Abstract: Quasi-identifiers (QIDs) are detected in a dataset using a set of computing tasks. The dataset has a plurality of records and a set of attributes. An index is generated for the dataset. The index has an indicator for each attribute value of each record in the dataset. Each indicator specifies all the records in the dataset having the same value for the attribute. Each task is assigned an attribute combination and a subset of the plurality of records in the dataset and is passed to a thread for execution on computing resources. The executing task inspects the set of records specified by the index indicator for each attribute value in the attribute combination to produce a result. The result of at least one task identifies a unique record for the associated attribute combination. The attribute combination producing the unique record is a QID.

Abstract: Quasi-identifiers (QIDs) are detected in a dataset using a set of computing tasks. The dataset has a plurality of records and a set of attributes. An index is generated for the dataset. The index has an indicator for each attribute value of each record in the dataset. Each indicator specifies all the records in the dataset having the same value for the attribute. Each task is assigned an attribute combination and a subset of the plurality of records in the dataset and is passed to a thread for execution on computing resources. The executing task inspects the set of records specified by the index indicator for each attribute value in the attribute combination to produce a result. The result of at least one task identifies a unique record for the associated attribute combination. The attribute combination producing the unique record is a QID.

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: 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: As information becomes more accessible to the public, the ability to predict and estimate sensitive data from the data already available to the general public becomes easier. The existing privacy-preserving data mining approaches only consider the information the user is querying and do not consider the information the user already has, and how the user can use that information in combination with the query information to create sensitive data that the user should not have access to. Some embodiments of the present invention provide a query analysis (QA) program that solves the aforementioned problem by taking into account data that a user may already have, whether it is private data or data that is available to the public, and then using that data, along with the data that would be returned in the query, to determine if sensitive data could be recreated.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: As information becomes more accessible to the public, the ability to predict and estimate sensitive data from the data already available to the general public becomes easier. The existing privacy-preserving data mining approaches only consider the information the user is querying and do not consider the information the user already has, and how the user can use that information in combination with the query information to create sensitive data that the user should not have access to. Some embodiments of the present invention provide a query analysis (QA) program that solves the aforementioned problem by taking into account data that a user may already have, whether it is private data or data that is available to the public, and then using that data, along with the data that would be returned in the query, to determine if sensitive data could be recreated.

Abstract: Embodiments relate to managing resource consumption in a computing system. An aspect includes providing a resource policy by defining a plurality of threshold values relating to the resource consumption, wherein the resources are consumed by a plurality of user-defined functions performing tasks for a database management system, wherein the user-defined functions are executed by a plurality of processes external to the database management system. Another aspect includes performing an action, as defined by the resource policy, on at least one of the user-defined functions.

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 computer processor provides a set of datasets, including at least a first dataset, with each dataset of the set of datasets respectively being configured to allow the dataset to be presented according to multiple variations, with each variation being defined by a selection of at least one transformation. The computer processor receives customer feedback information relating to at least a first variation of the first dataset. The computer processor trains a first machine learning algorithm, based, at least in part, upon the customer feedback information. The computer processor performs, by the first machine learning algorithm, a marketing act. The marketing act includes at least one of the following: (i) defining a new variation of the first dataset, (ii) defining a new transformation for defining variations of the first dataset, (iii) recommending a predefined variation of the first dataset, and (iv) pricing a predefined variation of the first dataset.

Abstract: A computer processor provides a set of datasets, including at least a first dataset, with each dataset of the set of datasets respectively being configured to allow the dataset to be presented according to multiple variations, with each variation being defined by a selection of at least one transformation. The computer processor receives customer feedback information relating to at least a first variation of the first dataset. The computer processor trains a first machine learning algorithm, based, at least in part, upon the customer feedback information. The computer processor performs, by the first machine learning algorithm, a marketing act. The marketing act includes at least one of the following: (i) defining a new variation of the first dataset, (ii) defining a new transformation for defining variations of the first dataset, (iii) recommending a predefined variation of the first dataset, and (iv) pricing a predefined variation of the first dataset.

Abstract: A computer program product for creating models comprises a computer readable storage medium having stored thereon first program instructions executable by a processor to cause the processor to receive the modeling tasks each having a target variable and at least one covariate, the target variable and the at least one covariate being the same for all of the modeling tasks, a relationship between the target variable and the at least one covariate being different for all of the modeling tasks, and second program instructions executable by the processor to cause the processor to generate, for each of the modeling tasks, a model including a transfer function for approximating the relationship between the target value and the at least one covariate of the modeling task in a manner that at least two of the models share an identical transfer function and the models satisfy an accuracy condition.

Abstract: A method for generating models for a plurality of modeling tasks is disclosed. The method comprises receiving, with a processing device, the modeling tasks each having a target variable and at least one covariate. The target variable and at least one covariate are the same for all of the modeling tasks. A relationship between the target variable and at least one covariate is different for all of the modeling tasks. For each of the modeling tasks, generating a model including a transfer function for approximating the relationship between the target value and at least one covariate of the modeling task in a manner that at least two of the models share at least one identical transfer function and the models satisfy an accuracy condition.

Abstract: Accessing data in a database includes receiving, from a first user, a first query for a dataset stored in a database. A first set of patterns is provided in the dataset. For each pattern in the first set of patterns, a significance value is provided in response to the received first query. A set of tags is provided for flagging a pattern of the first set of patterns, the set of tags indicating at least two data categories describing the pattern. Input information received from the first user indicates tags of at least a first subset of patterns of the first set of patterns, wherein each tag of the tags is selected from the set of tags. The significance values of the first subset of patterns are adjusted based on the tags.

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 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.

Abstract: In general, the present disclosure describes techniques for detecting changes in demographic data of a customer based on energy consumption data of the customer. For example, a customer data management system receives energy consumption data of a customer and detects, based at least in part on the received energy consumption data of the customer, a change in demographic data associated with the customer. The customer data management system then outputs, based at least in part on the detecting, at least one demographic change report associated with the demographic data.