Abstract: A method for predicting a change-point in continuous time-series data by computer includes accepting an input of continuous time-series data in past w periods; and determining whether or not a change-point in time-series appears in future k periods; wherein determining further includes determining, by a plurality of preliminary classifiers on the basis of the continuous time-series data in the past w periods and their respective preliminary classification rules, whether or not the change-point appears in future (k+i) periods, where i=0, 1, . . . , m; and determining, by a secondary classifier on the basis of determinations made by the plurality of preliminary classifiers and a secondary classification rule, whether or not the change-point appears in future c periods.

Abstract: A method for predicting a change-point in continuous time-series data by computer includes accepting an input of continuous time-series data in past w periods; and determining whether or not a change-point in time-series appears in future k periods; wherein determining further includes determining, by a plurality of preliminary classifiers on the basis of the continuous time-series data in the past w periods and their respective preliminary classification rules, whether or not the change-point appears in future (k+i) periods, where i=0, 1, . . . , m; and determining, by a secondary classifier on the basis of determinations made by the plurality of preliminary classifiers and a secondary classification rule, whether or not the change-point appears in future c periods.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Determining a cache hit ratio of a caching device analytically and precisely.

Abstract: Determining a cache hit ratio of a caching device analytically and precisely.

Abstract: 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.

Abstract: 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.