Fuzzy query translation for relational database systems

The paper presents a new method for fuzzy query translation based on the alpha-cuts operations of fuzzy numbers. This proposed method allows the retrieval conditions of SQL queries to be described by fuzzy terms represented by fuzzy numbers. It emphasizes friendliness and flexibility for inexperienced users. The authors have implemented a fuzzy query translator to translate user's fuzzy queries into precise queries for relational database systems. Because the proposed method allows the user to construct his fuzzy queries intuitively and to choose different retrieval threshold values for fuzzy query translation, the existing relational database systems will be more friendly and more flexible to the users.     

Active concept learning in image databases.

Concept learning in content-based image retrieval systems is a challenging task. This paper presents an active concept learning approach based on the mixture model to deal with the two basic aspects of a database system: the changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we a) propose a new user directed semi-supervised expectation-maximization algorithm for mixture parameter estimation, and b) develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation versus exploration in the search space helps to find the optimal model efficiently. Our concept knowledge transduction approach is able to deal with the cases of image insertion and query images being outside the database. The system handles the situation where users may mislabel images during relevance feedback. Experimental results on Corel database show the efficacy of our active concept learning approach and the improvement in retrieval performance by concept transduction.     

CoBase: A scalable and extensible cooperative information system

A new generation of information systems that integrates knowledge base technology with database systems is presented for providing cooperative (approximate, conceptual, and associative) query answering. Based on the database schema and application characteristics, data are organized into Type Abstraction Hierarchies (TAHs). The higher levels of the hierarchy provide a more abstract data representation than the lower levels. Generalization (moving up in the hierarchy), specialization (moving down the hierarchy), and association (moving between hierarchies) are the three key operations in deriving cooperative query answers for the user. Based on the context, the TAHs can be constructed automatically from databases. An intelligent dictionary/directory in the system lists the location and characteristics (e.g., context and user type) of the TAHs. CoBase also has a relaxation manager to provide control for query relaxations. In addition, an explanation system is included to describe the relaxation and association processes and to provide the quality of the relaxed answers. CoBase uses a mediator architecture to provide scalability and extensibility. Each cooperative module, such as relaxation, association, explanation, and TAH management, is implemented as a mediator. Further, an intelligent directory mediator is provided to direct mediator requests to the appropriate service mediators. Mediators communicate with each other via KQML. The GUI includes a map server which allows users to specify queries graphically and incrementally on the map, greatly improving querying capabilities. CoBase has been demonstrated to answer imprecise queries for transportation and logistic planning applications. Currently, we are applying the CoBase methodology to match medical image (X-ray, MRI) features and approximate matching of emitter signals in electronic warfare applications.This work supported by ARPA contract F30602-94-C-0207.     

Controlled generation of intensional answers

Intensional answers are conditions that tuples of values must satisfy to belong to the usual extensional answer of a query addressed to a deductive database. The authors review the concept of intensional answers and introduce a general method for generating them as logical consequences of the query and of deduction rules. The authors show how integrity constraints can filter out inadequate answers and produce simpler and more informative answers. An efficient organization for the combination of answers and constraints is described. The introduction of negation in queries and in the body of deduction rules is discussed. Beyond the mechanics of answer generation, the interest of the approach also depends on a strategy for selecting answers to a user submitting a query. This requires techniques for user modeling and dialogue management similar to those required for expert systems     

Attribute-level neighbor hierarchy construction using evolved pattern-based knowledge induction

Neighbor knowledge construction is the foundation for the development of cooperative query answering systems capable of searching for close match or approximate answers when exact match answers are not available. This paper presents a technique for developing neighbor hierarchies at the attribute level. The proposed technique is called the evolved pattern-based knowledge induction (ePKI) technique and allows construction of neighbor hierarchies for nonunique attributes based upon confidences, popularities, and clustering correlations of inferential relationships among attribute values. The technique is applicable for both categorical and numerical (discrete and continuous) attribute values. Attribute value neighbor hierarchies generated by the ePKI technique allow a cooperative query answering system to search for approximate answers by relaxing each individual query condition separately. Consequently, users can search for approximate answers even when the exact match answers do not exist in the database (i.e., searching for existing similar parts as part of the implementation of the concepts of rapid prototyping). Several experiments were conducted to assess the performance of the ePKI in constructing attribute-level neighbor hierarchies. Results indicate that the ePKI technique produces accurate neighbor hierarchies when strong inferential relationships appear among data.     

QQL: A DB&;IR Query Language

Traditional database query languages are based on set theory and crisp first order logic. However, many applications require retrieval-like queries which return result objects associated with a degree of being relevant to the query. Historically, retrieval systems estimate relevance by exploiting hidden object semantics whereas query processing in database systems relies on matching select-conditions with attribute values. Thus, different mechanisms were developed for database and information retrieval systems. In consequence, there is a lack of support for queries involving both retrieval and database search terms. In this work, we introduce the quantum query language (QQL). Its underlying unifying theory is based on the mathematical formalism of quantum mechanics and quantum logic. Van Rijsbergen already discussed the strong relation between the formalism of quantum mechanics and information retrieval. In this work, we interrelate concepts from database query processing to concepts from quantum mechanics and logic. As result, we obtain a common theory which allows us to incorporate seamlessly retrieval search into traditional database query processing.     

A methodology which requires user participation in the design of information systems

User participation in the design of computerized information systems is an accepted philosophy. The goal is to enhance the appreciation for and thus utility of the new system. In reality, however, system design procedures tend not to achieve this goal. The reason is that participation is implemented half heartedly, allowing the user to ask and answer questions, but not get involved in the design. This paper presents an adaptation of Nadler's “IDEALS” approach to the design of information systems. The approach presented is a five step procedure which removes the user from his daily information related activities and allows him to hypothesize an idealized use of information in his job. The approach then proceeds to allow the user and analyst to creatively implement a close approximation to the ideal design target.     

DM4的Top N查询优化设计

随着关系数据库系统越来越多地运用到多媒体系统、决策支持系统、信息检索系统中,如何快速得到一个查询的最佳结果而不是全部结果这个问题变得越来越重要。回答这类TopN查询的一种快速有效的方法是利用数据库系统原有的统计信息,将TopN查询映射为一个范围查询。但它存在的难点在于如何选择这个范围阈值。文章提出通过分层抽样的方法确定范围阈值,并把它应用到DM4系统中,实践证明该方法是行之有效的。     

Neighborhood systems and approximate retrieval

An approximate retrieval model is proposed based on the notion of neighborhood systems. The knowledge used in the model consists of an information table, in which each object is represented by its values on a finite set of attributes, and neighborhood systems on attribute values, which provide semantic similarity or closeness of different values. An information table can be used for exact retrieval. With the introduction of neighborhood systems to information tables, one is able to perform approximate retrieval. Operations on neighborhood systems are introduced based on power algebras. An ordering relation representing the information of a neighborhood system is suggested and examined. Approximate retrieval is carried out by the relaxation of the original query using neighborhood systems, and the combination of intermediate results using neighborhood system operations. The final retrieval results are presented according to the proposed ordering relation. In contrast to many existing systems, a main advantage of the proposed model is that the retrieval results are a non-linear ordering of objects.     

Information retrieval and hypertext: competing technologies or complementary access methods

Abstract. Information retrieval typically involves accessing textual information from a database in response to a user's vague information need. Hypertext or hypermedia, on the other hand, involves a user browsing through a database of textual or multimedia information in response to a variety of types of information need. Thus information retrieval can be said to have a searching metaphor while hypertext has a browsing analogy. Initially, these two technologies for information access appear to be very different, almost competitive in nature. In this paper information retrieval systems are briefly reviewed and hypertext systems are also examined. These two techniques for accessing information have been integrated into a prototype system which is described. The system dynamically generates guided tours in response to a user's query and the tour guides the user through the hypertext. Some experiments reporting on the effectiveness of this as an information access strategy are given.     

基于模糊语言方法的信息检索系统的研究

该文提出了一个基于模糊语言方法的信息检索系统模型。该系统分为查询界面子系统、数据库子系统和检索子系统三大部分。在查询界面子系统,用布尔表达式表示用户的查询请求,并对每个查询关键词赋予了两种不同语义的语言值权重,该权重表达了用户的模糊检索要求;在数据库子系统,用索引词一文档模糊矩阵表示待检索的文档,对每个索引词。根据其在文档中的出现频率大小。引入了数值权重;在检索子系统,运用模糊语言方法,对用户输入的布尔查询表达式与索引词一文档模糊矩阵进行自底向上的模糊匹配,最后返回满足用户要求的检索结果。相对于传统的基于查询关键词精确匹配的检索系统而言,该系统能较好地满足用户查询要求中的灵活性。     

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

 Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.     

Fast semantic object search and detection for vegetable trading information using Steiner tree

We propose an approach to speed up the semantic object search and detection for vegetable trading information using Steiner Tree. Through analysis, comparing the relevant ontology construction method, we present a set of ontology construction methods based on domain ontology for vegetables transaction information. With Jena2 provides rule-based reasoning engine, More related information could be searched with the help of ontology database and ontology reasoning, query expansion is to achieve sub-vocabulary of user input, the parent class of words, equivalence class of extensions, and use of ontology reasoning to get some hidden information to use of these technologies, we design and implementation of ontology-based semantic vegetables transaction information retrieval system, and through compare to keyword-based matching of large-scale vegetable trading site retrieval systems, the results show that the recall and precision rate of ontology-based information retrieval system much better than keyword-based information retrieval system, and has some practical value.     

RUBRIC: A System for Rule-Based Information Retrieval

A research prototype software system for conceptual information retrieval has been developed. The goal of the system, called RUBRIC, is to provide more automated and relevant access to unformatted textual databases. The approach is to use production rules from artificial intelligence to define a hierarchy of retrieval subtopics, with fuzzy context expressions and specific word phrases at the bottom. RUBRIC allows the definition of detailed queries starting at a conceptual level, partial matching of a query and a document, selection of only the highest ranked documents for presentation to the user, and detailed explanation of how and why a particular document was selected. Initial experiments indicate that a RUBRIC rule set better matches human retrieval judgment than a standard Boolean keyword expression, given equal amounts of effort in defining each. The techniques presented may be useful in stand-alone retrieval systems, front-ends to existing information retrieval systems, or real-time document filtering and routing.     

Partial user-supplied information and user modeling for improving QoS

The incorporation of user-supplied information has become mandatory for the improvement of QoS in network systems. There is the question about accommodation of new users of a service, given that information about former users of a service is available. In the present work, we followed two approaches to derive information about new users in the network design and control processes, where both are based on prototype generation for the answers of former users to a QoS related questionnaire. In the first approach, attempts were made to map user attributes to prototypes. The second approach used a mapping from partial answers to a prototype. As a result, the first approach appeared to be infeasible, while the second showed good results. In the resulting trade-off between number of prototypes and classification accuracy, it is possible, for example, with 8 prototypes for around 1000 users to predict the answers of new users by using only 30% of the answers of former users, while reducing accuracy by only 13% at the same time.     

强制数据隐私和用户隐私的外包数据库服务研究*

外包数据库中的数据隐私和用户隐私保护是现代外包数据库服务面临的新挑战,针对目前外包数据库服务中单方面考虑数据隐私保护或用户隐私保护技术难以同时满足外包数据库安全需求的不足,提出一种可同时强制数据隐私和用户隐私保护的外包数据库服务模型,采用属性分解和部分属性加密技术,基于结合准标志集自动检测技术的近似算法实现外包数据的最小加密属性分解,同时把密码学应用于辅助随机服务器协议,以实现数据库访问时的用户隐私保护。理论分析和实验结果表明,该模型可以提供有效的数据隐私保护和查询处理,以及较好的用户隐私保护计算复杂度。     

Relaxation as a platform for cooperative answering

Responses to queries posed by a user of a database do not always contain the information desired. Database answers to a query, although they may be logically correct, can sometimes be misleading. Research in the area of cooperative answering for databases and deductive databases seeks to rectify these problems. We introduce a cooperative method calledrelaxation for expanding deductive database and logic programming queries. The relaxation method expands the scope of a query by relaxing the constraints implicit in the query. This allows the database to return answers related to the original query as well as the literal answers themselves. These additional answers may be of interest to the user. In section 1 we introduce the problem and method. In Section 2 we give some background on the research done in cooperative answering. Section 3 discusses the relaxation method, a potential control strategy, and uses. Section 4 looks at a semantic counterpart to this notion. In Section 5 we explore some of the control and efficiency issues. We enumerate open issues in Section 6, and conclude in Section 7.     

Adaptive similarity search for the retrieval of rare events from large time series databases

Improving the recall of information retrieval systems for similarity search in time series databases is of great practical importance. In the manufacturing domain, these systems are used to query large databases of manufacturing process data that contain terabytes of time series data from millions of parts. This allows domain experts to identify parts that exhibit specific process faults. In practice, the search often amounts to an iterative query–response cycle in which users define new queries (time series patterns) based on results of previous queries. This is a well-documented phenomenon in information retrieval and not unique to the manufacturing domain. Indexing manufacturing databases to speed up the exploratory search is often not feasible as it may result in an unacceptable reduction in recall. In this paper, we present a novel adaptive search algorithm that refines the query based on relevance feedback provided by the user. Additionally, we propose a mechanism that allows the algorithm to self-adapt to new patterns without requiring any user input. As the search progresses, the algorithm constructs a library of time series patterns that are used to accurately find objects of the target class. Experimental validation of the algorithm on real-world manufacturing data shows, that the recall for the retrieval of fault patterns is considerably higher than that of other state-of-the-art adaptive search algorithms. Additionally, its application to publicly available benchmark data sets shows, that these results are transferable to other domains.