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.     

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.     

A knowledge-based system for generating informative responses to indirect database queries

The objective of this study is to develop a knowledge-base framework for generatingcooperative answers to indirect queries. Anindirect query can be considered as a nonstandard database query in which a user did not specify explicitly the information request. In a cooperative query answering system, a user's indirect query should be answered with an informative response, either anaffirmative response or anegative response, which is generated on the basis of the inference of the user's information request and the reformulation of the users' indirect query.This paper presents methods for inferring users' intended actions, determining users' information requirements, and for automatically reformulating indirect queries into direct queries. The inference process is carried out on the basis of a user model, calluser action model, as well as the query context. Two kinds ofinformative responses, i.e.affirmative responses andnegative responses can be generated by arule-based approach.     

Intelligent query answering by knowledge discovery techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems     

DAWN: an efficient framework of DCT for data with error estimation

On-line analytical processing (OLAP) has become an important component in most data warehouse systems and decision support systems in recent years. In order to deal with the huge amount of data, highly complex queries and increasingly strict response time requirements, approximate query processing has been deemed a viable solution. Most works in this area, however, focus on the space efficiency and are unable to provide quality-guaranteed answers to queries. To remedy this, in this paper, we propose an efficient framework of DCT for dAta With error estimatioN, called DAWN, which focuses on answering range-sum queries from compressed OP-cubes transformed by DCT. Specifically, utilizing the techniques of Geometric series and Euler’s formula, we devise a robust summation function, called the GE function, to answer range queries in constant time, regardless of the number of data cells involved. Note that the GE function can estimate the summation of cosine functions precisely; thus the quality of the answers is superior to that of previous works. Furthermore, an estimator of errors based on the Brown noise assumption (BNA) is devised to provide tight bounds for answering range-sum queries. Our experiment results show that the DAWN framework is scalable to the selectivity of queries and the available storage space. With GE functions and the BNA method, the DAWN framework not only delivers high quality answers for range-sum queries, but also leads to shorter query response time due to its effectiveness in error estimation.     

OWL-QL—a language for deductive query answering on the Semantic Web

This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL query language (OWL-QL) as a candidate standard language and protocol for query–answering dialogues among Semantic Web computational agents using knowledge represented in the W3Cs ontology web language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query–answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute.     

A three-valued semantics for querying and repairing inconsistent databases

The problem of managing and querying inconsistent databases has been deeply investigated in the last few years. As the problem of consistent query answering is hard in the general case, most of the techniques proposed so far have an exponential complexity. Polynomial techniques have been proposed only for restricted forms of constraints (such as functional dependencies) and queries. In this paper, a technique for computing “approximate” consistent answers in polynomial time is proposed, which works in the presence of a wide class of constraints (namely, full constraints) and Datalog queries. The proposed approach is based on a repairing strategy where update operations assigning an undefined truth value to the “reliability” of tuples are allowed, along with updates inserting or deleting tuples. The result of a repair can be viewed as a three-valued database which satisfies the specified constraints. In this regard, a new semantics (namely, partial semantics) is introduced for constraint satisfaction in the context of three-valued databases, which aims at capturing the intuitive meaning of constraints under three-valued logic. It is shown that, in order to compute “approximate” consistent query answers, it suffices to evaluate queries by taking into account a unique repair (called deterministic repair), which in some sense “summarizes” all the possible repairs. The so obtained answers are “approximate” in the sense that are safe (true and false atoms in the answers are, respectively, true and false under the classical two-valued semantics), but not complete.     

An information retrieval approach for approximate queries

With the growing availability of online information systems, a need for user interfaces that are flexible and easy to use has arisen. For such type of systems, an interface that allows the formulation of approximate queries can be of great utility since these allow the user to quickly explore the database contents even when he is unaware of the exact values of the database instances. Our work focuses on this problem, presenting a new model for ranking approximate answers and a new algorithm to compute the semantic similarity between attribute values, based on information retrieval techniques. To demonstrate the utility and usefulness of the approach, we perform a series of usability tests. The results suggest that our approach allows the retrieval of more relevant answers with less effort by the user.     

Probabilistic query answering over inconsistent databases

This paper presents a framework for querying inconsistent databases in the presence of functional dependencies. Most of the works dealing with the problem of extracting reliable information from inconsistent databases are based on the notion of repair, a minimal set of tuple insertions and deletions which leads the database to a consistent state (called repaired database), and the notion of consistent query answer, a query answer that can be obtained from every repaired database. In this work, both the notion of repair and query answer differ from the original ones. In the presence of functional dependencies, tuple deletions are the only operations that are performed in order to restore the consistency of an inconsistent database. However, deleting a tuple to remove an integrity violation potentially eliminates useful information in that tuple. In order to cope with this problem, we adopt a notion of repair, based on tuple updates, which allows us to better preserve information in the source database. A drawback of the notion of consistent query answer is that it does not allow us to discriminate among non-consistent answers, namely answers which can be obtained from a non-empty proper subset of the repaired databases. To obtain more informative query answers, we propose the notion of probabilistic query answer, that is query answers are tuples associated with probabilities. This new semantics of query answering over inconsistent databases allows us to give a measure of uncertainty to query answers. We show that the problem of computing probabilistic query answers is FP ^#P -complete. We also propose a technique for computing probabilistic answers to arbitrary relational algebra queries.     

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.     

Minimal model generation for refined answering of generalized queries in disjunctive deductive databases

Generalized queries are defined as sets of clauses in implication form. They cover several tasks of practical importance for database maintenance such as answering positive queries, computing database completions and integrity constraints checking. We address the issue of answering generalized queries under the minimal model semantics for the class of disjunctive deductive databases (DDDBs). The advanced approach is based on having the query induce an order on the models returned by a sound and complete minimal model generating procedure. We consider answers that are true in all and those that are true in some minimal models of the theory. We address the issue of answering positive queries through the construction of the minimal model state of the DDDB, using a minimal model generating procedure. The refinements allowed by the procedure include isolating a minimal component of a disjunctive answer, the specification of possible updates to the theory to enable the derivability of certain queries and deciding the monotonicity properties of answers to different classes of queries.     

On the finite controllability of conjunctive query answering in databases under open-world assumption

In this paper we study queries over relational databases with integrity constraints (ICs). The main problem we analyze is OWA query answering, i.e., query answering over a database with ICs under open-world assumption. The kinds of ICs that we consider are inclusion dependencies and functional dependencies, in particular key dependencies; the query languages we consider are conjunctive queries and unions of conjunctive queries. We present results about the decidability of OWA query answering under ICs. In particular, we study OWA query answering both over finite databases and over unrestricted databases, and identify the cases in which such a problem is finitely controllable, i.e., when OWA query answering over finite databases coincides with OWA query answering over unrestricted databases. Moreover, we are able to easily turn the above results into new results about implication of ICs and query containment under ICs, due to the deep relationship between OWA query answering and these two classical problems in database theory. In particular, we close two long-standing open problems in query containment, since we prove finite controllability of containment of conjunctive queries both under arbitrary inclusion dependencies and under key and foreign key dependencies. The results of our investigation are very relevant in many research areas which have recently dealt with databases under an incomplete information assumption: e.g., data integration, data exchange, view-based information access, ontology-based information systems, and peer data management systems.     

Paged similarity queries

An important feature of a database management systems (DBMS) is its client/server architecture, where managing shared memory among the clients and the server is always an tough issue. However, similarity queries are specially sensitive to this kind of architecture, since the answer sizes vary widely. Usually, the answers of similarity query are fully processed to be sent in full to the user, who often is interested in just parts of the answer, e.g. just few elements closer or farther to the query reference. Compelling the DBMS to retrieve the full answer, further ignoring its majority is at least a waste of server processing power. Paging the answer is a technique that splits the answer onto several pages, following client requests. Despite the success of paging on traditional queries, little work has been done to support it in similarity queries. In this work, we present a technique that not only provides paging in similarity range or k-nearest neighbor queries, but also supports them in two variations: the forward similarity query and the backward similarity query. They return elements either increasingly farther of increasingly closer to the query reference. The reported experiments show that, depending on the proportion of the interesting part over the full answer, both techniques allow answering queries much faster than it is obtained in the non-paged way.     

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.     

DM4的TopN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。