R. Grossman, S. Bailey, A. Ramu, B. Malhi and A. Turinsky, The preliminary design of Papyrus: A system for high performance, distributed data mining over clusters. In: Advances in Distributed and Parallel Knowledge Discovery, AAAI Press/The MIT Press, 2000: 259-275.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....[9] is a documentanalysistoolworking on a distributed environment, based on cooperative agents. It works without any relational database underneath. Instead, there are PADMA agents that perform several relational operations with the information extracted from the documents. The Papyrus system [6] is able to mine distributed data sources on a WAN network scenario. Papyrus system uses meta clusters to generate local models which are exchanged to generate a global model. The idea is founded on a theory similar to JAM system, nevertheless they use a model representation language (PMML [5] ....

R. L. Grossman, S. Kasif, D. Mon, A. Ramu, and B. Malhi. The preliminary design of Papyrus: A system for high performance, distributed data mining over clusters, meta-clusters and super-clusters. In ########### ## ### ###### ######## ## ########### #### ######. AAAI Press, 1998.

.... of distributed data mining [6] and the closely related area of multi agent learning [14; 15] There are several projects underway on large scale distributed data mining: some examples are the Kensington project [2] for mining enterprise data distributed across the internet, the Papyrus project [4] for providing a high performance networking and computing testbed for mining on the internet, the JAM project [12] for developing a java agent based meta learning framework for distributed mining and the BODHI project [7] for doing collective data mining with stress on learning from vertically ....

R. L. Grossman, S. Kasif, D. Mon, A. Ramu, and B. Malhi. The preliminary design of papyrus: A system for high performance, distributed data mining over clusters, meta-clusters and super-clusters. In Kargupta et al. [6]. http://www.lac.uic.edu/~grossman/cv/ dataspace-background.htm.

....storage, access, and analysis. The ability of various organizations to collect, store, and retrieve huge amounts of data has necessitated the development of algorithms that can extract useful information from these databases. KDD addresses this issue. Distributed knowledge discovery (DKD) [10, 13, 20, 22, 25, 32, 37, 50, 55] takes KDD to a new platform. It embraces the growing trend of merging computation with communication and explores all facets of the KDD process in the context of the emerging distributed computing environments. DKD accepts the fact that data may be inherently distributed among di erent loosely ....

R. Grossman, S. Bailey, S. Kasif, D. Mon, A. Ramu, and B. Malhi. The preliminary design of papyrus: A system for high performance, distributed data mining over clusters, metaclusters and super-clusters. Fourth International Conference of Knowledge Discovery and Data Mining, New York, New York, Pages 37-43, 1998.

....achieved this goal through the implementation and demonstration of a system we call JAM (Java Agents for Meta Learning) To our knowledge, JAM is the first system to date that employs meta learning as a means to mine distributed databases. A commercial system based upon JAM has recently appeared [26]. JAM, however, is more than an implementation of a distributed meta learning system. It is a distributed data mining system addressing many practical problems for which centralized or host based systems are not appropriate. On the other hand, distributed systems have increased complexity. Their ....

R. Grossman, S. Baily, S. Kasif, D. Mon, and A. Ramu. The preliminary design of papyrus: A system for high performance. In P. Chan H. Kargupta, editor, Work. Notes KDD-98 Workshop on Distributed Data Mining, pages 37--43. AAAI Press, 1998.

....message communication (s is the number of data sites) for determining whether a candidate set is large. The architecture of a distributed data mining system plays an important role in its performance. Architectural requirements for ecient data communication in a wide area network are explored in [13]. This work also reports several tools like a persistent object manager called PTool, a modeling language called Predictive Model Markup Language (PMML) a model manager called Anubis, and an object transportation layer named Bast to facilitate the local data mining and wide area combining ....

R. et al. Grossman. The preliminary design of papyrus: A system for high performance, distributed data mining over clusters, meta-clusters, and super-clusters. In Advances in Distributed and Parallel Knowledge Discovery, page Not available. AAAI/MIT Press, 1999.

....learners observe data sequences generated according to the identical target distribution, while the second model applies when the data sequences may not have the identical target distribution over all agent learners. Papyrus, another system in development by the National Center for Data Mining (Grossman, Bailey, Kasif, Mon, Ramu, Malhi, 1998), is a hierarchical organization of the nodes within a data mining framework. The intent of this project is to develop a distributed data mining system that reflects the current distribution of the data across multiple sites, and the existing network configurations connecting these configurations. ....

Grossman, R., Bailey, S., Kasif, S., Mon, D., Ramu, A., & Malhi, B. (1998). The preliminary design of papyrus: A system for high performance, distributed data mining over clusters, meta-clusters and superclusters.

....than the original data, thus allowing economical communication and enhancing scalability. The authors report on a PADMA implementation for unstructured text mining but note that the architecture is not domain specific. Papyrus, a system in development by the National Center for Data Mining (Grossman, Bailey, Kasif, Mon, Ramu, Malhi, 1998), is a hierarchical organization of the nodes within a data mining framework. The intent of this project is to develop a distributed data mining system that reflects the current distribution of the data across multiple sites, and the existing network configurations connecting these configurations. ....

Grossman, R., Bailey, S., Kasif, S., Mon, D., Ramu, A., & Malhi, B. (1998). The preliminary design of papyrus: A system for high performance, distributed data mining over clusters, meta-clusters and superclusters.

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R. L. Grossman, S. Bailey, A. Ramu, B. Malhi and A. Turinsky, The Preliminary Design of Papyrus: A System for High Performance, Distributed Data Mining over Clusters, in Advances in Distributed and 12 Parallel Knowledge Discovery, H. Kargupta and P. Chan, editors, AAAI Press/The MIT Press, Menlo Park, California, 2000, pages 259-275.

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R. L. Grossman, S. Kasif, D. Mon, A. Ramu and B. Malhi, The Preliminary Design of Papyrus: A System for High Performance, Distributed Data Mining over Clusters, Meta-Clusters and Super-Clusters, Proceedings of the KDD-98 Workshop on Distributed Data Mining, AAAI, to appear.

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R. L. Grossman, S. Bailey, S. Kasif, D. Mon, A. Ramu and B. Malhi, The Preliminary Design of Papyrus: A System for High Performance, Distributed Data Mining over Clusters, Meta-Clusters and SuperClusters, Proceedings of the Workshop on Distributed Data Mining, The Fourth International Conference on Knowledge Discovery and Data Mining New York City, August 27-31, 1998, to appear.

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R. L. Grossman, S. Kasif, D. Mon, A. Ramu and B. Malhi, The Preliminary Design of Papyrus: A System for High Performance, Distributed Data Mining over Clusters, Meta-Clusters and SuperClusters, Proceedings of the KDD-98 Workshop on Distributed Data Mining, AAAI, to appear.

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R. Grossman, S. Bailey, A. Ramu, B. Malhi and A. Turinsky, The preliminary design of Papyrus: A system for high performance, distributed data mining over clusters. In: Advances in Distributed and Parallel Knowledge Discovery, AAAI Press/The MIT Press, 2000: 259-275.

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R. Grossman, S. Bailey, A. Ramu, B. Malhi and A. Turinsky, The preliminary design of Papyrus: A system for high performance, distributed data mining over clusters. In: Advances in Distributed and Parallel Knowledge Discovery, AAAI Press/The MIT Press, 2000: 259-275.

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R. L. Grossman, S. Bailey, A. Ramu, B. Malhi, and A. Turinsky. The Preliminary Design of Papyrus: A System for High Performance, Distributed Data Mining over Clusters. In Hillol Kargupta and Philip Chan, editors, Advances in Distributed and Parallel Knowledge Discovery, pages 259--275. MIT/AAAI Press, Menlo Park, CA, 2000.

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R. Grossman, S. Bailey, S. Kasif, D. Mon, A. Ramu, and B. Malhi. The preliminary design of Papyrus: a system for high performance, distributed data mining over clusters, meta-clusters and super-clusters. In Proceddings of the International KDD 98 Conference, pages 37--43, 1998.

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R. Grossman, S. Bailey, S. Kasif, D. Mon, A. Ramu, and B. Malhi. The preliminary design of PAPYRUS: A system for high performance, distributed data mining over clusters, meta-clusters and super-clusters. Advances in Distributed and Parallel Knowledge Discovery, Eds: Hillol Kargupta and Philip Chan, AAAI/MIT Press, pages 259-276, 2000.

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