Logic-based query optimization for object databases

We present a technique for transferring query optimization techniques, developed for relational databases, into object databases. We demonstrate this technique for ODMG database schemas defined in ODL and object queries expressed in OQL. The object schema is represented using a logical representation (Datalog). Semantic knowledge about the object data model, e.g., class hierarchy information, relationship between objects, etc., as well as semantic knowledge about a particular schema and application domain are expressed as integrity constraints. An OQL object query is represented as a logic query and query optimization is performed in the Datalog representation. We obtain equivalent (optimized) logic queries, and subsequently obtain equivalent (optimized) OQL queries for each equivalent logic query. We present one optimization technique for semantic query optimization (SQO) based on the residue technique of U. Charavarthy et al. (1990; 1986; 1988). We show that our technique generalizes previous research on SQO for object databases. We handle a large class of OQL queries, including queries with constructors and methods. We demonstrate how SQO can be used to eliminate queries which contain contradictions and simplify queries, e.g., by eliminating joins, or by reducing the access scope for evaluating a query to some specific subclass(es). We also demonstrate how the definition of a method or integrity constraints describing the method, can be used in optimizing a query with a method     

Polymorphic queries for P2P systems

When a query is posed on a centralized database, if it refers to attributes that are not defined in the database, the user is warranted to get either an error or an empty set. In contrast, when a query is posed on a peer in a P2P system and refers to attributes not found in the local database, the query should not be simply rejected if the relevant information is available at other peers. This paper proposes a query model for unstructured P2P systems to answer such queries. (a) We introduce a class of polymorphic queries, a revision of conjunctive queries by incorporating type variables to accommodate attributes not defined in the local database. (b) We define the semantics of polymorphic queries in terms of horizontal and vertical object expansions, to find attributes and tuples, respectively, missing from the local database. We show that both expansions can be conducted in a uniform framework. (c) We develop a top-K algorithm to approximately answer polymorphic queries. (d) We also provide a method to merge tuples collected from various peers, based on matching keys specified in polymorphic queries. Our experimental study verifies that polymorphic queries are able to find more sensible information than traditional queries supported by P2P systems, and that these queries can be evaluated efficiently.     

一种自底向上的静态语义查询优化算法

演绎数据库语义查询优化是运用数据库中的语义知识，即完整性约束条件，将用户提交的一种查询转换为能有效执行，并与原查询等价的查询的一种优化方法．至今在这一领域已有了许多的算法，但大多是基于自顶向下的查询计算模式．而本文提出的静态语义查询优化算法及其改进算法是在优化“并”和“连接”操作的过程中进行自底向上的查询计算，因此相对自顶向下的计算方式更有效地提高了查询执行效率．     

数据集成中XML数据查询语义重写

查询重写是数据库研究的一个基本问题，它和查询优化，数据仓库，数据集成，语义缓存等数据库问题密切相关，为提高集成系统的查询效率，系统选择提交频率较高的XML查询物化为中间层视图，用户提交查询后，系统尽可能利用中间视图层中视图，而不是访问数据源来回答查询，这个问题实际可以归结为半结构化查询重写问题，考虑到中间视图层空间的有限性，已有视图应当尽可能回答更多的查询，传统查询重写方法有考虑半结构化数据之间的约束，而根据约束可以等价变换查询，从而提高中间视图层中的表达能力，提出了一种新的半结构化查询重写的方法，该方法在保证算法正确性和完备性的基础上，利用上半结构化数据中的约束，尤其是XML文件中的路径依赖，来增强中间层物化视图的表达能力，理论分析和初步原型实验证明方法的有效性。     

Automatic knowledge acquisition and maintenance for semantic queryoptimization

The authors present an approach to acquiring knowledge from previously processed queries. By using newly acquired knowledge together with given semantic knowledge, it is possible to make the query processor and/or optimizer more intelligent so that future queries can b processed more efficiently. The acquired knowledge is in the form of constraints. While some constraints are to be enforced for all database states, others are known to be valid for the current state of the database. The former constraints are statistic integrity constraints, while the latter are called dynamic integrity constraints. Some situations in which certain dynamic semantic constraints can be automatically extracted are identified. This automatic tool for knowledge acquisition can also be used as an interactive tool for identifying potential static integrity constraints. The concept of minimal knowledge base is introduced, and a method to maintain the knowledge base is presented. An algorithm to compute the restriction (selection) closure, i.e. all deductible restrictions, from a given set of restrictions, join predicates (as given in a query), and constraints is given     

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.     

An intelligent query processing for distributed ontologies

In this paper, we propose an intelligent distributed query processing method considering the characteristics of a distributed ontology environment. We suggest more general models of the distributed ontology query and the semantic mapping among distributed ontologies compared with the previous works. Our approach rewrites a distributed ontology query into multiple distributed ontology queries using the semantic mapping, and we can obtain the integrated answer through the execution of these queries. Furthermore, we propose a distributed ontology query processing algorithm with several query optimization techniques: pruning rules to remove unnecessary queries, a cost model considering site load balancing and caching, and a heuristic strategy for scheduling plans to be executed at a local site. Finally, experimental results show that our optimization techniques are effective to reduce the response time.     

A knowledge-based approach to multiple query processing

The collective processing of multiple queries in a database system has recently received renewed attention due to its capability of improving the overall performance of a database system and its applicability to the design of knowledge-based expert systems and extensible database systems. A new multiple query processing strategy is presented which utilizes semantic knowledge on data integrity and information on predicate conditions of the access paths (plans) of queries. The processing of multiple queries is accomplished by the utilization of subset relationships between intermediate results of query executions, which are inferred employing both semantic and logical information. Given a set of fixed order access plans, the A^* algorithm is used to find the set of reformulated access plans which is optimal for a given collection of semantic knowledge.     

Query by class,rule, and concept

The ideal query language for a knowledge base will probably never be found: easy formulation and easy evaluation of queries are two conflicting goals. Easy formulation asks for a flexible, expressive language near to human language or gestures. Easy evaluation of queries requires an effective mapping to machine code, which computes the correct answer in a finite number of steps. This article approaches the problem by a query language with three faces. The first projects queries to concepts of the knowledge representation language KL-One for easy formulation and readability. The second presents queries as rules of a deductive database with fixpoint semantics. The third presents queries as classes whose instances are the materialized answer (view) to the query. The methods for maintaining and updating the views are compiled from their deductive interpretation.     

Querying fragmented relations in a distributed database

Summary The paper discusses the query optimization problem in a distributed database system supporting a complex fragmentation schema, in which different fragments can share the same set of data. The fragmentation and the distribution of data are assumed to be transparent to the user, who queries the database in terms of the global relations. The optimization of queries stated on a single fragmented relation is considered, and a method for its solution is presented. The method is based on an algorithm which calculates a set of virtual fragments that can alternatively be used to answer the query.     

Semantic query optimization for query plans of heterogeneousmultidatabase systems

New applications of information systems need to integrate a large number of heterogeneous databases over computer networks. Answering a query in these applications usually involves selecting relevant information sources and generating a query plan to combine the data automatically. As significant progress has been made in source selection and plan generation, the critical issue has been shifting to query optimization. This paper presents a semantic query optimization (SQO) approach to optimizing query plans of heterogeneous multidatabase systems. This approach provides global optimization for query plans as well as local optimization for subqueries that retrieve data from individual database sources. An important feature of our local optimization algorithm is that we prove necessary and sufficient conditions to eliminate an unnecessary join in a conjunctive query of arbitrary join topology. This feature allows our optimizer to utilize more expressive relational rules to provide a wider range of possible optimizations than previous work in SQO. The local optimization algorithm also features a new data structure called AND-OR implication graphs to facilitate the search for optimal queries. These features allow the global optimization to effectively use semantic knowledge to reduce the data transmission cost. We have implemented this approach in the PESTO (Plan Enhancement by SemanTic Optimization) query plan optimizer as a part of the SIMS information mediator. Experimental results demonstrate that PESTO can provide significant savings in query execution cost over query plan execution without optimization     

A Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases

An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same database management system connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization. Received 6 February 1999 / Revised 15 February 2000 / Accepted 10 May 2000     

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.     

Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks

Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.     

The Threshold Algorithm: From Middleware Systems to the Relational Engine

The answer to a top-k query is an ordered set of tuples, where the ordering is based on how closely each tuple matches the query. In the context of middleware systems, new algorithms to answer top-k queries have been recently proposed. Among these, the threshold algorithm (TA) is the most well-known instance due to its simplicity and memory requirements. TA is based on an early-termination condition and can evaluate top-k queries without examining all the tuples. This top-k query model is prevalent not only over middleware systems, but also over plain relational data. In this work, we analyze the challenges that must be addressed to adapt TA to a relational database system. We show that, depending on the available indices, many alternative TA strategies can be used to answer a given query. Choosing the best alternative requires a cost model that can be seamlessly integrated with that of current optimizers. In this work, we address these challenges and conduct an extensive experimental evaluation of the resulting techniques by characterizing which scenarios can take advantage of TA-like algorithms to answer top-k queries in relational database systems     

A Model for Query Decomposition and Answer Construction in Heterogeneous Distributed Database Systems

The problem of retrieving information from a collection of heterogeneous distributed databases has attracted a number of solutions. However, the task of integrating established database systems is complicated not only by the differences between the database systems themselves, but also by the differences in structure and semantics of the information contained within them. The problem is exacerbated when one needs to provide access to such a system for naive end-users.This paper is concerned with a Knowledge-Based Systems approach to solving this problem for clearly bounded situations, in which both the domain and the types of query are constrained. At the user interface, dialogue is conducted in terms of concepts with which the user is familiar, and these are then mapped into appropriate database queries. To achieve this a model for query decomposition and answer construction has been used. This model is based around the development of an Intensional Structure containing information necessary for the recapture of semantic information lost in the query decomposition process and required in the answer construction process. The model has been successfully implemented in combination with an embedded KBS, within a five-layer representation model.     

An efficient query optimization strategy for spatio-temporal queries in video databases

The interest for multimedia database management systems has grown rapidly due to the need for the storage of huge volumes of multimedia data in computer systems. An important building block of a multimedia database system is the query processor, and a query optimizer embedded to the query processor is needed to answer user queries efficiently. Query optimization problem has been widely studied for conventional database systems; however it is a new research area for multimedia database systems. Due to the differences in query processing strategies, query optimization techniques used in multimedia database systems are different from those used in traditional databases. In this paper, a query optimization strategy is proposed for processing spatio-temporal queries in video database systems. The proposed strategy includes reordering algorithms to be applied on query execution tree. The performance results obtained by testing the reordering algorithms on different query sets are also presented.     

Designing data warehouses

A Data Warehouse (DW) is a database that collects and stores data from multiple remote and heterogeneous information sources. When a query is posed, it is evaluated locally, without accessing the original information sources. In this paper we deal with the issue of designing a DW, in the context of the relational model, by selecting a set of views to materialize in the DW. First, we briefly present a theoretical framework for the DW design problem, which concerns the selection of a set of views that (a) fit in the space allocated to the DW, (b) answer all the queries of interest, and (c) minimize the total query evaluation and view maintenance cost. We then formalize the DW design problem as a state space search problem by taking into account multiquery optimization over the maintenance queries (i.e., queries that compute changes to the materialized views) and the use of auxiliary views for reducing the view maintenance cost. Finally, incremental algorithms and heuristics for pruning the search space are presented.     

Measuring the quality of queries in the fuzzy relational databases

In this paper, we develop a new method to measure the quality of each tuple as an answer with respect to Select‐Project‐Join (SPJ) queries so that we can determine which answers are better answers to the given query in a fuzzy relational database. The quality of an answer is viewed as how much sure information is provided, and how much extra information is needed so that it will be a sure answer to the query. The less extra information that is required and the more sure information that is provided by an answer, the higher the quality of that answer is, and in consequence, it will be more reliable. © 2001 John Wiley & Sons, Inc.