Agents for Integrating Distributed Data for Complex Computations

Algorithms for many complex computations assume that all the relevant data is available on a single node of a computer network. In the emerging distributed and networked knowledge environments, databases relevant for computations may reside on a number of nodes connected by a communication network. These data resources cannot be moved to other network sites due to privacy, security, and size considerations. The desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In this paper, we present a methodology wherein each distributed data source is represented by an agent. Each such agent has the capability to decompose global computations into local parts, for itself and for agents at other sites. The global computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network

Sharing Learned Models among Remote Database Partitions by Local Meta-learning

We explore the possibility of importing "blackbox " models learned over data sources at remote sites to improve models learned over locally available data sources. In this way, we may be able to learn more accurate knowledge from globally available data than would otherwise be possible from partial, locally available data. Proposed meta-learning strategies in our previous work are extended to integrate local and remote models. We also investigate the effect on accuracy performance when data overlap among different sites. Introduction Much of the research in inductive learning concentrates on problems with relatively small amounts of data residing at one location. With the coming age of very large network computing, it is likely that orders of magnitude more data in databases at various sites will be available for various learning problems of real world importance. Frequently, local databases represent only a partial view of all the data globally available. For example, in detecting cr..

Knowledge integration in distributed data mining

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