A BELIEF-DRIVEN DISCOVERY FRAMEWORK BASED ON DATA MONITORING AND TRIGGERING

A new knowledge-discovery framework, called Data Monitoring and Discovery Triggering (DMDT), is defined, where the user specifies monitors that âwatch" for significant changes to the data and changes to the user-defined system of beliefs. Once these changes are detected, knowledge discovery processes, in the form of data mining queries, are triggered. The proposed framework is the result of an observation, made in the previous work of the authors, that when changes to the user-defined beliefs occur, this means that, there are interesting patterns in the data. In this paper, we present an approach for finding these interesting patterns using data monitoring and belief-driven discovery techniques. Our approach is especially useful in those applications where data changes rapidly with time, as in some of the On-Line Transaction Processing (OLTP) systems. The proposed approach integrates active databases, data mining queries and subjective measures of interestingness based on user-defined systems of beliefs in a novel and synergetic way to yield a new type of data mining systems.Information Systems Working Papers Serie

Decomposability and Its Role in Parallel Logic-Program Evaluation

This paper is concerned with the issue of parallel evaluation of logic programs. We define the concept of program decomposability, which means that the load of evaluation can be partitioned among a number of processors, without a need for communication among them. This in turn results in a very significant speed-up of the evaluation process. Some programs are decomposable, whereas others are not. We completely syntactically characterize three classes of single rule programs with respect to decomposability: nonrecursive, simple linear, and simple chain programs. We also establish two sufficient conditions for decomposability

Partitioning vs. Replication for Token-Based Commodity

The proliferation of e-commerce has enabled a new set of applications that allow globally distributed purchasing of commodities such as books, CDs, travel tickets, etc., over the Internet. These commodities can be represented on line by tokens, which can be distributed among servers to enhance the performance and availability of such applications. There are two main approaches for distributing such tokens ? replication and partitioning. Token replication requires expensive distributed synchronization protocols to provide data consistency, and is subject to both high latency and blocking in case of network partitions. On the other hand, token partitioning allows many transactions to execute locally without any global synchronization, which results in low latency and immunity against network partitions. In this paper, we examine the Data-Value Partitioning (DVP) approach to token-based commodity distribution. We propose novel DVP strategies that vary in the way they redistribute tokens among the servers of the system. Using a detailed simulation model and real Internet message traces, we investigate the performance of our DVP strategies by comparing them against a previously proposed scheme, Generalized Site Escrow (GSE), which is based on replication and escrow transactions. Our experiments demonstrate that, for the types of applications and environment we address, replication-based approaches are neither necessary nor desirable, as they inherently require quorum synchronization to maintain consistency. We show that DVP, primarily due to its ability to provide high server autonomy, performs favorably in all cases studied. (Also cross-referenced as UMIACS-TR-2000-6