Similarity-based ranking and query processing in multimedia databases

Since media-based evaluation yields similarity values, results to a multimedia database query, Q(Y₁,…,Y_n), is defined as an ordered list S_Q of n-tuples of the form X₁,…,X_n. The query Q itself is composed of a set of fuzzy and crisp predicates, constants, variables, and conjunction, disjunction, and negation operators. Since many multimedia applications require partial matches, S_Q includes results which do not satisfy all predicates. Due to the ranking and partial match requirements, traditional query processing techniques do not apply to multimedia databases. In this paper, we first focus on the problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, providing the user with a meaningful final ranking”. More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the requirements of multimedia retrieval problem. We then build on these results in answering the related problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, finding an efficient way to process the query.” We develop an algorithm to efficiently process queries with unordered fuzzy predicates (sub-queries). Although this algorithm can work with different fuzzy semantics, it benefits from the statistical properties of the semantics proposed in this paper. We also present experimental results for evaluating the proposed algorithm in terms of quality of results and search space reduction.     

On Similarity Measures for Multimedia Database Applications

A multimedia database query consists of a set of fuzzy and boolean (or crisp) predicates, constants, variables, and conjunction, disjunction, and negation operators. The fuzzy predicates are evaluated based on different media criteria, such as color, shape, layout, keyword. Since media-based evaluation yields similarity values, results to such a query is defined as an ordered set. Since many multimedia applications require partial matches, query results also include tuples which do not satisfy all predicates. Hence, any fuzzy semantics which extends the boolean semantics of conjunction in a straight forward manner may not be desirable for multimedia databases. In this paper, we focus on the problem of ‘given a multimedia query which consists of multiple fuzzy and crisp predicates, how to provide the user with a meaningful overall ranking.’ More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the retrieval requirements in multimedia databases. Received 13 August 1999 / Revised 13 May 2000 / Accepted in revised form 26 July 2000     

Uncertain deductive databases: A hybrid approach

Uncertainty in deductive databases and logic programming has been modeled using a variety of (numeric and non-numeric) formalisms in the past, including probabilistic, possibilistic, and fuzzy set-theoretic approaches, and many valued logic programming. In this paper, we consider a hybrid approach to the modeling of uncertainty in deductive databases. Our model, called deductive IST (DIST) is based on an extension of the Information Source Tracking (IST) model, recently proposed for relational databases. The DIST model permits uncertainty to be modeled and manipulated in essentially qualitative terms with an option to convert qualitative expressions of uncertainty into numeric form (e.g., probabilities). An uncertain deductive database is modeled as a Horn clause program in the DIST framework, where each fact and rule is annotated with an expression indicating the “sources” contributing to this information and their nature of contribution. (1) We show that positive DIST programs enjoy the least model/least fixpoint semantics analogous to classical logic programs. (2) We show that top-down (e.g., SLD-resolution) and bottom-up (e.g., magic sets rewriting followed by semi-naive evaluation) query processing strategies developed for datalog can be easily extended to DIST programs. (3) Results and techniques for handling negation as failure in classical logic programming can be easily extended to DIST. As an illustration of this, we show how stratified negation can be so extended. We next study the problem of query optimization in such databases and establish the following results. (4) We formulate query containment in qualitative as well as quantitative terms. Intuitively, our qualitative sense of containment would say a query Q₁ is contained in a query Q₂ provided for every input database D, for every tuple t, t ε Q₂(D) holds in every “situation” in which t ε Q₁(D) is true. The quantitative notion of containment would say Q₁ is contained in Q₂ provided on every input, the certainty associated with any tuple computed by Q₁ is no more than the certainty associated with the same tuple by Q₂ on the given input. We also prove that qualitative and quantitative notions of containment (both absolute and uniform versions) coincide. (5) We establish necessary and sufficient conditions for the qualitative containment of conjunctive queries. (6) We extend the well-known chase technique to develop a test for uniform containment and equivalence of positive DIST programs. (7) Finally, we prove that the complexity of testing containment of conjunctive DIST queries remains the same as in the classical case when number of information sources is regarded as a constant (so, it's NP-complete in the size of the queries). We also show that testing containment of conjunctive queries is co-NP-complete in the number of information sources.     

An Extension to GCWA and Query Evaluation for Disjunctive Deductive Databases

We present a simple and intuitive extension GCWA^G of the generalized closed world assumption (GCWA) from positive disjunctive deductive databases to general disjunctive deductive databases (with default negation). This semantics is defined in terms of unfounded sets and possesses an argumentation-theoretic characterization. We also provide a top-down procedure for GCWA^G, which is sound and complete with respect to GCWA^G. We investigate two query evaluation methods for GCWA^G: database partition, and database splitting. The basic idea of these methods is to divide the original deductive database into several smaller sub-databases and the query evaluation in the original database is transformed into the problem of query evaluation in smaller or simplified components. We prove that these two methods of query evaluation are all sound with respect to GCWA^G.     

A modal proof theory for final polynomial coalgebras

An infinitary proof theory is developed for modal logics whose models are coalgebras of polynomial functors on the category of sets. The canonical model method from modal logic is adapted to construct a final coalgebra for any polynomial functor. The states of this final coalgebra are certain “maximal” sets of formulas that have natural syntactic closure properties.

The syntax of these logics extends that of previously developed modal languages for polynomial coalgebras by adding formulas that express the “termination” of certain functions induced by transition paths. A completeness theorem is proven for the logic of functors which have the Lindenbaum property that every consistent set of formulas has a maximal extension. This property is shown to hold if the deducibility relation is generated by countably many inference rules.

A counter-example to completeness is also given. This is a polynomial functor that is not Lindenbaum: it has an uncountable set of formulas that is deductively consistent but has no maximal extension and is unsatisfiable, even though all of its countable subsets are satisfiable.     

The power of inequality semijoins

Semijoin is a relational operator used in many relational query processing algorithms. Semijoins can be used to “reduce” the database by delimitting portions of the database that contain data relevant to a given query. For some queries, there exist sequences of semijoins that delimit the exact portions of the database needed to answer the query. Such sequences are called full reducers.

This paper considers a class of queries called natural inequality queries (NI queries), and characterizes a subclass for which full reducers exist. We also present an efficient algorithm that decides whether an NI query lies within this subclass, and constructs a full reducer for the query. The NI queries are a subset of the aggregate-free, conjunctive queries of QUEL, and permit join clauses to include <, , =, , >.     

障碍环境中可视反向视域K最近邻查询

在障碍环境下的空间应用中,用户通常只对视域范围内可视的数据对象感兴趣。为解决障碍环境中视域范围内的反向最近邻查询问题,将视域可视性引入到反向K最近邻查询中,提出一种可视反向视域K最近邻查询算法。给定某空间数据集P、障碍集O和查询点q,可视反向视域K最近邻查询检索P中数据点,并将q作为可视视域K最近邻。应用查询点进行障碍过滤,得到障碍过滤算法,利用数据对象的视域进行剪枝,使用查询点与数据对象的关系剪枝,形成有效的障碍剪枝规则,并根据剪枝规则得到视域可视性判断算法。在此基础上,分别基于R^*-树和VFR-树提出可视反向视域K最近邻查询算法R^*-V2-RKNN和VFR-V2-RKNN,并分别通过对R^*-树和VFR-树进行一次遍历得到查询结果。在真实数据集和模拟数据集上的实验结果表明,VFR-V2-RKNN算法的查询性能明显优于R^*-V2-RKNN算法。     

基于DOM的XML数据库的索引技术研究

XML作为一种数据交换的国际标准，已经贯穿于Internet应用的各个领域之中，如何快速准确地存储和查询XML数据的数据库技术是一个重要的研究课题。XML索引技术对XML数据库查询处理起着至关重要的作用，提出了基于DOM的XML数据库的索引技术(路径连接索引、值索引和引用索引)，解决了传统的基于树的遍历的XML数据查询方法性能上的不足，并着重对处理含有谓词和引用关系等较复杂的查询路径的不同处理方法进行了对比和分析，还给出了索引空间利用率、查询性能和索引维护代价3个方面的标准测试结果，表明新的索引技术可以有效地提高查询处理效率。     

Variational geometry: A new method for modifying part geometry for finite element analysis

Interactive graphic methods have the potential to significantly reduce the cost associated with pre- and post-processing of finite element analyses. One area of particular importance is the creation and modification of part geometry.

This paper describes a powerful method for modification of geometry for finite element analysis pre-processors. The method, called “Variational Geometry”, uses a single representation to describe the entire family of geometries that share a generic shape.

A solid geometric model of a component is defined with respect to a set of scalar parameters. Dimensions, such as those which appear on a mechanical drawing, are treated as constraints on the permissible values of these parameters. Constraints on the geometry are expressed as a set of non-linear algebraic equations. The values of the parameters and hence the geometry may be determined by solving the set of non-linear constraint equations.

A procedure for minimizing the computational requirements is presented. For a part with n degrees of freedom, the solution time is shown to be O(n).     

Database Repairs and Analytic Tableaux

In this article, we characterize in terms of analytic tableaux the repairs of inconsistent relational databases, that is databases that do not satisfy a given set of integrity constraints. For this purpose we provide closing and opening criteria for branches in tableaux that are built for database instances and their integrity constraints. We use the tableaux based characterization as a basis for consistent query answering, that is for retrieving from the database answers to queries that are consistent with respect to the integrity constraints.     

Chain-split evaluation in deductive databases

Many popularly studied recursions in deductive databases can be compiled into one or a set of highly regular chain generating paths, each of which consists of one or a set of connected predicates. Previous studies on chain-based query evaluation in deductive databases take a chain generating path as an inseparable unit in the evaluation. However, some recursions, especially many functional recursions whose compiled chain consists of infinitely evaluable function(s), should be evaluated by chain-split evaluation, which splits a chain generating path into two portions in the evaluation: an immediately evaluable portion and a delayed-evaluation portion. The necessity of chain-split evaluation is examined from the points of view of both efficiency and finite evaluation, and three chain-split evaluation techniques: magic sets, buffered evaluation, and partial evaluation are developed. Our study shows that chain-split evaluation is a primitive recursive query evaluation technique for different kinds of recursions, and it can be implemented efficiently in deductive databases by extensions to the existing recursive query evaluation methods     

基于Vague数据库的代数查询语言

基于Vague集理论的Vague关系数据库与其他模糊数据库一样,由于所含信息的模糊性,对应着现实世界的多种状态.虽然此类数据库能够更加真实地反映现实世界,但是在基于这些数据库的查询语言的有效性和计算过程的复杂性等方面却存在着一定的问题.本文基于Vague关系数据模型,对其代数查询语言中的选择、投影和连接操作进行了研究,指出基于一般Vague关系数据模型的查询语言中所存在的问题,并提出相应的解决方法,引入一种嵌套机制,对Vague关系模型进行了进一步扩展,进而对新模型查询语言中的三种操作在不同情况下进行了讨论,并给出了相应的定义.     

Clustering factor estimation for totally clustered attributes

Cost models based on the clustering factor (CF) of the attributes have been proposed and shown to be attractive for block access estimation in databases, thanks to their accuracy and economy of use. While query optimizers can use the actual CFs, measured from the data, physical design methods and tools must rely on estimates before the data are stored.

In this paper we present a CF estimation procedure which can be applied to totally clustered attributes (e.g. ordered attributes). Simple and accurate approximations of the derived formulas are also introduced.

Simulations show the accuracy of the proposed CF estimates and the improvment in their behaviour compared to previously published estimates. Reliability for physical design of cost models based on the CF in the presence of a skewed data distribution is also discussed.     

Models of approximation in databases

Partial information in databases can arise when information from several databases is combined. Even if each database is complete for some “world”, the combined databases will not be, and answers to queries against such combined databases can only be approximated. In this paper we describe various situations in which a precise answer cannot be obtained for a query asked against multiple databases. Based on an analysis of these situations, we propose a classification of constructs that can be used to model approximations.

The main goal of the paper is to study several formal models of approximations and their semantics. In particular, we obtain universality properties for these models of approximations. Universality properties suggest syntax for languages with approximations based on the operations which are naturally associated with them. We prove universality properties for most of the approximation constructs. Then we design languages built around datatypes given by the approximation constructs. A straightforward approach results in languages that have a number of limitations. In an attempt to overcome those limitations, we explain how all the languages can be embedded into a language for conjunctive and disjunctive sets from Libkin and Wong (1996) and demonstrate its usefulness in querying independent databases. We also discuss the semantics of approximation constructs and the relationship between them.     

Extension table built-ins for prolog

The ET* algorithm is a complete evaluation strategy for Datalog programs, which are logic programs without function symbols. The ET* algorithm uses extension tables and depth-first iterative deepening to provide the evaluation of pure function-free logic programs as declarative specifications. Extension tables are a memo facility that the algorithm uses both to cut infinite derivation paths for complete evaluation and to optimise the evaluation of logic programs. The original implementation of the ET* algorithm incorporated extension tables as part of the Prolog database using the built-in predicates assert and retract. The advantage of implementing the extension table using the Prolog database is the portability of the ET* algorithm. There are several disadvantages, however, with this approach. One disadvantage is the cost associated with the built-in predicates assert and retract, which are known to be expensive operations in most current Prolog systems. Another disadvantage is the differences across implementations in the semantics that these built-ins provide for dynamic predicates. This paper presents an efficient implementation of extension tables as a global data structure in Prolog, which includes a set of built-in primitives for manipulating the extension table. The ET* algorithm is updated to reflect the utilisation of the global extension table data structure. The implementations of the ET* algorithm are compared using time and space performance on a variety of benchmark programs.     

Hybrid query execution engine for large attributed graphs

Graphs are widely used for modeling complicated data such as social networks, bibliographical networks and knowledge bases. The growing sizes of graph databases motivate the crucial need for developing powerful and scalable graph-based query engines. We propose a SPARQL-like language, G-SPARQL, for querying attributed graphs. The language enables the expression of different types of graph queries that are of large interest in the databases that are modeled as large graph such as pattern matching, reachability and shortest path queries. Each query can combine both structural predicates and value-based predicates (on the attributes of the graph nodes/edges). We describe an algebraic compilation mechanism for our proposed query language which is extended from the relational algebra and based on the basic construct of building SPARQL queries, the Triple Pattern. We describe an efficient hybrid Memory/Disk representation of large attributed graphs where only the topology of the graph is maintained in memory while the data of the graph are stored in a relational database. The execution engine of our proposed query language splits parts of the query plan to be pushed inside the relational database (using SQL) while the execution of other parts of the query plan is processed using memory-based algorithms, as necessary. Experimental results on real and synthetic datasets demonstrate the efficiency and the scalability of our approach and show that our approach outperforms native graph databases by several factors.     

A methodology to retrieve text documents from multiple databases

This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.     

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.     

Inference by generating in deductive databases

A method, APEX, for query evaluation in deductive databases presented in this work is based on discovering of axioms and facts relevant to a given query. The notion of relevancy and migration of facts is derived from an analysis of data flow in the system. APEX is complete, and incorporates efficient query evaluation heuristics. Operation of APEX is illustrated by sample databases involving non-linear recursive axioms and cyclic relations. Main virtues of the method are its generality and adaptivity: it imposes no restrictions on the structure of axioms or the contents of relations, and it employs the knowledge of the actual data acquired at each step of a query evaluation.