Mathemtical model of composite objects and its application fororganizing engineering databases

The authors introduce a clustering concept called component aggregation which considers assemblies having the same types of parts as equivalent objects. The notion of equivalent objects is used to develop a mathematical model of composite objects. It is shown that the set of equivalence classes of objects form a Boolean algebra whose minterms represent the objects that are not considered composite at the current viewing level. The algebraic structure of composite objects serves as a basis for developing a technique for organizing composite objects and supporting materialization of explosion views. The technique provides a clustering mechanism which partitions the database into meaningful and application-oriented clusters, and allows any desired explosion view to be materialized using a minimal set of stored views. A simplified relational database for design data and a set of frequent access patterns in design applications are outlined and used to demonstrate the benefits of database organizations based on the mathematical model of composite objects     

Integrating relational and object-oriented database systems using a metaclass concept

This paper presents a specific approach of integrating a relational database system into a federated database system. The underlying database integration process consist of three steps: first, the external database systems have to be connected to the integrated database system environment and the external data models have to be mapped into a canonical data model. This step is often called syntactic transformation including structural enrichment and leads to component schemas for each external DBMS. Second, the resulting schemas from the first step are used to construct export schemas which are then integrated into global, individual schemas or views in the third step. In this paper we focus on the first step for relational databases, i.e., the connection of a relational database system and the mapping of the relational model into a canonical data model. We take POSTGRES as the relational database system and the object-oriented federated database system VODAK as the integration platform which provides the open, object-oriented data model as the canonical data model for the integration. We show different variations of mapping the relational model. By exploiting the metaclass concept provided by VML, the modelling language of VODAK, we show how to tailor VML such that the canonical data model meets the requirements of integrating POSTGRES into the global database system VODAK in an efficient way.     

A Methodology for Structured Database Decomposition

We present a methodology for structured database decomposition based on the relational data model. It is argued that in the distributed database environment, structured database decomposition is attractive both for efficiency and for database security considerations. Techniques for parallel processing and hashed access of structurally decomposed database are presented. Techniques for structured database decomposition to support multiple user views are also described. Structured database decomposition is most advantageous in a query only database environment with stable user views, although dynamic updates can also be handled using techniques described in this paper.     

Compositional algebra for interactive data access

An important issue for the success of a database application is the effectiveness of its interface. Frequently a relevant part of the programming effort is devoted to the generation of interfaces. The visual programming environments reduce only partly this effort, and in particular, things become more complicated when data coming from different sources (different views in the same database or even views from different databases or systems) are to be related and must cooperate in the data navigation and manipulation task. To overcome this problem we present a new database access paradigm based on an algebra on the domain of computational abstractions called “services” which include both dimensions: the data access computation and the user interaction. This means that the interaction is not implemented by using separated constructs as happens for traditional computational models; on the contrary, as the interaction is an integral part of the service paradigm, the user interaction is computed starting from the declarative specification of the data access itself. The combination of services in a service expression through the operators defined by the service algebra makes it possible to generate cooperating user interfaces for complex data navigation and manipulation. Through algebraic properties, which hold both from the data and user interface point of view, the service expressions can be simplified and optimized guaranteeing their initial semantics. The paper shows the application of the service algebra to the relational environment by means of a simple extension to SQL. Finally, the paper describes a tool based on a three tier architecture and on Java technology for developing and distributing services in Web environment. Services and combination of services expressed with the service algebra are automatically translated into Java objects, allowing the rapid development of platform independent data access services.     

Improving the performance and functionality of Mondrian open‐source OLAP systems

For a long time, the design of relational databases has focused on the optimization of atomic transactions (insert, select, update or delete). Currently, relational databases store tactical information of data warehouses, mainly for select‐like operations. However, the database paradigm has evolved, and nowadays on‐line analytical processing (OLAP) systems handle strategic information for further analysis. These systems enable fast, interactive and consistent information analysis of data warehouses, including shared calculations and allocations. OLAP and data warehouses jointly allow multidimensional data views, turning raw data into knowledge. OLAP allows ‘slice and dice’ navigation and a top‐down perspective of data hierarchies. In this paper, we describe our experience in the migration from a large relational database management system to an OLAP system on top of a relational layer (the data warehouse), and the resulting contributions in open‐source ROLAP optimization. Existing open‐source ROLAP technologies rely on summarized tables with materialized aggregate views to improve system performance (in terms of response time). The design and maintenance of those tables are cumbersome. Instead, we intensively exploit cache memory, where key data reside, yielding low response times. A cold start process brings summarized data from the relational database to cache memory, subsequently reducing the response time. We ensure concurrent access to the summarized data, as well as consistency when the relational database updates data. We also improve the OLAP functionality, by providing new features for automating the creation of calculated members. This makes it possible to define new measures on the fly using virtual dimensions, without re‐designing the multidimensional cube. We have chosen the XML/A de facto standard for service provision. Copyright © 2008 John Wiley & Sons, Ltd.     

Online aggregation with tight error bounds in dynamic environments

OLAP is a category of database technology that allows analysts to gain insight into the aggregation of data by enabling them to gain access to a variety of different views of the information contained in a database. It is very important to provide analysts with guaranteed error bounds for approximate results to aggregation queries in enterprise applications such as decision support systems. We propose a general method of providing tight error bounds for approximate results to OLAP range-sum queries. We perform an extensive experiment on diverse data sets and examine the effectiveness of the proposed method for various data cube dimensions and query sizes.     

A relational–XML data warehouse for data aggregation with SQL and XQuery

Integrating information from multiple data sources is becoming increasingly important for enterprises that partner with other companies for e‐commerce. However, companies have their internal business applications deployed on diverse platforms and no standard solution for integrating information from these sources exists. To support business intelligence query activities, it is useful to build a data warehouse on top of middleware that aggregates the data obtained from various heterogeneous database systems. Online analytical processing (OLAP) can then be used to provide fast access to materialized views from the data warehouse. Since extensible markup language (XML) documents are a common data representation standard on the Internet and relational tables are commonly used for production data, OLAP must handle both relational and XML data. SQL and XQuery can be used to process the materialized relational and XML data cubes created from the aggregated data. This paper shows how to handle the two kinds of data cubes from a relational–XML data warehouse using extract, transformation and loading. Copyright © 2008 John Wiley & Sons, Ltd.     

Query processing over object views of relational data

This paper presents an approach to object view management for relational databases. Such a view mechanism makes it possible for users to transparently work with data in a relational database as if it was stored in an object-oriented (OO) database. A query against the object view is translated to one or several queries against the relational database. The results of these queries are then processed to form an answer to the initial query. The approach is not restricted to a ‘pure’ object view mechanism for the relational data, since the object view can also store its own data and methods. Therefore it must be possible to process queries that combine local data residing in the object view with data retrieved from the relational database. We discuss the key issues when object views of relational databases are developed, namely: how to map relational structures to sub-type/supertype hierarchies in the view, how to represent relational database access in OO query plans, how to provide the concept of object identity in the view, how to handle the fact that the extension of types in the view depends on the state of the relational database, and how to process and optimize queries against the object view. The results are based on experiences from a running prototype implementation. Edited by: M.T. ?zsu. Received April 12, 1995 / Accepted April 22, 1996     

Materialized Sample Views for Database Approximation

We consider the problem of creating a sample view of a database table. A sample view is an indexed materialized view that permits efficient sampling from an arbitrary range query over the view. Such "sample views" are very useful in applications that require random samples from a database: approximate query processing, online aggregation, data mining, and randomized algorithms are a few examples. Our core technical contribution is a new file organization called the appendability, combinability, and exponentiality (ACE) tree that is suitable for organizing and indexing a sample view. One of the most important aspects of the ACE tree is that it supports online random sampling from the view. That is, at all times, the set of records returned by the ACE tree constitutes a statistically random sample of the database records satisfying the relational selection predicate over the view. Our paper presents experimental results that demonstrate the utility of the ACE tree.     

一个开放可扩展的SDAI实现系统

STEP标准数据存取界面－SDAI为应用程序提供了独立于数据存储的STEP数据访问界面，SDAI允许访问不同的数据存储系统，有不同的实现语言联编方式，如果对每一种实用语言和数据存储系统单独实现SDAI，其工作量将是巨大的，该文选择系数据库作为SDAI数据存储系统，C语言作为实现语言，实现系统独立于不同的关系数据库系统，并且为不同语言的联编提供了一个统一的开发平台，提高了系统的开放性和可扩展性，为STEP在企业信息集成中的应用提供了核心操作，STEP建模语言EXPRESS，关系数据库，SDAI实现语言在模式表示上的不匹配是系统实现要解决的主要问题，该文从数据字典，STEP数据的字储与访问等方面阐明了系统实现时没模式之间的匹配过程。     

Storage Support for Multidimensional Discrete Data in Multimedia Databases

Multidimensional discrete data (MDD) is enormously important for multimedia applications since diverse types of such data are used simultaneously by any multimedia application. However, multidimensional discrete data is not adequately supported by most existing database systems. In this paper we discuss the major management requirements imposed by such data on multimedia database management systems both at the conceptual level and at the internal level. At the internal level, the spatial structure of the data must also be preserved and special emphasis must be given to the efficient spatial access to subrectangles of very large data of arbitrary dimensionality. Our major contribution is the presentation of a Storage Manager for this kind of data which preserves the spatial nature of the data at the internal level of the database management system and provides efficient spatial access. This Storage Manager is based on an innovative combination of the subdivision of data in multidimensional tiles and of a spatial index to access those tiles. Furthermore, it supports advanced registration data. The lower level modules of the Storage Manager are responsible for the access to the data stored in secondary storage. The implementation of these modules can rely on any secondary storage access system to access data on disk. The Storage Manager was implemented using the functionality of a relational database system. The implementation of this relational Storage Manager is presented.     

Constraint Preserving Transformation from Relational Schema to XML Schema

XML has become the standard for publishing and exchanging data on the Web. However, most business data is managed and will remain to be managed by relational database management systems. As such, there is an increasing need to efficiently and accurately publish relational data as XML documents for Internet-based applications. One way to publish relational data is to provide virtual XML documents for relational data via an XML schema which is transformed from the underlying relational database schema such that users can access the relational database through the XML schema. In this paper, we discuss issues in transforming a relational database schema into the corresponding XML schema. We aim to preserve all integrity constraints defined in a relational database schema, to achieve high level of nesting and to avoid introducing data redundancy in the transformed XML schema. In the paper, we first propose a basic transformation algorithm which introduces no data redundancy, then we improve the algorithm by exploring further nesting of the transformed XML schema.     

Ultrawrap: SPARQL execution on relational data

The Semantic Web’s promise of web-wide data integration requires the inclusion of legacy relational databases,¹ i.e. the execution of SPARQL queries on RDF representation of the legacy relational data. We explore a hypothesis: existing commercial relational databases already subsume the algorithms and optimizations needed to support effective SPARQL execution on existing relationally stored data. The experiment is embodied in a system, Ultrawrap, that encodes a logical representation of the database as an RDF graph using SQL views and a simple syntactic translation of SPARQL queries to SQL queries on those views. Thus, in the course of executing a SPARQL query, the SQL optimizer uses the SQL views that represent a mapping of relational data to RDF, and optimizes its execution. In contrast, related research is predicated on incorporating optimizing transforms as part of the SPARQL to SQL translation, and/or executing some of the queries outside the underlying SQL environment.Ultrawrap is evaluated using two existing benchmark suites that derive their RDF data from relational data through a Relational Database to RDF (RDB2RDF) Direct Mapping and repeated for each of the three major relational database management systems. Empirical analysis reveals two existing relational query optimizations that, if applied to the SQL produced from a simple syntactic translations of SPARQL queries (with bound predicate arguments) to SQL, consistently yield query execution time that is comparable to that of SQL queries written directly for the relational representation of the data. The analysis further reveals the two optimizations are not uniquely required to achieve a successful wrapper system. The evidence suggests effective wrappers will be those that are designed to complement the optimizer of the target database.     

一种通用聚合算法在统计工作中的应用研究

目前政府统计部门正在积极建设统计数据库,统计数据库具有典型的多维特征。同时,统计业务人员经常要对各种级别的统计数据尤其是汇总级的数据进行所谓的即席查询(Ad-hoc query)。为了实现这一点,大多数统计查询和分析系统依赖于关系数据库平台本身提供的OLAP功能和接口,造成应用系统与数据库系统紧耦合反而降低了应用系统的可移植性。文中根据统计数据库多维特征和OLAP聚合操作的原理提出一种跨数据库系统平台的通用聚合算法,并成功应用于统计工作中,有效地解决了上述问题。     

Recommending XML physical designs for XML databases

Database systems employ physical structures such as indexes and materialized views to improve query performance, potentially by orders of magnitude. It is therefore important for a database administrator to choose the appropriate configuration of these physical structures for a given database. XML database systems are increasingly being used to manage semi-structured data, and XML support has been added to commercial database systems. In this paper, we address the problem of automatic physical design for XML databases, which is the process of automatically selecting the best set of physical structures for a database and a query workload. We focus on recommending two types of physical structures: XML indexes and relational materialized views of XML data. We present a design advisor for recommending XML indexes, one for recommending materialized views, and an integrated design advisor that recommends both indexes and materialized views. A key characteristic of our advisors is that they are tightly coupled with the query optimizer of the database system, and they rely on the optimizer for enumerating and evaluating physical designs. We have implemented our advisors in a prototype version of IBM DB2 V9, and we experimentally demonstrate the effectiveness of their recommendations using this implementation.     

Evolution of data management

Computers can now store all forms of information: records, documents, images, sound recordings, videos, scientific data, and many new data formats. Society has made great strides in capturing, storing, managing, analyzing, and visualizing this data. These tasks are generically called data management. This article sketches the evolution of data management systems. There have been six distinct phases in data management. Initially, data was manually processed. The next step used punched-card equipment and electromechanical machines to sort and tabulate millions of records. The third phase stored data on magnetic tape and used stored-program computers to perform batch processing on sequential files. The fourth phase introduced the concept of a database schema and on-line navigational access to the data. The fifth step automated access to relational databases and added distributed and client server processing. We are now in the early stages of sixth-generation systems that store richer data types, notably documents, images, voice, and video data. These sixth-generation systems are the storage engines for the emerging Internet and intranets. Early data management systems automated traditional information processing. Today they allow fast, reliable, and secure access to globally distributed data. Tomorrow's systems will access and summarize richer forms of data. It is argued that multimedia databases will be a cornerstone of cyberspace     

Extending the ODMG standard with views

Views are an important functionality provided by the relational database systems. However, commercial object-oriented database systems do not support a view mechanism because defining the semantics of views in the context of an object-oriented model is more difficult than in the relational model. Indeed, views are not included in the ODMG standard. In this paper, we present a proposal aimed at including views in the ODMG, by extending the object model and the object definition language (ODL). We consider object-oriented views as having the same functionality as relational views. Views are included in the object model in such a way that (i) views make a new kind of data type definition, just as are classes, interfaces and literals, (ii) an IS-VIEW relationship is introduced in order to specify the derivation of a view from its base class, and (iii) a view instance preserves the identity of its base instance. A view can import attributes, relationships and operations from its base class, and it can also add new operations, derived attributes and derived relationships. The extent of the view is defined by an object query language (OQL) predicate. We also describe a C++ binding showing the practicability of the proposed model.     

基于语义视图的SPARQL-SQL查询转换方法

针对油气井工程领域关系数据库,提出一种基于语义视图的SPARQL-SQL查询转换方法.该方法采用特定本体描述数据源关系模式,通过RDF三元组形式,将关系表定义为领域本体之上的语义查询视图,从而建立数据源与本体之间的映射关系,并根据语义映射信息,将提交的SPARQL语句进行解析与查询重写,转换为面向关系数据源的SQL语句.通过实现油气井虚拟数据中心,验证了该方法的可行性与有效性,并获得了良好的应用效果.     

Semi-supervised Collective Classification in Multi-attribute Network Data

Multi-attribute network refers to network data with multiple attribute views and relational view. Although semi-supervised collective classification has been investigated extensively, little attention is received for such kind of network data. In this paper, we aim to study and solve the semi-supervised learning problem for multi-attribute networks. There are two important challenges: (1) how to extract effective information from the rich multi-attribute and relational information; (2) how to make use of unlabeled data in the network. We propose a new generative model with network regularization, called MARL, which addresses the two challenges. In the approach, a generative model based on the probabilistic latent semantic analysis method is developed to leverage attribute information, and a network regularizer is incorporated to smooth label probability with relational information and unlabeled data. Comprehensive experiments on various data sets have been conducted to demonstrate the effectiveness of the proposed MARL, and the results reveal that our approach outperforms existing collective classification methods and multi-view classification methods in terms of accuracy.     

Abstract-driven pattern discovery in databases

The problem of discovering interesting patterns in large volumes of data is studied. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. A pattern is defined as a deductive rule expressed in user-defined terms that has a degree of uncertainty associated with it. Methods are presented for discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user