Efficient Distributed Skyline Queries for Mobile Applications

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites (database servers) which are interconnected through a high-speed wired network, and queries are issued by mobile units (laptop, cell phone, etc.) which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing (EDS-MC). In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.     

Effective utilization of copies in a transparent distributed environment

In a distributed relational database system, the processing of a query involves data transmission among different sites via a computer network. In a distributed database multiple copies of each relation can be allocated to different, physically distributed sites. In this paper we discuss the query preoptimization problem for join-queries. In general, there is a large number of possibilities to use the copies of the data item in a distributed relational database when evaluating a join-query. We consider the problem of a copy preselection for each relation in a join sequence of a join-query. We show how to express the preselection problem for a given query and data allocation to the network in terms of an integer linear programming problem, namely, a minimum cover problem. It can be treated as a heuristic for the first phase of a join-query optimization, and as such as an input to the final stage of optimization, the execution strategy generation for a join-query. In this paper we assumed that a distributed system provides fully transparent data management, i.e., data allocation to the network and data replication which is revealed to a user. We illustrate the proposed mathematical programming problem through a nontrivial example. Recommended by: R. Elamsri     

基于XML的数据管理技术

随着Web技术的快速发展,如何有效地存储、索引、查询和显示XML数据已经成为数据库研究领域的一个热点研究问题。本文介绍了XML数据的3种不同存储方法;XML搜索查询的工具和语言;XML数据的访问控制模型;XML最直接的显示方法以及正在实现的真正的XML数据库等。通过这些XML数据管理技术,可以了解到当下XML研究领域的先进技术和方法,指导今后的研究方向和重点。     

XML queries and algebra in the Enosys integration platform

We describe the Enosys XML integration platform, focusing on the query language, algebra, and architecture of its query processor. The platform enables the development of eBusiness applications in customer relationship management, e-commerce, supply chain management, and decision support. These applications often require that data be integrated dynamically from multiple information sources. The Enosys platform allows one to build (virtual and/or materialized) integrated XML views of multiple sources, using XML queries as view definitions. During run-time, the application issues XML queries against the views. Queries and views are translated into the XCQL algebra and are combined into a single algebra expression/plan. Query plan composition and query plan decomposition challenges are faced in this process. Finally, the query processor lazily evaluates the result, using an appropriate adaptation of relational database iterator models to XML. The paper describes the platform architecture and components, the supported XML query language and the query processor architecture. It focuses on the underlying XML query algebra, which differs from the algebras that have been considered by W3C in that it is particularly tuned to semistructured data and to optimization and efficient evaluation in a system that follows the conventional architecture of database systems.     

A Semantic Cache Framework for Secure XML Queries

Secure XML query answering to protect data privacy and semantic cache to speed up XML query answering are two hot spots in current research areas of XML database systems. While both issues are explored respectively in depth,they have not been studied together,that is,the problem of semantic cache for secure XML query answering has not been addressed yet. In this paper,we present an interesting joint of these two aspects and propose an efficient framework of semantic cache for secure XML query answering,which can improve the performance of XML database systems under secure circumstances. Our framework combines access control,user privilege management over XML data and the state-of-the-art semantic XML query cache techniques,to ensure that data are presented only to authorized users in an efficient way. To the best of our knowledge,the approach we propose here is among the first beneficial efforts in a novel perspective of combining caching and security for XML database to improve system performance. The efficiency of our framework is verified by comprehensive experiments.     

基于XML的分布式数据库非递归查询

分布式数据库系统数据的物理分布和冗余导致其处理难度和复杂度增加,分布式数据库的查询优化是数据库查询理论的核心。该文应用XML技术,分析用户提交的非递归查询任务,得到一个最小查询视图。对各分布式数据库进行查询,处理查询所得结果并返回给用户,实现对分布式数据库的有效查询。     

Improving network systems performance by clustering distributed database sites

Clustering network sites is a vital issue in parallel and distributed database systems DDBS. Grouping distributed database network sites into clusters is considered an efficient way to minimize the communication time required for query processing. However, clustering network sites is still an open research problem since its optimal solution is NP-complete. The main contribution in this field is to find a near optimal solution that groups distributed database network sites into disjoint clusters in order to minimize the communication time required for data allocation. Grouping a large number of network sites into a small number of clusters effectively increases the transaction response time, results in better data distribution, and improves the distributed database system performance. We present a novel algorithm for clustering distributed database network sites based on the communication time as database query processing is time dependent. Extensive experimental tests and simulations are conducted on this clustering algorithm. The experimental and simulation results show that a better network distribution is achieved with significant network servers load balance and network delay, a minor communication time between network sites is realized, and a higher distributed database system performance is recognized.     

基于XML的分布式数据库集成系统

提出一种将分布式数据库数据映射为符合用户要求的XML文档的描述语言——R2XL,在此基础上给出基于XML的分布式数据库集成系统,实现分布式数据库的透明访问和联合查询。对该系统中的查询处理、分布式数据库中的数据分片方法及相应的集成策略等关键问题进行了探讨。     

Mutant query plans

We propose a flexible and robust framework for distributed query processing based on mutant query plans (MQP). A MQP is an XML representation of a query plan that can also include verbatim XML data, references to resource locations (URLs), or abstract resource names (URNs). Servers work using local, possibly incomplete knowledge, partially evaluate as much of the query plan as they can, incorporate the partial results into a new, mutated query plan and transfer it to some other server that can continue processing. We have implemented an initial version of this framework, and present preliminary performance results.     

Active semantic caching to optimize multidimensional data analysis in parallel and distributed environments

In this paper, we present a multi-query optimization framework based on the concept of active semantic caching. The framework permits the identification and transparent reuse of data and computation in the presence of multiple queries (or query batches) that specify user-defined operators and aggregations originating from scientific data-analysis applications. We show how query scheduling techniques, coupled with intelligent cache replacement policies, can further improve the performance of query processing by leveraging the active semantic caching operators. We also propose a methodology for functionally decomposing complex queries in terms of primitives so that multiple reuse sites are exposed to the query optimizer, to increase the amount of reuse. The optimization framework and the database system implemented with it are designed to be efficient irrespective of the underlying parallel and/or distributed machine configuration. We present experimental results highlighting the performance improvements obtained by our methods using real scientific data-analysis applications on multiple parallel and distributed processing configurations (e.g., single symmetric multiprocessor (SMP) machine, cluster of SMP nodes, and a Grid computing configuration).     

利用约束优化数据库WEB视图上的查询

当前,许多大的WEB站点的信息和数据呈现出结构化或半结构化的特点,因而可经抽象,作为类似关系数据库或者面向对象数据库并加以处理,以提高操作效率,特别是在此基础上进行的查询操作。但查询这些抽象的结构化或半结构化的视图需要新的技术。由于数据存储比较分散,并且多数以HTML、XML等形式存放,因而网络访问成为主要的查询代价构成要素。为此通过利用WEB数据库的冗余来减少网络访问。文章采用数据模型Araneus的一个子集作为数据模型,并利用链接约束、包含约束、范围约束以及多种规则重写关系代数表达式,以期达到优化查询结果的目的。     

Efficiently Querying Large XML Data Repositories: A Survey

Extensible markup language (XML) is emerging as a de facto standard for information exchange among various applications on the World Wide Web. There has been a growing need for developing high-performance techniques to query large XML data repositories efficiently. One important problem in XML query processing is twig pattern matching, that is, finding in an XML data tree D all matches that satisfy a specified twig (or path) query pattern Q. In this survey, we review, classify, and compare major techniques for twig pattern matching. Specifically, we consider two classes of major XML query processing techniques: the relational approach and the native approach. The relational approach directly utilizes existing relational database systems to store and query XML data, which enables the use of all important techniques that have been developed for relational databases, whereas in the native approach, specialized storage and query processing systems tailored for XML data are developed from scratch to further improve XML query performance. As implied by existing work, XML data querying and management are developing in the direction of integrating the relational approach with the native approach, which could result in higher query processing performance and also significantly reduce system reengineering costs.     

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     

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。     

基于关系的XML数据存储

ＸＭＬ正成为Ｉｎｔｅｒｎｅｔ上数据表示与交换的标准,如何存储ＸＭＬ数据是一个关键问题。我们在系统实现时采用了基于关系数据库的存储方式。文章对于关系模式如何由ＤＴＤ生成、等问题,做了详细的探讨。     

A comparison of two approaches to utilizing XML in parametric databases for temporal data

The parametric data model captures an object in terms of a single tuple. This feature eliminates unnecessary self-join operations to combine tuples scattered in a temporal relation. Despite this advantage, this model is relatively difficult to implement on top of relational databases because the sizes of attributes are unfixed. Since data boundaries are not problematic in XML, XML can be an elegant solution to implement parametric databases for temporal data. There are two approaches to implementing parametric databases using XML: (1) a native XML database with XQuery engine, and (2) an XML storage with a temporal query language. To determine which approach is appropriate in parametric databases, we consider four questions: the effectiveness of XML in modeling temporal data, the applicability of XML query languages, the user-friendliness of the query languages, and system performances of two approaches. By evaluating the four questions, we show that the latter approach is more appropriate to utilizing XML in parametric databases.     

Storing and querying XML data using denormalized relational databases

XML database systems emerge as a result of the acceptance of the XML data model. Recent works have followed the promising approach of building XML database management systems on underlying RDBMSs. Achieving query processing performance reduces to two questions: (i) How should the XML data be decomposed into data that are stored in the RDBMS? (ii) How should the XML query be translated into an efficient plan that sends one or more SQL queries to the underlying RDBMS and combines the data into the XML result? We provide a formal framework for XML Schema-driven decompositions, which encompasses the decompositions proposed in prior work and extends them with decompositions that employ denormalized tables and binary-coded XML fragments. We provide corresponding query processing algorithms that translate the XML query conditions into conditions on the relational tables and assemble the decomposed data into the XML query result. Our key performance focus is the response time for delivering the first results of a query. The most effective of the described decompositions have been implemented in XCacheDB, an XML DBMS built on top of a commercial RDBMS, which serves as our experimental basis. We present experiments and analysis that point to a class of decompositions, called inlined decompositions, that improve query performance for full results and first results, without significant increase in the size of the database.Received: 21 December 2001, Accepted: 1 July 2003, Published online: 23 June 2004Edited by: A. HalevyAndrey Balmin: Andrey Balmin has been supported by NSF IRI-9734548.Yannis Papakonstantinou: The authors built the XCacheDB system while on leave at Enosys Software, Inc., during 2000.     

ArchIS: an XML-based approach to transaction-time temporal database systems

Effective support for temporal applications by database systems represents an important technical objective that is difficult to achieve since it requires an integrated solution for several problems, including (i) expressive temporal representations and data models, (ii) powerful languages for temporal queries and snapshot queries, (iii) indexing, clustering and query optimization techniques for managing temporal information efficiently, and (iv) architectures that bring together the different pieces of enabling technology into a robust system. In this paper, we present the ArchIS system that achieves these objectives by supporting a temporally grouped data model on top of RDBMS. ArchIS’ architecture uses (a) XML to support temporally grouped (virtual) representations of the database history, (b) XQuery to express powerful temporal queries on such views, (c) temporal clustering and indexing techniques for managing the actual historical data in a relational database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational databases, and can also assure excellent storage efficiency by providing compression as an option. This approach achieves full-functionality transaction-time databases without requiring temporal extensions in XML or database standards, and provides critical support to emerging application areas such as RFID.     

基于XML的石油勘探数据库查询应用系统的实现

通过XML技术在河南油田勘探数据库查询应用系统中的应用,提出了XML技术和数据库技术相结合的数据库快速灵活地查询浏览应用的解决方案。在该方案中,以XML为数据交换标准,采用了多级分布式三层架构,很好地实现了数据库查询应用系统的开放性和可扩充性。     

Efficiently adapting graphical models for selectivity estimation

Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.