International Business Machines Corporation

Philip Shilung Yu

Charu Chandra Aggarwal

Haixun Wang

Joel Leonard Wolf

Wei Fan

Kun-Lung Wu

Michail Vlachos

Xiaohui Gu

Paul Andrew Moskowitz

Bugra Gedik

Ming-Syan Chen

Spyridon Papadimitriou

Peter Kenneth Malkin

Vittorio R Castelli

Yuan-Chi Chang

Thomas David Erickson

John Thomas Richards

Stephen Joy Boies

Jimeng Sun

John Joseph Turek

David Perry Greene

Wendy Anne Kellogg

Samuel Dinkin

Yun Chi

Sriram Kolinjivadi Padmanabhan

Zon-Yin Shae

Daniel Manuel Dias

George Andrei Mihaila

Zhen Wen

Ioana Roxana Stanoi

Jason Benjamin Ellis

William Grey

Feifei Li

Hao He

Mark Richard Laff

Edith H Stern

Barry Edward Willner

Chung-Sheng Li

John Richard Smith

Balakrishna Raghavendra Iyer

Bishwaranjan Bhattacharjee

Hui Lei

Charles Philippe Tresser

Rajesh Ramkrishna Bordawekar

Deepak Srinivas Turaga

Kenneth Alan Goldman

Asit Dan

Milind R Naphade

Min Wang

Henrique Andrade

Hui-I Hsiao

James R Giles

Lisa Amini

Mark Steven Squillante

Chang-Shing Perng

John Steven Langford

Kirsten Weale Hildrum

Rachel K E Bellamy

Gerald Lebizay

Shyh-Kwei Chen

Dilip Dinkar Kandlur

Matthew Leon Hill

Brent Tzion Hailpern

Deepak Rajan

Jian Yin

Naoki Abe

Rohit Wagle

Tracee L Wolf

Shu-Ping Chang

Gang Luo

Yiming Ye

Murray Scott Campbell

Yeong-Chang L Lien

Matthias Gruetzner

Chidanand V Apte

Sam Dinkin

Xifeng Yan

Gee-Gwo Mei

Shu Lin

Jamshid Abdollahi Vayghan

Rachel Ke Bellamy

Carl E Abrams

Matthew A Hilbert

Kathleen M Troidle

Deepak Srinivac Turaga

Deepak Srinivao Turaga

Chung-Shen Li

Hadas Shachnai

Dina Thomas

Mark (Gee-Gwo) Mei

Jayachandran Sethuraman

A method (and system) of assigning a sales opportunity, includes creating an assignment model based on clustering historical sales opportunities, and providing a scoring mechanism on a plurality of sales agents for automatically optimizing an assignment of at least one sales opportunity to at least one of the plurality of sales agents.

Method and system for automatic assignment of sales opportunities to human agents

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.

Method and apparatus for adaptive in-operator load shedding

Techniques for similarity searching are provided. Structural data in a database is searched against one or more structural queries. A desired minimum degree of similarity between the one or more queries and the structural data in the database is first specified. One or more indices are then used to exclude from consideration any structural data in the database that does not share the minimum degree of similarity with one or more of the queries.

System and method for efficiently performing similarity searches of structural data

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.

Confidential fraud detection system and method

Interoperability is enabled between participants in a network by determining values associated with a value metric defined for at least a portion of the network. Information flow is directed between two or more of the participants based at least in part on semantic models corresponding to the participants and on the values associated with the value metric. The semantic models may define interactions between the participants and define at least a portion of information produced or consumed by the participants. The determination of the values and the direction of the information flow may be performed multiple times in order to modify the one or more value metrics. The direction of information flow may allow participants to be deleted from the network, may allow participants to be added to the network, or may allow behavior of the participants to be modified.

Enabling interoperability between participants in a network

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.

Systems and methods for metadata embedding in streaming medical data

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.

Resource adaptive spectrum estimation of streaming data

Systems and methods for parallel stream item counting are disclosed. A data stream is partitioned into portions and the portions are assigned to a plurality of processing cores. A sequential kernel is executed at each processing core to compute a local count for items in an assigned portion of the data stream for that processing core. The counts are aggregated for all the processing cores to determine a final count for the items in the data stream. A frequency-aware counting method (FCM) for data streams includes dynamically capturing relative frequency phases of items from a data stream and placing the items in a sketch structure using a plurality of hash functions where a number of hash functions is based on the frequency phase of the item. A zero-frequency table is provided to reduce errors due to absent items.

System and method for analyzing streams and counting stream items on multi-core processors

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.

System and method for classifying data streams with very large cardinality

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. As the level of confidence is raised, a sub-ensemble that has more models will be selected and as the level of confidence is lowered, a sub-ensemble that has fewer models will be selected. Finally, the invention applies the selected sub-ensemble, in place of the ensemble, to an example to make a prediction.

System and method for adaptive pruning

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.

Systems and methods for optimal component composition in a stream processing system

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.

System and method for resource adaptive classification of data streams

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.

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Chappaqua, NY, United States of America

Philip Shilung Yu