Enhancing accuracy and expressive power of range query answers over incomplete spatial databases via a novel reasoning approach

Modern spatial database applications built on top of distributed and heterogeneous spatial information sources such as conventional spatial databases underlying Geographical Information Systems (GIS), spatial data files and spatial information acquired or inferred from the Web, suffer from data integration and topological consistency problems. This more-and-more conveys in incomplete information, which makes answering range queries over incomplete spatial databases a leading research challenge in spatial database systems research. A significant instance of this setting is represented by the application scenario in which the geometrical information on a sub-set of spatial database objects is incomplete whereas the spatial database still stores topological relations among these objects (e.g., containment relations). Focusing on the spatial database application scenario above, in this paper we propose and experimentally assess a novel technique for efficiently answering range queries over incomplete spatial databases via integrating geometrical information and topological reasoning. We also propose I-SQE (Spatial Query Engine for Incomplete Information), an innovative query engine implementing this technique. Our proposed technique results not only effective but also efficient against both synthetic and real-life spatial data sets, and it finally allows us to enhance the quality and the expressive power of retrieved answers by meaningfully taking advantages from the amenity of representing spatial database objects via both the geometrical and the topological level.     

独立故障串并联控制系统可靠性的最优化研究

研究了独立故障的串并联复式控制系统在单个或多个约束条件下的可靠性最优化问题。从系统的物理概念出发,提出了一种混合方法。按照连续变量平衡提高各级串联环节的可靠性,分析得出了在约束条件下各个环节的并联个数的计算公式和简化计算思路。     

网格数据的语义查询优化研究

利用网格技术实现的异构数据源集成环境中,引入本体可以解决网格数据的语义查询问题。为了提高网格环境中语义查询的效率,提出了一个基于本体的语义查询优化器（GSQO）,该优化器主要实现了以下3个模块的优化：（1）用户查询语义扩展;（2）资源选择;（3）并行处理。实验结果表明,GSQO通过采取上述优化策略提供了较好的查询效率。     

Learning response time for WebSources using query feedback and application in query optimization

Abstract. The rapid growth of the Internet and support for interoperability protocols has increased the number of Web accessible sources, WebSources. Current wrapper mediator architectures need to be extended with a wrapper cost model (WCM) for WebSources that can estimate the response time (delays) to access sources as well as other relevant statistics. In this paper, we present a Web prediction tool (WebPT), a tool that is based on learning using query feedback from WebSources. The WebPT uses dimensions time of day, day, and quantity of data, to learn response times from a particular WebSource, and to predict the expected response time (delay) for some query. Experiment data was collected from several sources, and those dimensions that were significant in estimating the response time were determined. We then trained the WebPT on the collected data, to use the three dimensions mentioned above, and to predict the response time, as well as a confidence in the prediction. We describe the WebPT learning algorithms, and report on the WebPT learning for WebSources. Our research shows that we can improve the quality of learning by tuning the WebPT features, e.g., training the WebPT using a logarithm of the input training data; including significant dimensions in the WebPT; or changing the ordering of dimensions. A comparison of the WebPT with more traditional neural network (NN) learning has been performed, and we briefly report on the comparison. We then demonstrate how the WebPT prediction of delay may be used by a scrambling enabled optimizer. A scrambling algorithm identifies some critical points of delay, where it makes a decision to scramble (modify) a plan, to attempt to hide the expected delay by computing some other part of the plan that is unaffected by the delay. We explore the space of real delay at a WebSource, versus the WebPT prediction of this delay, with respect to critical points of delay in specific plans. We identify those cases where WebPT overestimation or underestimation of the real delay results in a penalty in the scrambling enabled optimizer, and those cases where there is no penalty. Using the experimental data and WebPT learning, we test how good the WebPT is in minimizing these penalties. Received June 22, 1999 / Accepted December 24, 1999     

Aggregation and negation-as-failure

Set-grouping and aggregation are powerful operations of practical interest in database query languages. An aggregate operation is a function that maps a set to some value, e.g., the maximum or minimum in the set, the cardinality of this set, the summation of all its members, etc. Since aggregate operations are typically non-monotonic in nature, recursive programs making use of aggregate operations must be suitably restricted in order that they have a well-defined meaning. In a recent paper we showed that partial-order clauses provide a well-structured means of formulating aggregate operations with recursion. In this paper, we consider the problem of expressing partial-order programs via negation-as-failure (NF), a well-known non-monotonic operation in logic programming. We show a natural translation of partial-order programs to normal logic programs: Anycost-monotonic partial-order programsP is translated to astratified normal program such that the declarative semantics ofP is defined as the stratified semantics of the translated program. The ability to effect such a translation is significant because the resulting normal programs do not make any explicit use of theaggregation capability, yet they are concise and intuitive. The success of this translation is due to the fact that the translated program is a stratified normal program. That would not be the case for other more general classes of programs thancost-monotonic partial-order programs. We therefore develop in stages a refined translation scheme that does not require the translated programs to be stratified, but requires the use of a suitable semantics. The class of normal programs originating from this refined translation scheme is itself interesting: Every program in this class has a clear intended total model, although these programs are in general neither stratified nor call-consistent, and do not have a stable model. The partial model given by the well-founded semantics is consistent with the intended total model and the extended well founded semantics,WFS ⁺, defines the intended model. Since there is a well-defined and efficient operational semantics for partial-order programs^{14, 15, 21)} we conclude that the gap between expression of a problem and computing its solution can be reduced with the right level of notation. Mauricio J. Osorio G., Ph.D.: He is an Associate Professor in the Department of Computer Systems Engineering, University of the Americas, Puebla, Mexico. He is the Head of the Laboratory of Theoretical Computer Science of the Center of Research (CENTIA), Puebla, Mexico. His research is currently funded by CENTIA and CONACYT (Ref. #C065-E9605). He is interested in Applications of Logic to Computer Science, with special emphasis on Logic Programming. He received his B.Sc. in Computer Science from the Universidad Autonoma de Puebla, his M.Sc. in Electrical Engineering from CINVESTAV in Mexico, and his Ph.D. from the State University of New York at Buffalo in 1995. Bharat Jayaraman, Ph.D.: He is an Associate Professor in the Department of Computer Science at the State University of New York at Buffalo. He obtained his bachelors degree in Electronics from the Indian Institute of Technology, Madras in 1975, and his Ph.D. from the University of Utah in 1981. His research interests are in Programming Languages and Declarative Modeling of Complex Systems. He has published over 50 research papers. He has served on the program committees of several conferences in the area of Programming Languages, and he is presently on the Editorial Board of the Journal of Functional and Logic Programming.     

A priori optimization for the probabilistic maximum independent set problem

We first propose a formal definition for the concept of probabilistic combinatorial optimization problem (under the a priori method). Next, we study the complexity of optimally solving probabilistic maximum independent set problem under several a priori optimization strategies as well as the complexity of approximating optimal solutions. For the different strategies studied, we present results about the restriction of probabilistic independent set on bipartite graphs.     

多引擎服务组合的执行优化

提出了多引擎的Web服务管理系统,解决了系统中服务组合的执行优化问题。分析了该多引擎系统中服务组合的调度执行,提出了动态规划算法。在分派和执行Web服务前,生成流水执行的子序列方案,使调度执行Web服务的引擎的最大负载最小。实验结果表明,与随机算法相比,该算法使性能显著提高。