Abstract: Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model.

Abstract: Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model.

Abstract: A method for selecting an analysis procedure for a value series, including displaying a value series on a computer display monitor, receiving one or more sequences of user provided annotations, where the annotations overlay at least a sub-interval of the value series on the computer display monitor, using the sequences of user provided annotations to select an optimal value series analysis method from a set of value series analysis methods, where selecting an optimal value series analysis method includes determining parameter values for the optimal value series analysis method, and presenting the selected optimal value series analysis method and parameters, and the optimal reconstruction of the annotation sequences to the user.

Abstract: A method for accelerating time series data base queries includes segmenting an original time series of signal values into non-overlapping chunks, where a time-scale for each of the chunks is much less than the time scale of the entire time series, representing time series signal values in each chunk as a weighted superposition of atoms that are members of a shape dictionary to create a compressed time series, storing the original time series and the compressed time series into a database, determining whether a query is answerable using the compressed time series or the original time series, and whether answering the query using the compressed time series is faster. If answering the query is faster using the compressed representation, the query is executed on weight coefficients of the compressed time series to produce a query result, and the query result is translated back into an uncompressed representation.

Abstract: A predictive analysis generates a predictive model (Padj(Y|X)) based on two separate pieces of information, a set of original training data (Dorig), and a “true” distribution of indicators (Ptrue(X)). The predictive analysis begins by generating a base model distribution (Pgen(Y|X)) from the original training data set (Dorig) containing tuples (x,y) of indicators (x) and corresponding labels (y). Using the “true” distribution (Ptrue(X)) of indicators, a random data set (D?) of indicator records (x) is generated reflecting this “true” distribution (Ptrue(X)). Subsequently, the base model (Pgen(Y|X)) is applied to said random data set (D?), thus assigning a label (y) or a distribution of labels to each indicator record (x) in said random data set (D?) and generating an adjusted training set (Dadj). Finally, an adjusted predictive model (Padj(Y|X)) is trained based on said adjusted training set (Dadj).

Abstract: A method for accelerating time series data base queries includes segmenting an original time series of signal values into non-overlapping chunks, where a time-scale for each of the chunks is much less than the time scale of the entire time series, representing time series signal values in each chunk as a weighted superposition of atoms that are members of a shape dictionary to create a compressed time series, storing the original time series and the compressed time series into a database, determining whether a query is answerable using the compressed time series or the original time series, and whether answering the query using the compressed time series is faster. If answering the query is faster using the compressed representation, the query is executed on weight coefficients of the compressed time series to produce a query result, and the query result is translated back into an uncompressed representation.

Abstract: A predictive analysis generates a predictive model (Padj(Y|X)) based on two separate pieces of information, a set of original training data (Dorig), and a “true” distribution of indicators (Ptrue(X)). The predictive analysis begins by generating a base model distribution (Pgen(Y|X)) from the original training data set (Dorig) containing tuples (x,y) of indicators (x) and corresponding labels (y). Using the “true” distribution (Ptrue(X)) of indicators, a random data set (D?) of indicator records (x) is generated reflecting this “true” distribution (Ptrue(X)). Subsequently, the base model (Pgen(Y|X)) is applied to said random data set (D?), thus assigning a label (y) or a distribution of labels to each indicator record (x) in said random data set (D?) and generating an adjusted training set (Dadj). Finally, an adjusted predictive model (Padj(Y|X)) is trained based on said adjusted training set (Dadj).

Abstract: A first data mining model and a second data mining model are compared. A first data mining model M1 represents results of a first data mining task on a first data set D1 and provides a set of first prediction values. A second data mining model M2 represents results of a second data mining task on a second data set D2 and provides a set of second prediction values. A relation R is determined between said sets of prediction values. For at least a first record of an input data set, a first and second probability distribution is created based on the first and second data mining models applied to the first record. A distance measure d is calculated for said first record using the first and second probability distributions and the relation. At least one region of interest is determined based on said distance measure d.