Path dictionary: a new access method for query processing inobject-oriented databases

We present a new access method, called the path dictionary index (PDI) method, for supporting nested queries on object-oriented databases. PDI supports object traversal and associative search, respectively, with a path dictionary and a set of attribute indexes built on top of the path dictionary. We discuss issues on indexing and query processing in object-oriented databases; describe the operations of the new mechanism; develop cost models for its storage overhead and query and update costs; and compare the new mechanism to the path index method. The result shows that the path dictionary index method is significantly better than the path index method over a wide range of parameters in terms of retrieval and update costs and that the storage overhead grows slowly with the number of indexed attributes     

Data storage practices and query processing in XML databases: A survey

With the rapid emergence of XML as a data exchange standard over the Web, storing and querying XML data have become critical issues. The two main approaches to storing XML data are (1) to employ traditional storage such as relational database, object-oriented database and so on, and (2) to create an XML-specific native storage. The storage representation affects the efficiency of query processing. In this paper, firstly, we review the two approaches for storing XML data. Secondly, we review various query optimization techniques such as indexing, labeling and join algorithms to enhance query processing in both approaches. Next, we suggest an indexing classification scheme and discuss some of the current trends in indexing methods, which indicate a clear shift towards hybrid indexing.     

An empirical investigation of factors influencing object-oriented database querying

Object-oriented databases (OODBs) provide an effective means for capturing complex data and semantic relationships underlying many real-world database applications. Because users' interactions with databases have increased significantly in today's era of client–server computing, it is important to examine users' ability to interact with such databases. We investigated a number of factors that potentially affect performance in writing queries on an OODB. First, we evaluated the utility of graphical and textual schemas associated with emerging OODBs from the perspective of database querying. Second, we examined the use of two different strategies (navigation and join) that could be used in writing OODB queries. Third, we examined a number of factors that potentially contribute to the complexity of an OODB query.Our exploratory study examined the performance of 20 graduate students in an experiment in which each participant wrote queries for two problems, one using a graphical OODB schema and the other a textual OODB schema. The participants had no prior exposure to the object-oriented data model. We found that there was no difference in query writing performance (either accuracy or time) using the graphical and textual schemas. Examination of query strategy revealed that a significant number of participants used a join strategy, rather than the navigation strategy that matches the database structure. Use of the join strategy resulted in significantly less accurate and slower query writing than did the navigation strategy. From the viewpoint of complexity, the number of objects referenced in a query, the number of starting points in the from clause, and the presence of special operators influenced both the accuracy and time of query writing.     

基于面向对象XML数据的索引机制

XML正在迅速成为Internet上信息表示和数据交换的重要标准.而面向对象的特征具有很强的建模能力,将面向对象的概念引入到XML可以提高XML模式语言的建模能力.而现有的索引模式都不支持面向对象XML数据的查询,由此提出了一种基于面向对象XML数据的OOCtree（Object-Oriented compact tree）索引模式,它是一棵包含组级和元素级的两级双向树.组级提供简洁的结构概要和继承信息,可以在查询早期阶段裁剪大量的搜索空间;元素级提供详细的孩子父亲连接关系,可以快速地访问某元素的父亲,极大地提高了查询处理效率.     

Using genetic algorithms to work out index configurations for the class-hierarchy indexing in object databases

Efficient indexing on a class hierarchy is essential for the achievement of high performance in query evaluation for object databases. In this paper, we present a practical indexing scheme, Partition Index Configuration Scheme (PINS), which provides good index configurations for any real database environment. PINS considers the distribution of key values, as well as query patterns such as query frequency on each class. PINS can easily be applied to any database system, since it uses the B⁺-tree structure. We develop a cost model and, through experiments, demonstrate the performance of the proposed policy over various class hierarchies.     

一种新的基于划分的结构连接算法

有效的结构连接是XML查询处理的关键。目前，大部分结构连接算法由于需要临时排序、建立索引或存在数据复制及I/O问题，大大降低了执行效率。该文在分析比较现有结构连接算法的基础上，提出了一种新的基于划分的结构连接算法。该算法不需要排序或建立索引，通过栈的机制解决了数据复制问题，并充分考虑内存缓冲提高了I/O性能。实验分析表明该算法具有良好的查询性能。     

n-Gram/2L索引结构的存储与时间优化算法

对分词检索算法n-Gram/2L的索引结构作了改进,在第二级倒排表中加入对文章标识的索引,提出一种基于Zigzag的分词检索算法n-Gram/2LZ（n-Gram/2L on Zigzag join）。在对数据量较大的文章进行检索和索引时,该算法在保留原有算法特性的基础上进一步减少了索引冗余,降低了索引的存储量,同时对查询算法的优化降低了查询时的系统开销,并且减少索引中记录访问次数,提高了查询效率。     

Object query optimization through detecting independent subqueries

The problem of query optimization in object-oriented databases is addressed. We follow the Stack-Based Approach to query languages, which employs the naming-scoping-binding paradigm of programming languages rather than traditional database concepts such as relational/object algebras or calculi. The classical environment stack is a semantic basis for definitions of object query operators, such as selection, projection/navigation, dependent join, and quantifiers. We describe a general object data model and define a formalized OQL-like query language SBQL. Optimization by rewriting concerns queries containing so-called independent subqueries. It consists in detecting them and then factoring outside loops implied by query operators. The idea is based on the formal static analysis of scoping rules and binding names occurring in a query. It is more general than the classical pushing selections/projections before joins.     

An interface between object-oriented databases and relational databases

Currently relational databases are widely used, while object-oriented databases are emerging as a new generation of database technology. This paper presents a methodology to provide effective sharing of information in object-oriented databases and relational databases. The object-oriented data model is selected as a common data model to build an integrated view of the diverse databases. An object-oriented query language is used as a standard query language. A method is developed to transform a relational data definition to an equivalent object-oriented data definition and to integrate local data definitions. Two distributed query processing methods are derived. One is for general queries and the other for a special class of restricted queries. Using the methods developed, it is possible to access distributed object-oriented databases and relational databases such that the locations and the structural differences of the databases are transparent to users.     

Functional-join processing

Inter-object references are one of the key concepts of object-relational and object-oriented database systems. In this work, we investigate alternative techniques to implement inter-object references and make the best use of them in query processing, i.e., in evaluating functional joins. We will give a comprehensive overview and performance evaluation of all known techniques for simple (single-valued) as well as multi-valued functional joins. Furthermore, we will describe special order-preserving\/ functional-join techniques that are particularly attractive for decision support queries that require ordered results. While most of the presentation of this paper is focused on object-relational and object-oriented database systems, some of the results can also be applied to plain relational databases because index nested-loop joins\/ along key/foreign-key relationships, as they are frequently found in relational databases, are just one particular way to execute a functional join. Received February 28, 1999 / Accepted September 27, 1999     

一种复杂XML Twig查询处理算法

根据复杂Twig查询的特点,充分利用DTD资源,建立一种基于DTD的索引结构,采用Dewey编码方法对XML文档进行统一编码,并提出一种基于DTD的复杂Twig查询处理算法STwigScan;查询时,通过扫描DTD索引,将复杂Twig查询定位在条件节点以及目标节点上,有效的减少查询处理算法的处理规模;实验证明,STwigScan算法处理规模比较小,查询效率比较高.     

A framework for join pattern indexing in intelligent databasesystems

In intelligent database systems, knowledge directed inference often derives large amounts of data, and the efficiency of query processing in these systems depends upon how the derived data is maintained. This paper focuses on situations where the rule is conditional on a join of multiple data objects (relations) and the rule-derived data are materialized to reduce the overall query processing costs. We develop an indexing technique based on a unique construct called join pattern relation. Several pattern redundancy reduction methods are also introduced to minimize the overhead cost of join indexing     

Efficient processing of spatial joins with DOT-based indexing

A spatial join is a query that searches for a set of object pairs satisfying a given spatial relationship from a database. It is one of the most costly queries, and thus requires an efficient processing algorithm that fully exploits the features of the underlying spatial indexes. In our earlier work, we devised a fairly effective algorithm for processing spatial joins with double transformation (DOT) indexing, which is one of several spatial indexing schemes. However, the algorithm is restricted to only the one-dimensional cases. In this paper, we extend the algorithm for the two-dimensional cases, which are general in Geographic Information Systems (GIS) applications. We first extend DOT to two-dimensional original space. Next, we propose an efficient algorithm for processing range queries using extended DOT. This algorithm employs the quarter division technique and the tri-quarter division technique devised by analyzing the regularity of the space-filling curve used in DOT. This greatly reduces the number of space transformation operations. We then propose a novel spatial join algorithm based on this range query processing algorithm. In processing a spatial join, we determine the access order of disk pages so that we can minimize the number of disk accesses. We show the superiority of the proposed method by extensive experiments using data sets of various distributions and sizes. The experimental results reveal that the proposed method improves the performance of spatial join processing up to three times in comparison with the widely-used R-tree-based spatial join method.     

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multi-dimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets ‘with and without’ filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.     

一种复杂多维层次的连接和聚集算法

由于数据仓库中存储着不同粒度、容量巨大的数据记录，所以如何有效地执行联机分析处理(OLAP)查询操作，特别是连接和聚集操作，便成为数据仓库领域的核心问题之一．为此，提出了一种降低连接和聚集操作的新算法(join and aggregation based on the complex multi-dimensional hierarchies，JACMDH)．算法充分考虑了复杂多维层次的特点，在原有的位图连接索引(bitmap join index)的基础上，采用层次联合代理(hierarchy combined surrogate)和预先分组排序的方法，使得复杂的多维层次上的连接和聚集操作转化成事实表上的区域查询，从而在处理多维层次聚集的同时，提高了连接和聚集的效率．算法性能分析和实验数据表明，JACMDH算法和目前流行的算法相比，其性能有显著的提高．     

The RDF-3X engine for scalable management of RDF data

RDF is a data model for schema-free structured information that is gaining momentum in the context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The “pay-as-you-go” nature of RDF and the flexible pattern-matching capabilities of its query language SPARQL entail efficiency and scalability challenges for complex queries including long join paths. This paper presents the RDF-3X engine, an implementation of SPARQL that achieves excellent performance by pursuing a RISC-style architecture with streamlined indexing and query processing. The physical design is identical for all RDF-3X databases regardless of their workloads, and completely eliminates the need for index tuning by exhaustive indexes for all permutations of subject-property-object triples and their binary and unary projections. These indexes are highly compressed, and the query processor can aggressively leverage fast merge joins with excellent performance of processor caches. The query optimizer is able to choose optimal join orders even for complex queries, with a cost model that includes statistical synopses for entire join paths. Although RDF-3X is optimized for queries, it also provides good support for efficient online updates by means of a staging architecture: direct updates to the main database indexes are deferred, and instead applied to compact differential indexes which are later merged into the main indexes in a batched manner. Experimental studies with several large-scale datasets with more than 50 million RDF triples and benchmark queries that include pattern matching, manyway star-joins, and long path-joins demonstrate that RDF-3X can outperform the previously best alternatives by one or two orders of magnitude.     

Graph indexing for spatial data traversal in road map databases

This paper proposes a graph indexing technique for processing constrained spatial queries and discusses the application of such a technique to road map databases where the graph topology is relatively stationary. The fundamental idea of our technique is to augment the original graph with selected augmented links so that query processing cost, especially I/O cost, is minimized. Based on the computational results derived from the probabilistic analysis, we found that the proposed graph indexing technique is a promising approach for significantly reducing costs of spatial queries.Scope and purposeSpatial data is found in geographic information systems where data attributes are associated with nodes and links in directed graphs. Queries on spatial data are generally expensive because of the recursive nature of spatial data traversal. We propose a graph indexing technique to expedite queries on spatial data. The graph index is an instrument for early identification of the relevant nodes and links to the query so that repeated accesses to the same data pages can be eliminated. This paper presents the graph indexing technique in the context of road map databases and shows that the graph indexing technique can improve significantly on the efficiency of constrained queries on spatial data.     

基于面向对象XML索引机制的研究与实现

面向对象的特征具有很强的建模能力,将面向对象的特征引入到XML可以增强XML的描述能力。而现有的索引都不支持面向对象的XML数据的查询,由此基于面向对象XML数据的两种索引模式被提出:基于Ctree的预处理模式和OOCtree模式。这两种模式都提供了面向对象XML数据的继承信息、简洁的结构概要和孩子父亲链接,可以在较短的时间内完成面向对象XML数据的查询;并从算法的建立到查询处理过程以及查询结果的比较来讨论分析了这两种索引模式的性能。     

Design and Query Strategies to Hypermedia Applications

In this paper we propose an object-oriented model for designing hypermedia applications. As the object-oriented paradigm allows complex and user-defined types, nonconventional and nonatomic attributes, we can take advantage of these capabilities, not only for information modelling, but also for providing alternative ways for accessing information.A query language is then presented; it is based on an Object-Oriented Database System query language. It combines features of object-oriented databases queries and primitives for hypermedia navigation. The language offers the possibility of querying both the application-domain information, and allowing the designers to obtain information about the schema of the application.We present some examples of the use of the object-oriented model and the query language.