嵌入式实时数据库系统的研究与设计

在分析研究嵌入式实时数据库系统的体系结构、关键技术、功能及特点的基础上,设计了测控系统中的嵌入式实时数据库系统并开发了相应的基于图形界面的管理系统。在开发系统的过程中,分析了传统嵌入式实时系统的各种解决方案的基础上,主要从模型建立、物理结构设计、查询优化等方面给出了较好的解决方案。     

基于二阶基调的时空查询代数形式化定义

提出了一种基于对象关系数据库和中间件技术的时空数据库系统实现与查询处理框架，着重论述了其中的时空查询代数。引入了二阶基调用于时空查询代数的形式化定义，给出了时宅数据库的逻辑结构定义、类型系统定义以及查询操作定义。     

SATCHMOREBID: SATCHMO(RE) with BIDirectional relevancy

SATCHMORE was introduced as a mechanism to integrate relevancy testing with the model-generation theorem prover SATCHMO. This made it possible to avoid invoking some clauses that appear in no refutation, which was a major drawback of the SATCHMO approach. SATCHMORE relevancy, however, is driven by the entire set of negative clauses and no distinction is accorded to the query negation. Under unfavorable circumstances, such as in the presence of large amounts of negative data, this can reduce the efficiency of SATCHMORE. In this paper we introduce a further refinement of SATCHMO called SATCHMOREBID: SATCHMORE with BIDirectional relevancy. SATCHMOREBID uses only the negation of the query for relevancy determination at the start. Other negative clauses are introduced on demand and only if a refutation is not possible using the current set of negative clauses. The search for the relevant negative clauses is performed in a forward chaining mode as opposed to relevancy propagation in SATCHMORE which is based on backward chaining. SATCHMOREBID is shown to be refutationally sound and complete. Experiments on a prototype SATCHMOREBID implementation point to its potential to enhance the efficiency of the query answering process in disjunctive databases. Donald Loveland, Ph.D.: He is Emeritus Professor of Computer Science at Duke University. He received his Ph.D. in mathematics from New York University and taught at NYU and CMU prior to joining Duke in 1973. His research in automated deduction includes defining the model elimination proof procedure and the notion of linear resolution. He is author of one book and editor/co-editor of two other books on automated theorem proving. He has done research in the areas of algorithms, complexity, expert systems and logic programming. He is an AAAI Fellow, ACM Fellow and winner of the Herbrand Award in Automated Reasoning. Adnan H. Yahya, Ph.D.: He is an associate professor at the Department of Electrical Engineering, Birzeit University, Palestine. He received his Diploma and PhD degrees from St. Petersburg Electrotechnical University and Nothwestern University in 1979 and 1984 respectively. His research interests are in Artificial Intelligence in general and in the areas of Deductive Databases, Logic Programming and Nonmonotonic Reasoning in particular. He had several visiting appointments at universities and research labs in the US, Germany, France and the UK. Adnan Yahya is a member of the ACM, IEEE and IEEE Computer Society.     

Query based model learning and stable tracking of a robot arm using radial basis function network

In the context of a robot manipulator, a generalized neural emulator over the complete workspace is very difficult to obtain because of dimensionally insufficient training data. A query based learning algorithm is proposed in this paper that can generate new examples where control inputs are independent of states of the system. This algorithm is centered around the concept of network inversion using an extended Kalman filtering based algorithm. This is a novel idea since robot manipulator is an open loop unstable system and generation of control input independent of state is a research issue for neural model identification. Two trajectory independent stable control schemes have been designed using the neural emulator. One of the control schemes uses forward-inverse-modeling approach to update the controller parameters adaptively following Lyapunov function synthesis technique. The proposed scheme is trajectory independent unlike the back-propagation scheme. The second type of controller predicts the minimum variance estimate of control action using recall process (network inversion) and the control law is derived following a Lyapunov function synthesis approach so that the closed loop system consisting of controller and neural emulator remains stable. The simulation experiments show that the model validation approach is efficient and the proposed control schemes guarantee stable accurate tracking.     

基于遗传算法的分布式异构数据库的查询优化

对于分布武异构数据库，查询优化问题是其核心问题。本文用基于位、值结构树型编码的遗传算法对分布武异构数据库进行查询优化，取得了很好的效果。     

防止利用ODBC技术非法获取企业数据库中重要数据

ODBC和Query技术相结合使得非计算机专业人员不需要编程技术就可以在Excel中直接获取有关数据库的数据，此方法的使用能使非授权人员避开应用系统对他合法性检查，获取企事业单位中某些数据库重要的数据。本文从技术层面上介绍了Access、FoxPro关系数据库的安全隐患以及防范的对策。     

基于数据流的近似查询计算及其应用研究

通过分析基于实化聚集视图的查询重写方法，将相关的查询计算理论与数据流的查询相结合，针对数据流的特征，给出了一种具有广泛应用前景的基于数据流的近似查询计算方案。     

VFP下纯中文万能查询类的实现

Visual Foxpro5．0提供了一个通用查询类，这个类对数据的一致性和可用性都作了准确的判断和处理，但该类为英文版且最多只能进行两个条件的组合查询，本文将它改进为纯中文任意多个条件的组合查询类。     

An interactive agent-based system for concept-based web search

Search engines are useful tools in looking for information from the Internet. However, due to the difficulties of specifying appropriate queries and the problems of keyword-based similarity ranking presently encountered by search engines, general users are still not satisfied with the results retrieved. To remedy the above difficulties and problems, in this paper we present a multi-agent framework in which an interactive approach is proposed to iteratively collect a user's feedback from the pages he has identified. By analyzing the pages gathered, the system can then gradually formulate queries to efficiently describe the content a user is looking for. In our framework, the evolution strategies are employed to evolve critical feature words for concept modeling in query formulation. The experimental results show that the framework developed is efficient and useful to enhance the quality of web search, and the concept-based semantic search can thus be achieved.     

基于中文文本分类的分词方法研究

文本分类有助于用户有选择地阅读和处理海量文本,因此其预备工作分词系统的研究是很有意义的。该文主要提出了一种基于中文文本分类的分词方法,区别于常用的基于字符串匹配等方法,并利用数据库特有的查询技术设计和实现了该分词系统,旨在通过新的分词方法提供更加准确的分词率,同时提高系统实现效率。