Abstract: Methods and apparatus, including computer program products, implementing and using techniques for compressing data included in several transactions. Each transaction has at least one item. A unique identifier is assigned to each different item and, if taxonomy is defined, to each different taxonomy parent. Sets of transactions are formed from the several transactions. The sets of transactions are stored using a computer data structure including: a list of identifiers of different items in the set of transactions, information indicating number of identifiers in the list, and bit field information indicating presence of the different items in the set of transactions, said bit field information being organized in accordance with the list for facilitating evaluation of patterns with respect to the set of transactions. A data structure for compressing data included in a set of transactions is also provided.

Abstract: Method, apparatus, and system, including computer program products, implementing and using techniques for time series analysis, wherein the time series exhibit trend and/or seasonality. Model parameters of the time series data are determined by selecting and/or suggesting a most appropriate combination of trend and seasonality of the time series data based on analysis of variances of transformed time series data.

Abstract: Method, apparatus, and system, including computer program products, implementing and using techniques for determining a periodic cycle of time series data. A frequency spectrum of the data is provided. The frequency spectrum is processed using at least one of the following steps, resulting in a processed frequency spectrum: filtering the frequency spectrum for reducing noise of the data, truncating the frequency spectrum at low frequencies, and weighting high frequency contributions over low frequency contributions of the frequency spectrum. A periodic cycle is extracted based on the processed frequency spectrum.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for pattern detection in input data containing several transactions, each transaction having at least one item. Filter conditions for interesting patterns are received, and a first set of filter conditions applicable in connection with generation of candidate patterns is determined. An evaluated candidate pattern is selected as a parent candidate pattern, and evaluation information about the parent candidate pattern is maintained. Child candidate patterns are generated by extending the parent candidate pattern and taking into account the first set of filter conditions. The child candidate patterns are evaluated with respect to the input data together in sets of similar candidate patterns and based on the evaluation information about the parent candidate pattern. At least one child candidate pattern successfully passing the evaluation step is recursively used as a parent candidate pattern.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for finding deviations in data. A set of candidate patterns is generated. A set of exception patterns that occur in the data less frequently than expected assuming statistical independence is selected from the set of candidate patterns. Data records that comply with at least one of the exception patterns are processed as exception candidates.

Abstract: Method, apparatus, and system, including computer program products, implementing and using techniques for time series analysis, wherein the time series exhibit trend and/or seasonality. Model parameters of the time series data are determined by selecting and/or suggesting a most appropriate combination of trend and seasonality of the time series data based on analysis of variances of transformed time series data.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for finding deviations in data. A set of candidate patterns is generated. A set of exception patterns that occur in the data less frequently than expected assuming statistical independence is selected from the set of candidate patterns. Data records that comply with at least one of the exception patterns are processed as exception candidates.

Abstract: Method, apparatus, and system, including computer program products, implementing and using techniques for determining a periodic cycle of time series data. A frequency spectrum of the data is provided. The frequency spectrum is processed using at least one of the following steps, resulting in a processed frequency spectrum: filtering the frequency spectrum for reducing noise of the data, truncating the frequency spectrum at low frequencies, and weighting high frequency contributions over low frequency contributions of the frequency spectrum. A periodic cycle is extracted based on the processed frequency spectrum.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for compressing data included in several transactions. Each transaction has at least one item. A unique identifier is assigned to each different item and, if taxonomy is defined, to each different taxonomy parent. Sets of transactions are formed from the several transactions. The sets of transactions are stored using a computer data structure including: a list of identifiers of different items in the set of transactions, information indicating number of identifiers in the list, and bit field information indicating presence of the different items in the set of transactions, said bit field information being organized in accordance with the list for facilitating evaluation of patterns with respect to the set of transactions. A data structure for compressing data included in a set of transactions is also provided.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for pattern detection in input data containing several transactions, each transaction having at least one item. Filter conditions for interesting patterns are received, and a first set of filter conditions applicable in connection with generation of candidate patterns is determined. An evaluated candidate pattern is selected as a parent candidate pattern, and evaluation information about the parent candidate pattern is maintained. Child candidate patterns are generated by extending the parent candidate pattern and taking into account the first set of filter conditions. The child candidate patterns are evaluated with respect to the input data together in sets of similar candidate patterns and based on the evaluation information about the parent candidate pattern. At least one child candidate pattern successfully passing the evaluation step is recursively used as a parent candidate pattern.