Abstract: To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning section 102 learns the occurrence probability distribution of time-series data 111 as a first statistical model (for example, a latent Markov model) defined by a finite number of variables including a latent variable. In the subsequent processing, the degree of a temporal change in the probability distribution is computed for each of the probability distribution of the entire first statistical model, its partial probability distribution (the probability distribution of the latent variable and conditional probability distribution contingent on the value of the latent variable), and the probability distribution in which the above plural probability distributions are linearly-combined with a weight. The change-point of the time-series data 111 is detected on the basis of the computed degree of the change.

Abstract: A network fault detection apparatus includes: data distribution learning units (2, 3, 4, and 5) that take, as input, data in which the state of the network is expressed by matrix variables of a hierarchical structure and that learn the state of the network as the probability distribution of the matrix variables, and fault detection units (6 and 7) that, based on the result of learning by the data distribution learning unit, detect, as a network fault, a state in which the probability distribution transitions from a distribution that indicates the normal state of the network to a distribution that indicates another state.

Abstract: To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning section 102 learns the occurrence probability distribution of time-series data 111 as a first statistical model (for example, a latent Markov model) defined by a finite number of variables including a latent variable. In the subsequent processing, the degree of a temporal change in the probability distribution is computed for each of the probability distribution of the entire first statistical model, its partial probability distribution (the probability distribution of the latent variable and conditional probability distribution contingent on the value of the latent variable), and the probability distribution in which the above plural probability distributions are linearly-combined with a weight. The change-point of the time-series data 111 is detected on the basis of the computed degree of the change.

Abstract: A model detection apparatus comprises a number of estimate parameter memories for storing mutually different distribution estimate parameters representing occurrences of input data. A number of distribution estimators are respectively associated with the parameter memories for producing distribution estimate parameters from data stored in the associated parameter memories and from a series of input data, and updating the associated parameter memories with the produced parameters. A model series memory stores candidate models corresponding in number to the parameter memories. A model series estimator produces candidate models using the series of input data, the stored distribution estimate parameters and the stored candidate models, and updates the model series memory with the produced candidate models. An optimal model series calculator calculates an optimal series of models from the candidate models stored in the model series memory.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.

Abstract: An outlier detection device for detecting abnormal data in a data set an outlier rule preservation unit, a filtering unit, a degree of outlier calculation unit, a sampling unit, and a supervised learning unit. The supervised learning unit generates a new rule characterizing abnormal data by supervised learning based on a set of the respective data to which a label indicating whether data is abnormal data or not is applied. The new rule is added to the set of rules held in the outlier rule preservation unit to update the rules. The filtering unit uses the rules for determining whether each data of the data set is abnormal or not. The abnormal data is compared to normal data for calculating a degree of abnormality by the degree of outlier calculation unit.

Abstract: For sequentially input data string, the outliner and the change point are detected through calculation of the outlier score and the change point score by combining a time-series model learning device to learn the generation mechanism of the read data series as the time-series statistic model, a score calculator to calculate the outlier score of each data based on the time-series model parameter and the input data, a moving average calculator to calculate the moving average of the outlier score, a time-series model learning device to learn the generation mechanism of the moving average series as the time-series statistic model and the above score calculator that further calculates the outlier score of the moving average based on the moving average of the outlier score and outputs the result as the change point score of the original data.

Abstract: Degree of outlier of one input data is calculated by an amount of change in a learned probability density from that before learning as a result of taking in of the input data. This is because data largely differing in a tendency from a so far learned probability density function can be considered to have a high degree of outlier. More specifically, a function of a distance between probability densities before and after data input is calculated as a degree of outlier. Accordingly, a probability density estimation device appropriately estimates a probability distribution of generation of unfair data while sequentially reading a large volume of data and a score calculation device calculates and outputs a degree of outlier of each data based on the estimated probability distribution.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.

Abstract: By integrating at a center and analyzing not only data accumulated in a single site but also data accumulated in distribution in a plurality of sites, macro information can be obtained. An aggregation unit of a site device of a plurality of remote sites extracts information contained in accumulated data to generate aggregated information. An aggregated information integration unit of a center device generates integrated aggregated information by integrating aggregated information which is received from each site and an approximate information generation unit generates approximate information which reproduces the contents of data of all the remote sites. Then, an analysis unit generates macro information based on the approximate information. This arrangement enables macro information as the whole system to be generated to high precision. In addition, the volume of communication between the site and the center at the time of data analysis can be reduced to ensure privacy protection.

Abstract: A time-series model learning unit reads in time-series data sequentially, learns the parameters of a time-series model and stores the parameters in a storage device. A loss function calculating unit reads in sequentially from storage each item of the input time-series data one at a time and calculates values of a loss function. A complexity calculating unit sums the loss values to sequentially calculate complexity as fitting error resulting when a time-series model is fit to the input data. Complexity is stored. A change-point searching unit reads in complexity regarding time-series data before and after change-point candidates from storage with respect to all change-point candidates, compares the sum of the complexities with complexity regarding all time-series data in a case where a change point is assumed to be absent, calculates the change-point score based upon the difference between the compared values, and detects and outputs the change point.

Abstract: A topic analyzing method is provided in which the number of main topics in text data which is added in time series and generation and disappearance of topics are identified in real time as needed, and features of main topics are extracted and thereby one can know a change in the content of a topic with a minimum amount of memory and processing time. There is provided a system that detects topics while sequentially reading text data in a situation where the text data is added in time series, including learning means for representing a topic generation model by a mixture distribution model and learning the topic generation model online while more-heavily discounting the older data on the basis of a timestamp of the data; and model selecting means for selecting an optimal topic generation model from among a plurality of candidate topic generation models on the basis of information criteria of the topic generation models, wherein the topics are detected as mixture components of the optimal generation model.

Abstract: A model detection apparatus comprises a number of estimate parameter memories for storing mutually different distribution estimate parameters representing occurrences of input data. A number of distribution estimators are respectively associated with the parameter memories for producing distribution estimate parameters from data stored in the associated parameter memories and from a series of input data, and updating the associated parameter memories with the produced parameters. A model series memory stores candidate models corresponding in number to the parameter memories. A model series estimator produces candidate models using the series of input data, the stored distribution estimate parameters and the stored candidate models, and updates the model series memory with the produced candidate models. An optimal model series calculator calculates an optimal series of models from the candidate models stored in the model series memory.

Abstract: An object of the present invention is to analyze questionnaire replies automatically from questionnaire reply statements including free reply description in natural language collected through a network, by using a text classification engine, and to issue the analysis results of the claimant as the knowledge in the rule formats. Questionnaire respondents send questionnaire reply statements from respondent computers. Questionnaire reply statements are accumulated in a database through a network. A text classification engine reads out accumulated questionnaire reply statements from the database, and learns the rules for classifying the questionnaire reply statements, and issues them to the claimant.

Abstract: A time-series model learning unit reads in the time-series data sequentially, learns the parameters of a time-series model and stores the learned parameters in a complexity and parameter storage device. A loss function calculating unit reads in sequentially from the storage device with regard to each item of the input time-series data one at a time and calculates the values of a loss function. A complexity calculating unit sums the loss values to sequentially calculate complexity as fitting error that results when a time-series model is fit to the input time-series data. Complexity is stored in the storage device.

Abstract: Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.