Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph a plurality of data flows between one or more heterogeneous data sources relating to the query. An analytical flow is provided for one or more of the plurality of data flows for those of the one or more heterogeneous data sources that are undetected, and two or more of the one or more of the plurality of data flows are aggregated or disaggregated for the one or more heterogeneous data sources that are nested within the knowledge graph. One or more criteria is received from a user via an interactive graphical user interface (GUI) to use for defining the one or more optimal data flows.

Abstract: Embodiments for self-managed adaptable models for prediction systems by one or more processors. One or more adaptive models may be applied to data streams from a plurality of data sources according to one or more data recipes such that the one or more adaptive models predict a plurality of target variables.

Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph of heterogeneous data source relationships, a plurality of data flows between one or more heterogeneous data sources relating to the query, and an ontology of concepts and representing a domain knowledge of the one or more heterogeneous data sources.

Abstract: Embodiments for network reconstruction from message data by a processor. A digital map may be created using one or more messages of a plurality of vehicles obtained at a plurality of control points of a route network. The digital map may be analyzed to estimate a feasibility of simultaneous trajectories of the plurality of vehicles between selected locations in the route network.

Abstract: Embodiments for ensemble policy generation for prediction systems by a processor. Policies are generated and/or derived for a set of ensemble models to predict a plurality of target variables for streaming data such that the plurality of policies enables dynamic adjustment of the prediction system. One or more of the policies are updated according to one or more error states of the set of ensemble models.

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.