2033362
Bounding the cost of learned rules
Abstract In this article we approach one key aspect of the utility problem in explanation-based learning (EBL)—the expensive-rule problem —as an avoidable defect in the learning procedure. In particular, we examine the relationship between the cost of solving a problem without learning versus the cost of using a learned rule to provide the same solution, and refer to a learned rule as expensive if its use is more costly than the original problem solving from which it was learned. The key idea we explore is that expensiveness is inadvertently and unnecessarily introduced into learned rules by the learning algorithms themselves . This becomes a particularly powerful idea when combined with an analysis tool which identifies these hidden sources of expensiveness, and modifications of the learning algorithms which eliminate them. The result is learning algorithms for which the cost of learned rules is bounded by the cost of the problem solving that they replace.
11496281
A Transformational Analysis of the EBL Utility Problem
Efficiency is a major concern for all problem solving systems. One way of achieving efficiency is the application of learning techniques to speed up problem solving. Accordingly, there has been considerable amount of research on applying explanation-based learning (EBL)(Mitchell, Keller, & Kedar-Cabelli 1986) techniques to problem solving. However, EBL is known to suffer from the utility problem, where the cost of using the learned knowledge overwhelms its benefit. We show that how the cost increase of a learned rule in an EBL system can be analyzed by characterizing the learning process as a sequence of transformations from a problem solving episode to a learned rule. The analysis of how the cost changes through the transformations can be a useful tool for revealing the sources of cost increase in the learning system. We focus on the Soar problem solving system which uses a variant of EBL called chunking(Rosenbloom et al. 1991). The chunking process has been decomposed into a sequence of transformations from the problem solving to a chunk (learned rule). By analyzing these transformations, we have identified a set of sources which can make the output chunk expensive. The set of sources and the proposed solutions are :
14647313
Energy-efficient disk replacement and file placement techniques for mobile systems with hard disks
Mobile systems have usually used hard disks as the secondary storage devices because of their high capacity per cost and high I/O throughput. However, their high power consumption is the main limiting factor for extending their adoptions in mobile systems. In this paper, we propose enhanced file placement techniques for mobile platforms with multiple smaller disks (instead of a single large disk). We investigate that how many smaller disks are necessary to obtain energy saving while maintaining the required performance using both a simplified energy model and a realistic trace-based simulator under the proposed multiple disk configurations. We also propose energy-efficient file placement techniques, which aggregate files with common attributes the same set of disks. By skewing I/O operations, the proposed techniques achieve additional energy saving. Experimental results show that the proposed techniques can reduce the energy consumption by up to 43% when eight 1" disks are used instead of a single 2.5" disk with an acceptable increase in the average response time.
5155977
A Hybrid Keyword Search across Peer-to-Peer Federated Databases
The need for Keyword search in databases is suggested both by Web integration with legacy database management system and by dynamic Web publication. However, it sacrifices the inherent meaning of database schema. Web search engines provide clues for resource location on the Web, but have similar semantic problems. The Semantic Web suggests an ideal solution for the semantic problem on the Web. But due to the need for sophisticated domain definition and lack of unified definitions, many Web pages are not part of the Semantic Web. We define a hybrid search to be a search combining semantic metadata and keywords. A hybrid search on P2P based federated databases provides meaningful and scalable search on an overlay network across the Internet. This paper describes the design of the combined search for unstructured data with associated metadata, information retrieval from the repository, peer-to-peer based communication layer, and data integration hub.
27330844
Efficient processing of regular path joins using PID
Abstract XML is data that has no fixed structure. So it is hard to design a schema for storing and querying an XML data. Instead of a fixed schema, graph-based data models are widely adopted for querying XML. Queries on XML are based on paths in a data graph. A meaningful query usually has several paths in it, but much of recent research is more concerned with optimizing a single path in a query. In this paper, we present an efficient technique for processing multiple path expressions in a query. We implemented our technique and present preliminary performance results.
3492450
A framework for data prefetching using off-line training of Markovian predictors
An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from pure software approach by inserting prefetch instructions through program analysis to purely hardware mechanisms have been proposed. The degrees of success of those techniques are dependent on the nature of the applications. The need for innovative approach is rapidly growing with the introduction of applications such as object-oriented applications that show dynamically changing memory access behavior In this paper, we propose a novel framework for the use of data prefetchers that are trained off-line using smart learning algorithms to produce prediction models which captures hidden memory access patterns. Once built, those prediction models are loaded into a data prefetching unit in the CPU at the appropriate point during the runtime to drive the prefetching. On average by using table size of about 8KB size, we were able to achieve prediction accuracy of about 68% through our own proposed learning method and performance was boosted about 37% on average on the benchmarks we tested. Furthermore, we believe our proposed framework is amenable to other predictors and can be done as a phase of the profiling-optimizing-compiler.
2551048
Computers Are from Mars, Organisms Are from Venus
Biology and computer science share a natural affinity. Physicist Erwin Schrodinger envisioned life as an aperiodic crystal, observing that the organizing structure of life is neither completely regular, like a pure crystal, nor completely chaotic and without structure, like dust in the wind. Perhaps this is why biological information has never satisfactorily yielded to classical mathematical analysis. Machine computations combine elegant algorithms with brute-force calculations, which seems a reasonable approach to this aperiodic structure. Likewise, computing seeks to create a machine that can flexibly solve diverse problems. In nature, such plastic problem solving resides uniquely in the domain of organic matter. Thus, examining how organisms solve problems can lead to new computation and algorithm development approaches that devour the problems that are so easy to approach using a computer, yet so difficult to tackle in the laboratory. The paper discusses the two most successful uses of computers in biology, comparative sequence analysis and in silico cloning. It considers DNA computing and genetic algorithms.