Abstract: Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.

Abstract: Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.

Abstract: Techniques for perturbing an evolving data stream are provided. The evolving data stream is received. An online linear transformation is applied to received values of the evolving data stream generating a plurality of transform coefficients. A plurality of significant transform coefficients are selected from the plurality of transform coefficients. Noise is embedded into each of the plurality of significant transform coefficients, thereby perturbing the evolving data stream. A total noise variance does not exceed a defined noise variance threshold.

Abstract: A computer-implemented method, system, and a computer readable article of manufacture identify local patterns in at least one time series data stream. A data stream is received that comprises at least one set of time series data. The at least one set of time series data is formed into a set of multiple ordered levels of time series data. Multiple ordered levels of hierarchical approximation functions are generated directly from the multiple ordered levels of time series data. A set of approximating functions are created for each level. A current window with a current window length is selected from a set of varying window lengths. The set of approximating functions created at one level in the multiple ordered levels is passed to a subsequent level as a set of time series data. The multiple ordered levels of hierarchical approximation functions are stored into memory after being generated.

Abstract: System and method for modeling a content-based network. The method includes finding single mode clusters from among network (sender and recipient) and content dimensions represented as a tensor data structure. The method allows for derivation of useful cross-mode clusters (interpretable patterns) that reveal key relationships among user communities and keyword concepts for presentation to users in a meaningful and intuitive way. Additionally, the derivation of useful cross-mode clusters is facilitated by constructing a reduced low-dimensional representation of the content-based network. Moreover, the invention may be enhanced for modeling and analyzing the time evolution of social communication networks and the content related to such networks. To this end, a set of non-overlapping or possibly overlapping time-based windows is constructed and the analysis performed at each successive time interval.

Abstract: Disclosed is a method, information processing system, and computer readable medium for preserving privacy of nonstationary data streams. The method includes receiving at least one nonstationary data stream with time dependent data. Calculating, for a given instant of sub-space of time, A set of first-moment statistical values is calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.

Abstract: A method, information processing system, and computer readable medium are provided for preserving privacy of one-dimensional nonstationary data streams. The method includes receiving a one-dimensional nonstationary data stream. A set of first-moment statistical values are calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.

Abstract: A system and method for using continuous failure predictions for proactive failure management in distributed cluster systems includes a sampling subsystem configured to continuously monitor and collect operation states of different system components. An analysis subsystem is configured to build classification models to perform on-line failure predictions. A failure prevention subsystem is configured to take preventive actions on failing components based on failure warnings generated by the analysis subsystem.

Abstract: A computer-implemented method, system, and a computer readable article of manufacture identify local patterns in at least one time series data stream. A data stream is received that comprises at least one set of time series data. The at least one set of time series data is formed into a set of multiple ordered levels of time series data. Multiple ordered levels of hierarchical approximation functions are generated directly from the multiple ordered levels of time series data. A set of approximating functions are created for each level. A current window with a current window length is selected from a set of varying window lengths. The set of approximating functions created at one level in the multiple ordered levels is passed to a subsequent level as a set of time series data. The multiple ordered levels of hierarchical approximation functions are stored into memory after being generated.

Abstract: Techniques for perturbing an evolving data stream are provided. The evolving data stream is received. An online linear transformation is applied to received values of the evolving data stream generating a plurality of transform coefficients. A plurality of significant transform coefficients are selected from the plurality of transform coefficients. Noise is embedded into each of the plurality of significant transform coefficients, thereby perturbing the evolving data stream. A total noise variance does not exceed a defined noise variance threshold.

Abstract: In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.

Abstract: An exemplary method for computing correlation anomaly scores, including, defining a first similarity matrix for a target run of data, the target run of data includes an N number of sensors, defining a second similarity matrix for a reference run of data, the target run of data includes the N number of sensors, developing a k-neighborhood graph Ni of the i-th node for the target run of data, wherein the k-neighborhood graph of the i-th node is defined as a graph comprising the i-th node and its k-nearest neighbors (NN), developing a k-neighborhood graph Ni of the i-th node for the reference run of data, defining a probability distribution p(j|i), wherein p(j|i) is the probability that the j-th node becomes one of the k-NN of the i-th node, coupling the probability between the i-th node and the neighbors of the i-th node, determining an anomaly score of the i-th node, and determining whether the target run of data has changed from the reference run of data responsive to determining the anomaly score of the i-th

Abstract: A system and method for using continuous failure predictions for proactive failure management in distributed cluster systems includes a sampling subsystem configured to continuously monitor and collect operation states of different system components. An analysis subsystem is configured to build classification models to perform on-line failure predictions. A failure prevention subsystem is configured to take preventive actions on failing components based on failure warnings generated by the analysis subsystem.

Abstract: Disclosed is a method, information processing system, and computer readable medium for preserving privacy of nonstationary data streams. The method includes receiving at least one nonstationary data stream with time dependent data. Calculating, for a given instant of sub-space of time, A set of first-moment statistical values is calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.

Abstract: A method, information processing system, and computer readable medium are provided for preserving privacy of one-dimensional nonstationary data streams. The method includes receiving a one-dimensional nonstationary data stream. A set of first-moment statistical values are calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.

Abstract: In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.

Abstract: Systems and methods for reordering dimensions of a multiple-dimensional dataset includes ordering dimensions of multi-dimensional dataset such that original D dimensions of the data are reordered to obtain a smooth sequence representation which includes placement of the D dimensions with similar behavior at adjacent positions in an ordered sequence representation. The ordered sequence representation is segmented into groups of K<D dimensions for placement in a K-dimensional indexing structure.

Abstract: A method, system, and computer readable medium for identifying local patterns in at least one time series data stream are disclosed. The method comprises generating multiple ordered levels of hierarchal approximation functions. The multiple ordered levels are generated directly from at least one given time series data stream including at least one set of time series data. The hierarchical approximation functions for each level of the multiple levels is based upon creating a set of approximating functions. The hierarchical approximation functions are also based upon selecting a current window with a current window length from a set of varying window lengths. The current window is selected for a current level of the multiple levels.