1194012
BIG: A Resource-Bounded Information Gathering Agent
Effective information gathering on the WWW is a complex task requiring planning, scheduling, text processing, and interpretation-style reasoning about extracted data to resolve inconsistencies and to refine hypotheses about the data. This paper describes the rationale, architecture, and implementation of a next generation information gathering system - a system that integrates several areas of AI research under a single research umbrella. The goal of this system is to exploit the vast number of information sources available today on the NII including a growing number of digital libraries, independent news agencies, government agencies, as well as human experts providing a variety of services. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering coordination problem. Our solution is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource tradeoffs 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.
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.
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.
3568899
Whole Execution Traces
Different types of program profiles (control flow, value, address, and dependence) have been collected and extensively studied by researchers to identify program characteristics that can then be exploited to develop more effective compilers and architectures. Due to the large amounts of profile data produced by realistic program runs, most work has focused on separately collecting and compressing different types of profiles. In this paper we present a unified representation of profiles called Whole Execution Trace (WET) which includes the complete information contained in each of the above types of traces. Thus WETs provide a basis for a next generation software tool that will enable mining of program profiles to identify program characteristics that require understanding of relationships among various types of profiles. The key features of our WET representation are: WET is constructed by labeling a static program representation with profile information such that relavent and related profile information can be directly accessed by analysis algorithms as they traverse the representation; a highly effective two tier strategy is used to significantly compress the WET; and compression techniques are designed such that they do not adversely affect the ability to rapidly traverse WET for extracting subsets of information corresponding to individual profile types as well as a combination of profile types (e.g., in form of dynamic slices of WETs). Our experimentation shows that on an average execution traces resulting from execution of 647 Million statements can be stored in 331 Megabytes of storage after compression. The compression factors range from 16 to 83. Moreover the rates at which different types of profiles can be individually or simultaneously extracted are high.
20916764
Low-complexity Interpolation Architecture for Soft-decision Reed-Solomon Decoding
Reed-Solomon (RS) codes have very broad applications in digital communication and storage systems. Among the decoding algorithms of RS codes, the Koetter-Vardy (KV) soft-decision decoding algorithm can achieve substantial coding gain with a polynomial complexity. One of the major steps of the KV algorithm is the interpolation. Recently, a new algorithm was proposed to solve the interpolation problem. Compared to previous efforts, this algorithm is computationally simpler, and thus can potentially lead to practical high-speed hardware implementations of the KV algorithm. This paper proposes novel transformation techniques to further reduce the hardware complexity of the new interpolation algorithm. In addition, efficient VLSI architectures are provided for the new algorithm
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:
5541899
Automatic identification of digital video based on shot-level sequence matching
To locate a video clip in large collections is very important for retrieval applications, especially for digital rights management. In this paper, we present a novel technique for automatic identification of digital video. This new algorithm is based on dynamic programming that fully uses the temporal dimension to measure the similarity between two video sequences. A normalized chromaticity histogram is used as a feature which is illumination-invariant. Dynamic programming is applied on shot-level to find the optimal nonlinear mapping between video sequences. Two new normalized distance measures are presented for video sequence matching. One measure is based on the normalization of the optimal path found by dynamic programming. The other measure combines both the visual features and the temporal information. Experimental results show that the shot-level approach is robust to frame rate conversion, color correction, and compressions. The proposed distance measures are suitable for variable-length comparisons.
14218778
Reconstruction of DNA sequencing by hybridization
MOTIVATION
It is widely recognized that the hybridization process is prone to errors and that the future of DNA sequencing by hybridization is predicated on the ability to successfully cope with such errors. However, the occurrence of hybridization errors results in the computational difficulty of the reconstruction of DNA sequencing by hybridization. The reconstruction problem of DNA sequencing by hybridization with errors is a strongly NP-hard problem. So far the problem has not been solved well.


RESULTS
In this paper, a new approach is presented to solve the reconstruction problem of DNA sequencing by hybridization, which realizes the computational part of the SBH experiment. The proposed algorithm accepts both the negative and positive errors. The computational experiments show that the algorithm behaves satisfactorily, especially for the case with k-tuple repetitions and positive errors.