Abstract: One embodiment of the present method and apparatus adaptive in-operator load shedding includes receiving at least two data streams (each comprising a plurality of tuples, or data items) into respective sliding windows of memory. A throttling fraction is then calculated based on input rates associated with the data streams and on currently available processing resources. Tuples are then selected for processing from the data streams in accordance with the throttling fraction, where the selected tuples represent a subset of all tuples contained within the sliding window.

Abstract: Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

Abstract: Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

Abstract: Systems and methods for embedding metadata such as personal patient information within actual medical data signals obtained from a patient are provided wherein two watermarks, a robust watermark and a fragile watermark are embedded in a given medical data signal. The robust watermark includes a binary coded representation of the metadata that is incorporated into the frequency domain of the medical data signal using discrete Fourier transformations and additive embedding. Error correcting code can also be added to the binary representation of the metadata using Hamming coding. A given robust watermark can be incorporated multiple times in the medical data signal. The fragile watermark is added on top of the modified medical signal containing the robust watermark in the spatial domain of the modified medical signal. The fragile watermark utilizes hash function to generate random sequences that are incorporated through the medical data signal.

Abstract: One embodiment of the present method and apparatus adaptive in-operator load shedding includes receiving at least two data streams (each comprising a plurality of tuples, or data items) into respective sliding windows of memory. A throttling fraction is then calculated based on input rates associated with the data streams and on currently available processing resources. Tuples are then selected for processing from the data streams in accordance with the throttling fraction, where the selected tuples represent a subset of all tuples contained within the sliding window.

Abstract: Streaming environments typically dictate incomplete or approximate algorithm execution, in order to cope with sudden surges in the data rate. Such limitations are even more accentuated in mobile environments (such as sensor networks) where computational and memory resources are typically limited. Introduced herein is a novel “resource adaptive” algorithm for spectrum and periodicity estimation on a continuous stream of data. The formulation is based on the derivation of a closed-form incremental computation of the spectrum, augmented by an intelligent load-shedding scheme that can adapt to available CPU resources. Experimentation indicates that the proposed technique can be a viable and resource efficient solution for real-time spectrum estimation.

Abstract: One embodiment of the present method and apparatus adaptive in-operator load shedding includes receiving at least two data streams (each comprising a plurality of tuples, or data items) into respective sliding windows of memory. A throttling fraction is then calculated based on input rates associated with the data streams and on currently available processing resources. Tuples are then selected for processing from the data streams in accordance with the throttling fraction, where the selected tuples represent a subset of all tuples contained within the sliding window.

Abstract: An object and attributes that describe that object are identified. The attributes are grouped into attribute patterns, and classification classes are identified. For each identified class a sketch table containing a plurality of parallel hash tables is created. For the object to be classified, each attribute pattern is processed using the all of the hash functions for each sketch table, resulting in a plurality of values under each sketch table for a single attribute pattern. The lowest value is selected for each sketch table. The distribution of values across all sketch tables is evaluated for each attribute pattern, producing a discriminatory power for each attribute pattern. Attribute patterns having a discriminatory power above a given threshold are selected and added to the associated sketch table values. The sketch table with the largest overall sum is identified, and the associated class is assigned to the object belonging to the attribute patterns.

Abstract: Systems and methods for embedding metadata such as personal patient information within actual medical data signals obtained from a patient are provided wherein two watermarks, a robust watermark and a fragile watermark are embedded in a given medical data signal. The robust watermark includes a binary coded representation of the metadata that is incorporated into the frequency domain of the medical data signal using discrete Fourier transformations and additive embedding. Error correcting code can also be added to the binary representation of the metadata using Hamming coding. A given robust watermark can be incorporated multiple times in the medical data signal. The fragile watermark is added on top of the modified medical signal containing the robust watermark in the spatial domain of the modified medical signal. The fragile watermark utilizes hash function to generate random sequences that are incorporated through the medical data signal.

Abstract: Disclosed in a method and structure for searching data in databases using an ensemble of models. First the invention performs training. This training orders models within the ensemble in order of prediction accuracy and joins different numbers of models together to form sub-ensembles. The models are joined together in the sub-ensemble in the order of prediction accuracy. Next in the training process, the invention calculates confidence values of each of the sub-ensembles. The confidence is a measure of how closely results form the sub-ensemble will match results from the ensemble. The size of each of the sub-ensembles is variable depending upon the level of confidence, while, to the contrary, the size of the ensemble is fixed. After the training, the invention can make a prediction. First, the invention selects a sub-ensemble that meets a given level of confidence.

Abstract: A system and method are provided for optimizing component composition in a distributed stream-processing environment having a plurality of nodes capable of being associated with one or more of a plurality of stream processing components. The system includes an adaptive composition probing (ACP) module and a hierarchical state manager. The ACP module probes a subset of the plurality of stream processing components to determine the optimal component composition in response to a stream processing request. The hierarchical state manager manages local and global information for use by said ACP module in determining the optimal component composition.

Abstract: Systems and methods for embedding metadata such as personal patient information within actual medical data signals obtained from a patient are provided wherein two watermarks, a robust watermark and a fragile watermark are embedded in a given medical data signal. The robust watermark includes a binary coded representation of the metadata that is incorporated into the frequency domain of the medical data signal using discrete Fourier transformations and additive embedding. Error correcting code can also be added to the binary representation of the metadata using Hamming coding. A given robust watermark can be incorporated multiple times in the medical data signal. The fragile watermark is added on top of the modified medical signal containing the robust watermark in the spatial domain of the modified medical signal. The fragile watermark utilizes hash function to generate random sequences that are incorporated through the medical data signal.

Abstract: An object and attributes that describe that object are identified. The attributes are grouped into attribute patterns, and classification classes are identified. For each identified class a sketch table containing a plurality of parallel hash tables is created. For the object to be classified, each attribute pattern is processed using the all of the hash functions for each sketch table, resulting in a plurality of values under each sketch table for a single attribute pattern. The lowest value is selected for each sketch table. The distribution of values across all sketch tables is evaluated for each attribute pattern, producing a discriminatory power for each attribute pattern. Attribute patterns having a discriminatory power above a given threshold are selected and added to the associated sketch table values. The sketch table with the largest overall sum is identified, and the associated class is assigned to the object belonging to the attribute patterns.

Abstract: One embodiment of the present method and apparatus adaptive load shedding includes receiving at least one data stream (comprising a plurality of tuples, or data items) into a first sliding window of memory. A subset of tuples from the received data stream is then selected for processing in accordance with at least one data stream operation, such as a data stream join operation. Tuples that are not selected for processing are ignored. The number of tuples selected and the specific tuples selected depend at least in part on a variety of dynamic parameters, including the rate at which the data stream (and any other processed data streams) is received, time delays associated with the received data stream, a direction of a join operation performed on the data stream and the values of the individual tuples with respect to an expected output.

Abstract: A system and method for resource adaptive classification of data streams. Embodiments of systems and methods provide classifying data received in a computer, including discretizing the received data, constructing an intermediate data structure from said received data as training instances, performing subspace sampling on said received data as test instances and adaptively classifying said received data based on statistics of said subspace sampling.

Abstract: An object and attributes that describe that object are identified. The attributes are grouped into attribute patterns, and classification classes are identified. For each identified class a sketch table containing a plurality of parallel hash tables is created. For the object to be classified, each attribute pattern is processed using the all of the hash functions for each sketch table, resulting in a plurality of values under each sketch table for a single attribute pattern. The lowest value is selected for each sketch table. The distribution of values across all sketch tables is evaluated for each attribute pattern, producing a discriminatory power for each attribute pattern. Attribute patterns having a discriminatory power above a given threshold are selected and added to associated sketch table values. The sketch table with the largest overall sum is identified, and the associated class is assigned to the object belonging to the attribute patterns.

Abstract: One embodiment of the present method and apparatus adaptive load shedding includes receiving at least one data stream (comprising a plurality of tuples, or data items) into a first sliding window of memory. A subset of tuples from the received data stream is then selected for processing in accordance with at least one data stream operation, such as a data stream join operation. Tuples that are not selected for processing are ignored. The number of tuples selected and the specific tuples selected depend at least in part on a variety of dynamic parameters, including the rate at which the data stream (and any other processed data streams) is received, time delays associated with the received data stream, a direction of a join operation performed on the data stream and the values of the individual tuples with respect to an expected output.

Abstract: Introduced herein is a “resource adaptive” algorithm for spectrum and periodicity estimation on a continuous stream of data. The formulation is based on the derivation of a closed-form incremental computation of the spectrum, augmented by a load-shedding scheme that can adapt to available CPU resources to provide a resource efficient solution for real time spectrum estimation.

Abstract: Uncertain data is classified by constructing an error adjusted probability density estimate for the data, and applying a subspace exploration process to the probability density estimate to classify the data.

Abstract: Load shedding schemes for mining data streams. A scoring function is used to rank the importance of stream elements, and those elements with high importance are investigated. In the context of not knowing the exact feature values of a data stream, the use of a Markov model is proposed herein for predicting the feature distribution of a data stream. Based on the predicted feature distribution, one can make classification decisions to maximize the expected benefits. In addition, there is proposed herein the employment of a quality of decision (QoD) metric to measure the level of uncertainty in decisions and to guide load shedding. A load shedding scheme such as presented herein assigns available resources to multiple data streams to maximize the quality of classification decisions. Furthermore, such a load shedding scheme is able to learn and adapt to changing data characteristics in the data streams.