributions of workforce data includes acquiring workforce allocation data, arranging the workforce allocation data in sets of fraction data with respect to the Ldistances, clustering the sets of fraction data t corresponding set of cluster centers, or Ldistances for each set, minimizing the sets of fraction data based on the cluster centers or Ldistances and outputting analysis results of the clustering problem.

[object Object]

Provided is a system which supports a user's behavior by generating a behavioral decision function indicating behavior to be adopted to a certain target. The system includes: a data acquiring section which acquires a cost caused as a result of adopting each of a plurality of behaviors to a target as training data for generating the behavioral decision function, the plurality of behaviors having already been adopted to the target; and a function generator which generates, based on the training data, the behavioral decision function to minimize the expected shortfall of a cost to be obtained as a result of adopting the behavior to the target.

System for supporting user's behavior

Provided is a systemwhich supports a user's behavior by generating a behavioral decision function indicating behavior to be adopted to a certain target. The systemincludes: a data acquiring sectionwhich acquires a cost caused as a result of adopting each of a plurality of behaviors to a target as training data for generating the behavioral decision function, the plurality of behaviors having already been adopted to the target; and a function generatorwhich generates, based on the training data, the behavioral decision function to minimize the expected shortfall of a cost to be obtained as a result of adopting the behavior to the target.

A system such as a Web-based system in which a plurality of computers interact with each other is monitored to detect online an anomaly. Transactions of a service provided by each of a plurality of computers to another computer are collected, a matrix of correlations between nodes in the system is calculated from the transactions, and a feature vector representing anode activity balance is obtained from the matrix. The feature vector is monitored using a probability model to detect a transition to an anomalous state.

Anomaly detection

Provides an analysis system for analyzing dependencies among server programs in a computer system, comprising: a transaction detection unit for detecting transactions that is processing of a service which each of the plurality of server programs performs by being called from any other server program; a child transaction candidate detection unit for detecting candidates for a child transaction of each detected transaction, the child transaction being called in the transaction concerned; and a calling frequency calculation unit for outputting service calling frequencies obtained by estimating a frequency at which each server program allows each of the other server programs to perform a service by calling the other program, the estimation being made based on the service detected in association with each transaction and on a service detected in association with the candidate for the child transaction of the transaction.

Computer operation analysis

A system such as a Web-based system in which a plurality of computers interact with each other is monitored to detect online an anomaly. Transactions of a service provided by each of a plurality of computers to another computer are collected, a matrix of correlations between nodes in the system is calculated from the transactions, and a feature vector representing a node activity balance is obtained from the matrix. The feature vector is monitored using a probability model to detect a transition to an anomalous state.

Detects a condition for classification of data. Apparatus detects a set of some constituents as a factor of the classification. Apparatus has means for selecting a pattern which is a set of constituents; means of selecting a second pattern formed of the first pattern and at least one constituent added to the first pattern; means of generating an evaluation value for a measure of classification of the plurality of objects under a condition including the first pattern but not the second pattern on the basis of the number of objects satisfying the classification condition in the plurality of objects classified into the first group and the number of objects satisfying the classification condition in the objects classified into the second group; and means of outputting the first and second patterns as a factor of classification when the measure indicated by the evaluation value exceeds a reference measure.

Classification factor detection

A solution is provided for the problem of determining a successful bidder in an auction wherein bids are submitted for a great number of trading targets (products). Two restrictions are imposed on the combinations of products that are permitted: 1. Bids may be submitted for only one product type; and 2. The relationship existing among available combinations of products for which bids may be submitted must be based on a hierarchical structure. From a set of bids submitted under the above restrictions, a subset of optimum bids are selected by using dynamic programming.

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Yamato, Japan

Hisashi Kashima