3446035
Resource-Bounded Searches in an Information Marketplace
Over the past five years, we have been developing a Web-based, resource-bounded, information-gathering agent-called BIG, for Bounded Information Gathering-to support a human decision process. Although its techniques are general enough to apply to a wide range of domains, BIG specifically helps clients pick software packages. For example, a client can instruct BIG to recommend a database package for Windows 98 and specify constraints on both the search process and the product characteristics. BIG will then formulate a plan, locate and extract relevant information from both structured and unstructured documents, and return a recommendation to the client, along with supporting data. Our focus is on a recently developed capability, namely, to schedule information-gathering activity in a way that controls the money spent on acquiring information from sites that charge a fee for access. This capability supports an information marketplace on the Web, which we feel will eventually supersede (but obviously not completely replace) the current model of free information access supported by advertising fees.
3393161
BIG: An agent for resource-bounded information gathering and decision making
Abstract The World Wide Web has become an invaluable information resource but the explosion of available information has made Web search a time consuming and complex process. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering control problem. This paper describes the rationale, architecture, and implementation of a next generation information gathering system—a system that integrates several areas of Artificial Intelligence research under a single umbrella. Our solution to the information explosion is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource trade-offs of different possible gathering approaches, extracts information from both unstructured and structured documents, and uses the extracted information to refine its search and processing activities.
18753637
Fast factorization architecture in soft-decision Reed-Solomon decoding
Reed-Solomon (RS) codes are among the most widely utilized block error-correcting codes in modern communication and computer systems. Compared to its hard-decision counterpart, soft-decision decoding offers considerably higher error-correcting capability. The recent development of soft-decision RS decoding algorithms makes their hardware implementations feasible. Among these algorithms, the Koetter-Vardy (KV) algorithm can achieve substantial coding gain for high-rate RS codes, while maintaining a polynomial complexity with respect to the code length. In the KV algorithm, the factorization step can consume a major part of the decoding latency. A novel architecture based on root-order prediction is proposed in this paper to speed up the factorization step. As a result, the time-consuming exhaustive-search-based root computation in each iteration level, except the first one, of the factorization step is circumvented with more than 99% probability. Using the proposed architecture, a speedup of 141% can be achieved over prior efforts for a (255, 239) RS code, while the area consumption is reduced to 31.4%.
14209049
Maintaining data warehouses over changing information sources
I n recent years, the number of digital information storage and retrieval systems has increased immensely. These information sources are generally interconnected via some network, and hence the task of integrating data from different sources to serve it up to users is an increasingly important one [10]. Applications that could benefit from this wealth of digital information are thus experiencing a pressing need for suitable integration tools that allow them to make effective use of such distributed and diverse data sets. In contrast to the on-demand approach to information integration, the approach of tailored information repository construction, commonly referred to as data warehousing, is characterized by the following properties:
3575990
Pruning dynamic slices with confidence
Given an incorrect value produced during a failed program run (e.g., a wrong output value or a value that causes the program to crash), the backward dynamic slice of the value very frequently captures the faulty code responsible for producing the incorrect value. Although the dynamic slice often contains only a small percentage of the statements executed during the failed program run, the dynamic slice can still be large and thus considerable effort may be required by the programmer to locate the faulty code.In this paper we develop a strategy for pruning the dynamic slice to identify a subset of statements in the dynamic slice that are likely responsible for producing the incorrect value. We observe that some of the statements used in computing the incorrect value may also have been involved in computing correct values (e.g., a value produced by a statement in the dynamic slice of the incorrect value may also have been used in computing a correct output value prior to the incorrect value). For each such executed statement in the dynamic slice, using the value profiles of the executed statements, we compute a confidence value ranging from 0 to 1 - a higher confidence value corresponds to greater likelihood that the execution of the statement produced a correct value. Given a failed run involving execution of a single error, we demonstrate that the pruning of a dynamic slice by excluding only the statements with the confidence value of 1 is highly effective in reducing the size of the dynamic slice while retaining the faulty code in the slice. Our experiments show that the number of distinct statements in a pruned dynamic slice are 1.79 to 190.57 times less than the full dynamic slice. Confidence values also prioritize the statements in the dynamic slice according to the likelihood of them being faulty. We show that examining the statements in the order of increasing confidence values is an effective strategy for reducing the effort of fault location.
18848996
Comparative approaches to equipment scheduling in high volume factories
Mail processing and distribution centers (P&DCs) are large factories that accept, sort, and sequence mail in preparation for delivery. A central problem in these facilities is how to schedule the equipment over the day to ensure batch production under tight equipment and workforce constraints. The problem falls into the general category of multi-level lot sizing and is notoriously difficult to solve. No exact algorithms exist that are efficient and can consistently provide high-quality solutions. In the paper, we investigate two specialized approaches for solving this problem. The first is a piece-by-piece LP-based heuristic and the second is a Benders decomposition. The heuristic uses the LP fractional solution as a target and attempts to find an integer solution that is as close to it as possible by minimizing the L"1-norm. The procedure consists of solving the LP relaxation of the original model and two reduced mixed-integer programs. The process is similar to what has been called 'piece-by-piece decomposition' in nonlinear programming. While the goal there is to find better initial starting points for the nonlinear code, our goal is to improve solution quality. Computational testing using data provided by the Dallas P&DC confirmed the superiority of the piece-by-piece LP-based heuristic with respect to branch and bound, which proved to be computational expensive, and the Benders algorithm which took several hours to find feasible solutions. In general, the LP-based heuristic was an order of magnitude faster than branch and bound and provided solutions that were able to process more than 99.75% of the daily volume.
1771938
Adaptively Scheduling Parallel Loops in Distributed Shared-Memory Systems
Using runtime information of load distributions and processor affinity, the authors propose an adaptive scheduling algorithm and its variations from different control mechanisms. The proposed algorithm applies different degrees of aggressiveness to adjust loop scheduling granularities, aiming at improving the execution performance of parallel loops by making scheduling decisions that match the real workload distributions at runtime. They experimentally compared the performance of the algorithm and its variations with several existing scheduling algorithms on two parallel machines: the KSR-1 and the Convex Exemplar. The kernel application programs used for performance evaluation were carefully selected for different classes of parallel loops. The results show that using runtime information to adaptively adjust scheduling granularity is an effective way to handle loops with a wide range of load distributions when no prior knowledge of the execution can be used. The overhead caused by collecting runtime information is insignificant in comparison with the performance improvement. The experiments show that the adaptive algorithm and its five variations outperformed the existing scheduling algorithms.