International Business Machines Corporation

Hewlett-packard Development Company, L.p.

Dimitrios Gunopulos

Rakesh Agrawal

Roberto Javier Bayardo

Dieter Helmut Roller

Frank Leymann

Ramakrishnan Srikant

Prabhakar Raghavan

Ching-Tien Ho

John Christopher Shafer

Paul Bao-Luo Chou

Sunita Sarawagi

Pasumarti Venkata Kamesam

Edna Grossman

Johannes Ernst Gehrke

Vana Kalogeraki

A mechanism for information retrieval in fully decentralized, distributed, peer-to-peer network systems. Peer profiles are aggregated and collected in real-time by each peer. Each peer uses and integrates knowledge that it collects during query-reply cycles for each future query received, thereby learning over time and making information retrieval a more intelligent and rapid process. Each peer then autonomously decides which of its peers are most likely to have an answer to a given query. A routine is provided for monitoring the messages and profiling each peer, building a local peer profile for each node exchanging messages in the peer-to-peer network based on messages passing through the node.

Distributed information retrieval in peer-to-peer networks

A user can easily organize computerized document folders by associating a few sample documents in the document database with each folder. The present invention learns folder profiles based on the sample documents and moves the remaining documents into the folders accordingly. In this way, the user can construct new folders, or rearrange existing folders, or cause the computer to automatically rearrange and maintain the folders. This is particularly useful for managing a database of perhaps thousands of emails.

System and method for organizing repositories of semi-structured documents such as email

A dense data-set mining system and method is provided that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint, known as minimum gap, which prunes any rule having conditions that do not contribute to its predictive accuracy. The method maintains efficiency even at low supports on data that is dense in the sense that many items appear with high frequency (e.g. relational data).

System and method for constraint-based rule mining in large, dense data-sets

Prospective customers may be selected for target making with respect to a particular product and market without the need to conduct designed marketing campaigns. A computer implemented process applies data mining techniques to databases containing records representing customer and overall market populations for the purpose of selecting market segments and prospective customers for targeted marketing.

Prospective customer selection using customer and market reference data

A computer-implemented method, apparatus, and article of manufacture that constructs graph models from logs of past, unstructured executions of the given process. The graph model so produced conforms to the dependencies and past executions present in the log. By providing graph models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing processes.

Generating process models from workflow logs

A method for finding clusters of units in high-dimensional data having the steps of determining dense units in selected subspaces within a data space of the high-dimensional data, determining each cluster of dense units that are connected to other dense units in the selected subspaces within the data space, determining maximal regions covering each cluster of connected dense units, determining a minimal cover for each cluster of connected dense units, and identifying the minimal cover for each cluster of connected dense units.

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Riverside, CA, United States of America

Dimitrios Gunopulos