不一致数据库上带信任标记的查询结果

不一致数据无法正确反映现实世界,其上的查询结果内含错误或矛盾,而现有的很多不一致数据查询处理相关研究都存在信息丢失的问题.AQA(annotation based query answer)针对这一问题采用信任标签在属性级别上区分一致和不一致数据,避免了信息丢失.但AQA假设记录在依赖左边属性上的分量可信,且只针对函数依赖一种约束,具有应用局限性.在综合约束(函数依赖、包含依赖和域约束)范围内、不确定属性任意的情况下扩展了AQA,重新审视了AQA的数据模型及其上的查询代数,讨论了任意约束在查询结果上的蕴含约束计算问题.实验结果表明,扩展后的AQA非连接类查询的性能和普通的SQL基夺相同,连接查询经优化后性能接近普通SQL查询,但AQA不丢失信息与部分同类研究相比有很大优势.     

Derived types in semantic association discovery

Semantic associations are direct or indirect linkages between two entities that are construed from existing associations among entities. In this paper we extend our previous query language approach for discovering semantic associations with an ability to retrieve semantic associations that, besides explicitly stated (base) associations, may contain associations derived using logic-based derivation rules. As will be shown, this makes it possible to find semantic associations that are both compact and intuitive. To implement this new feature, we introduce a rewriting principle that utilizes derived associations to reduce resulting semantic associations if possible. Other proposed means to assist the interpretation of query results include answer expansion and the ordering of answers. The incorporated answer expansion feature lets the user investigate rewritten semantic associations in a query result at the desired level of detail. The ordering of answers is based on the lengths of the resulting semantic associations, whereby priority is given to shorter semantic associations which often express close and relevant relationships.     

Intelligent query answering in rule based systems

We propose that in large knowledge bases which are collections of atomic facts and general rules (Horn clauses), the rules should be allowed to occur in the answer for a query. We introduce a new concept of the answer for a query which includes both atomic facts and general rules. We provide a method of transforming rules by relational algebra expressions built from projection, join, and selection and demonstrate how the answers consisting of both facts and general rules can be generated.     

Querying Incomplete Information in Semistructured Data

Semistructured data occur in situations where information lacks a homogeneous structure and is incomplete. Yet, up to now the incompleteness of information has not been reflected by special features of query languages. Our goal is to investigate the principles of queries that allow for incomplete answers. We do not present, however, a concrete query language. Queries over classical structured data models contain a number of variables and constraints on these variables. An answer is a binding of the variables by elements of the database such that the constraints are satisfied. In the present paper, we loosen this concept in so far as we allow also answers that are partial; that is, not all variables in the query are bound by such an answer. Partial answers make it necessary to refine the model of query evaluation. The first modification relates to the satisfaction of constraints: in some circumstances we consider constraints involving unbound variables as satisfied. Second, in order to prevent a proliferation of answers, we only accept answers that are maximal in the sense that there are no assignments that bind more variables and satisfy the constraints of the query. Our model of query evaluation consists of two phases, a search phase and a filter phase. Semistructured databases are essentially labeled directed graphs. In the search phase, we use a query graph containing variables to match a maximal portion of the database graph. We investigate three different semantics for query graphs, which give rise to three variants of matching. For each variant, we provide algorithms and complexity results. In the filter phase, the maximal matchings resulting from the search phase are subjected to constraints, which may be weak or strong. Strong constraints require all their variables to be bound, while weak constraints do not. We describe a polynomial algorithm for evaluating a special type of queries with filter constraints, and assess the complexity of evaluating other queries for several kinds of constraints. In the final part, we investigate the containment problem for queries consisting only of search constraints under the different semantics.     

Prioritized repairing and consistent query answering in relational databases

A consistent query answer in an inconsistent database is an answer obtained in every (minimal) repair. The repairs are obtained by resolving all conflicts in all possible ways. Often, however, the user is able to provide a preference on how conflicts should be resolved. We investigate here the framework of preferred consistent query answers, in which user preferences are used to narrow down the set of repairs to a set of preferred repairs. We axiomatize desirable properties of preferred repairs. We present three different families of preferred repairs and study their mutual relationships. Finally, we investigate the complexity of preferred repairing and computing preferred consistent query answers.     

On querying simple conceptual graphs with negation

We consider basic conceptual graphs, namely simple conceptual graphs (SGs), which are equivalent to the existential conjunctive positive fragment of first-order logic. The fundamental problem, deduction, is performed by a graph homomorphism called projection. The existence of a projection from a SG Q to a SG G means that the knowledge represented by Q is deducible from the knowledge represented by G. In this framework, a knowledge base is composed of SGs representing facts and a query is itself a SG. We focus on the issue of querying SGs, which highlights another fundamental problem, namely query answering. Each projection from a query to a fact defines an answer to the query, with an answer being itself a SG. The query answering problem asks for all answers to a query.

This paper introduces atomic negation into this framework. Several understandings of negation are explored, which are all of interest in real world applications. In particular, we focus on situations where, in the context of incomplete knowledge, classical negation is not satisfactory because deduction can be proven but there is no answer to the query. We show that intuitionistic deduction captures the notion of an answer and can be solved by projection checking. Algorithms are provided for all studied problems. They are all based on projection. They can thus be combined to deal with several kinds of negation simultaneously. Relationships with problems on conjunctive queries in databases are recalled and extended. Finally, we point out that this discussion can be put in the context of semantic web databases.     

Query processing over incomplete autonomous databases: query rewriting using learned data dependencies

Incompleteness due to missing attribute values (aka “null values”) is very common in autonomous web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such possibleanswers and gauge their relevance by accessing their likelihood of being pertinent answers to the query. The autonomous nature of web databases poses several challenges in realizing this objective. Such challenges include the restricted access privileges imposed on the data, the limited support for query patterns, and the bounded pool of database and network resources in the web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges. Our technique involves reformulating the user query based on mined correlations among the database attributes. The reformulated queries are aimed at retrieving the relevant possibleanswers in addition to the certain answers. QPIAD is able to gauge the relevance of such queries allowing tradeoffs in reducing the costs of database query processing and answer transmission. To support this framework, we develop methods for mining attribute correlations (in terms of Approximate Functional Dependencies), value distributions (in the form of Naïve Bayes Classifiers), and selectivity estimates. We present empirical studies to demonstrate that our approach is able to effectively retrieve relevant possibleanswers with high precision, high recall, and manageable cost.     

Efficient kNN search in polyphonic music databases using a lower bounding mechanism

Querying polyphonic music from a large data collection is an interesting topic. Recently, researchers have attempted to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this paper, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion and/or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operations to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases. The experimental results show that our method outperforms the previous methods in efficiency. We also evaluate the effectiveness of our method by showing the search results to the musician and nonmusician user groups. The experimental results provide useful guidelines on the design of a polyphonic music database.     

Towards an effective automatic query expansion process using an association rule mining approach

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon?test.     

Constraint-based query evaluation in deductive databases

Constraints play an important role in the efficient query evaluation in deductive databases. Constraint-based query evaluation in deductive databases is investigated, with emphasis on linear recursions with function symbols. Constraints are grouped into three classes: rule constraints, integrity constraints, and query constraints. Techniques are developed for the maximal use of different kinds of constraints in rule compilation and query evaluation. The study on the roles of different classes of constraints in set-oriented evaluation of linear recursions shows the following: rule constraints should be integrated with their corresponding deduction rules in the compilation of recursions; integrity constraints, including finiteness constraints and monotonicity constraints, should be used in the analysis of finite evaluability and termination for specific queries; and query constraints, which are often useful in search space reduction and termination, should be transformed, when necessary, and should be pushed into the compiled chains as deeply as possible for efficient evaluation. The constraint-based query-processing technique integrates query-independent compilation and chain-based query evaluation methods and demonstrates its great promise in deductive query evaluation     

INCREMENTAL EXTRACTION OF ASSOCIATION RULES IN APPLICATIVE DOMAINS

In recent years, the KDD process has been advocated to be an iterative and interactive process. It is seldom the case that a user is able to answer immediately all his questions on date with a single query. On the contrary, the work-flow of the typical user consists of several steps in which he/she iteratively refines the extracted knowledge by inspecting previous results and posing new queries. Given this view of the KDD process, in order to reduce the computational effort, it becomes crucial to have KDD systems that are able to exploit past results. This is especially true in environments in which the system knowledge base is the result of many discoveries on data made separately by the collaborative effort of different users.

In this paper, we consider the problem of mining frequent association rules from database relations. We first model a general, constraint-based, mining language for this task. Then, we propose an algorithm that answers such queries reusing past results. In particular, this solution is effective for a new class of constraints, called context dependent, which are more difficult than the traditionally studied item dependent constraints. Nevertheless, we show that some typical queries of important application domains, such as market stock trading, analysis of web log, and gene microarrays in bioinformatics, have context-dependent constraints. We show with a set of experiments in these application domains that the proposed solution with an incremental approach is both effective and viable.     

Consistent query answers from virtually integrated XML data

When data sources are virtually integrated, there is no common and centralized method to maintain global consistency, so inconsistencies with regard to global integrity constraints are very likely to occur. In this paper, we consider the problem of defining and computing consistent query answers when queries are posed to virtual XML data integration systems, which are specified following the local-as-view approach. We propose a powerful XML constraint model to define global constraints, which can express keys and functional dependencies, and which also extends the newly introduced conditional functional dependencies to XML. We provide an approach to defining XML views, which supports not only edge-path mappings but also data-value bindings to express the join operator. We give formal definitions of repair and consistent query answers with the XML data integration settings. Given a query on the global system, we present a two-step method to compute consistent query answers. First, the given query is transformed using the global constraints, such that to run the transformed query on the original global system will generate exactly the consistent query answers. Because the global instance is not materialized, the query on the global instance is then rewritten in the form of queries on the underlying data sources by reversing rules in view definitions. We illustrate that the XPath query transformations can be implemented in XQuery. Finally, we implement prototypes of our method and evaluate our algorithms in the experiments.     

基于HowNet语义相似度的FAQ研究

FAQ是网站提供在线帮助的主要手段。利用检索机制根据用户提出的问题建立一个候选问句集,利用知网研究了用户问句和候选问句之间的相似度,从中找出最相似的问句,并将相应答案返回给用户。实验表明,该方法提高了问句匹配的准确率。     

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.     

An Imprecision-based Language for the Web

In this paper, we introduce a fuzzy language to extract information from the web extending the web query language WebSQL [1]. These extensions are based on two observations: the inadequacy of traditional Boolean query languages for web documents, and the need to move beyond the notion of query providing just a set of answers in order to provide a better data presentation through answers' restructuring. In order to address the first issue, we consider fuzzy sets to express imprecision in data, queries and answers. In our case, data imprecision comes from the data classification provided by several search engines. Query imprecision occurs in weighting values provided at query definition time. Answer imprecision allows to filter and rank the answers. To address the second point, we provide an answer restructuring language to model the restructuring phase that follows the query phase. The restructuring language allows creation/deletion of links and page creation. Thus several answer organizations are possible as a result to the same query. The resulting language extends in a uniform framework WebSQL. Then we provide a mapping for the language constructs into an extended relational algebra called SAME^W[2] expressing similarity-based queries over imprecisely classified data, queries involving navigation among web pages and answer restructurings. Finally, we study the optimization of similarity-based queries using equivalence and containment rules holding for SAME^Wand presenting several algorithms for query evaluation.     

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

基于空值修复的数据库一致性查询方法*

完整性约束常用来定义数据库的数据语义,违反约束的数据库实例为不一致数据库,返回含有不一致结果的查询称为不一致查询。一致性查询目的在于不修改数据库实例而从不一致数据库获取满足约束的查询结果,已有方法因其支持的约束类型有限或计算复杂度高而影响其应用范围。提出了一种基于空值修复的数据库一致性查询方法,首先将原始完整性约束转换为与查询相关的统一约束,然后根据统一约束对原SQL查询进行查询重写,重写后的查询将不一致属性值当做空值来处理以获得满足完整性约束的结果。系统实现与实验证明,该方法在多种完整性约束类型与SQL     

Adaptive query relaxation and top-<Emphasis Type="Italic">k</Emphasis> result ranking over autonomous web databases

Internet users may suffer the empty or too little answer problem when they post a strict query to the Web database. To address this problem, we develop a general framework to enable automatically query relaxation and top-k result ranking. Our framework consists of two processing steps. The first step is query relaxation. Based on the user original query, we speculate how much the user cares about each specified attribute by measuring its specified value distribution in the database. The rare distribution of the specified value of the attribute indicates the attribute may important for the user. According to the attribute importance, the original query is then rewritten as a relaxed query by expanding each query criterion range. The relaxed degree on each specified attribute is varied with the attribute weight adaptively. The most important attribute is relaxed with the minimum degree so that the answer returned by the relaxed query can be most relevant to the user original intention. The second step is top-k result ranking. In this step, we first generate user contextual preferences from query history and then use them to create a priori orders of tuples during the off-line pre-processing. Only a few representative orders are saved, each corresponding to a set of contexts. Then, these orders and associated contexts are used at querying time to expeditiously provide top-k relevant answers by using the top-k evaluation algorithm. Results of a preliminary user study demonstrate our query relaxation, and top-k result ranking methods can capture the users preferences effectively. The efficiency and effectiveness of our approach is also demonstrated.     

A holistic and principled approach for the empty-answer problem

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty-answer set, our framework dynamically computes and suggests alternative queries with fewer conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty-answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. Moreover, we discuss two important extensions to the base framework: the specification of a minimum size on the number of results returned by a relaxed query and the possibility of proposing multiple conditions at the same time. We analyze the proposed solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantages over the existing approaches.     

A progressive query language and interactive reasoner for information fusion support

Previous approaches in query processing do not consider queries to automatically combine results obtained from different information sources, i.e. they do not support information fusion. In this work, an approach for information fusion using a progressive query language and an interactive reasoner is for this reason introduced. The system basically consists of a query processor with fusion capability and a reasoner with learning capability. This query processor first executes a query to produce some initial results. If the initial results are uninformative, then the reasoner guided by the user creates a more elaborate query by means of some rule and returns the query to the query processor to produce a more informative answer. What is novel in our approach is that application-dependent information fusion rules can be initially specified by the user and subsequently learned by the reasoner. Examples of progressive queries are drawn from multi-sensor information fusion applications.