An XML-Based Approach to Publishing and Querying the History of Databases

There is much current interest in publishing and viewing databases as XML documents. The general benefits of this approach follow from the popularity of XML and the tool set available for visualizing and processing information encoded in this universal standard. In this paper, we explore the additional and unique benefits achieved by this approach on temporal database applications. We show that XML with XQuery can provide surprisingly effective solutions to the problem of supporting historical queries on past content of database relations and their evolution. Indeed, using XML, the histories of database relations can be naturally represented by temporally grouped data models. Thus, we identify mappings from relations to XML that are most conducive to modeling and querying database histories, and show that temporal queries that would be difficult to express in SQL can be easily expressed in standard XQuery. This approach is very general, insofar as it can be used to store the version history of arbitrary documents and, for relational databases, it also supports queries on the evolution of their schema. Then, we turn to the problem of supporting efficiently the storage and the querying of relational table histories. We present an experimental study of the pros and cons of using native XML databases, versus using traditional databases, where the XML-represented histories are supported as views on the historical tables.     

S2CX: From relational data via SQL/XML to (Un-)Compressed XML

The gap between storing data in relational databases and transferring data in form of XML has been closed e.g. by SQL/XML queries that generate XML data out of relational data sources. However, only few relational database systems support the evaluation of SQL/XML queries. And even in those systems supporting SQL/XML, the evaluation of such queries is quite slow compared to the evaluation of SQL queries. In this paper, we present S2CX, an approach that allows to efficiently evaluate SQL/XML queries on any relational database system, no matter whether it supports SQL/XML or not. As a result to an SQL/XML query, S2CX supports different output formats ranging from plain XML to different compressed XML representations including a succinct encoding of XML data, schema-aware compressed XML to grammar compressed XML. In many cases, S2CX produces compressed XML as a result to an SQL/XML query even faster than the evaluation of SQL/XML queries into non-compressed XML as provided by Oracle 11 g and by DB2. Furthermore, our approach to query evaluation scales better, i.e., the larger the dataset, the faster is our approach compared to SQL/XML query evaluation in Oracle 11 g and in DB2.     

A foundation for conventional and temporal query optimizationaddressing duplicates and ordering

Most real-world databases contain substantial amounts of time-referenced, or temporal, data. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query representation, optimization, and processing mechanisms must be provided. This paper presents a foundation for query optimization that integrates conventional and temporal query optimization and is suitable for both conventional DBMS architectures and ones where the temporal support is obtained via a layer on top of a conventional DBMS. This foundation captures duplicates and ordering for all queries, as well as coalescing for temporal queries, thus generalizing all existing approaches known to the authors. It includes a temporally extended relational algebra to which SQL and temporal SQL queries may be mapped, six types of algebraic equivalences, concrete query transformation rules that obey different equivalences, a procedure for determining which types of transformation rules are applicable for optimizing a query, and a query plan enumeration algorithm     

Storing and querying fuzzy XML data in relational databases

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data management has been extensively investigated in various database management systems. Currently, introducing native support for XML data in relational database management systems (RDBMs) has attracted considerable interest with a view to leveraging the powerful and reliable data management services provided by RDBMs. Although there is a rich literature on XML-to-relational storage, none of the existing solutions satisfactorily addresses the problem of storing fuzzy XML data in RDBMs. In this paper, we study the methodology of storing and querying fuzzy XML data in relational databases. In particular, we present an edge-based approach to shred fuzzy XML data into relational data. The unique feature of our approach is that no schema information is required for our data storage. On this basis, we present a generic approach to translate path expression queries into SQL for processing XML queries.     

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.     

Continuous authentication on relational streams

According to the database outsourcing model, a data owner delegates database functionality to a third-party service provider, which answers queries received from clients. Authenticated query processing enables the clients to verify the correctness of query results. Despite the abundance of methods for authenticated processing in conventional databases, there is limited work on outsourced data streams. Stream environments pose new challenges such as the need for fast structure updating, support for continuous query processing and authentication, and provision for temporal completeness. Specifically, in addition to the correctness of individual results, the client must be able to verify that there are no missing results in between data updates. This paper presents a comprehensive set of methods covering relational streams. We first describe REF, a technique that achieves correctness and temporal completeness but incurs false transmissions, i.e., the provider has to inform the clients whenever there is a data update, even if their results are not affected. Then, we propose CADS, which minimizes the processing and transmission overhead through an elaborate indexing scheme and a virtual caching mechanism. In addition, we present an analytical study to determine the optimal indexing granularity, and extend CADS for the case that the data distribution changes over time. Finally, we evaluate the effectiveness of our techniques through extensive experiments.     

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.     

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.     

Qualitative and quantitative temporal constraints and relationaldatabases: theory, architecture, and applications

Many different applications in different areas need to deal with both: databases, in order to take into account large amounts of structured data; and quantitative and qualitative temporal constraints about such data. We propose an approach that extends: temporal databases and artificial intelligence temporal reasoning techniques and integrate them in order to face such a need. Regarding temporal reasoning, we consider some results that we proved recently about efficient query answering in the Simple Temporal Problem framework and we extend them in order to deal with partitioned sets of constraints and to support relational database operations. Regarding databases, we extend the relational model in order to consider also qualitative and quantitative temporal constraints both in the data (data expressiveness) and in the queries (query expressiveness). We then propose a modular architecture integrating a relational database with a temporal reasoner. We also consider classes of applications that fit into our approach and consider patient management in a hospital as an example     

Toward Practical Query Evaluation for Constraint Databases

Linear constraint databases (LCDBs) extend relational databases to include linear arithmetic constraints in both relations and queries. A LCDB can also be viewed as a powerful extension of linear programming (LP) where the system of constraints is generalized to a database containing constraints and the objective function is generalized to a relational query containing constraints. Our major concern is query optimization in LCDBs. Traditional database approaches are not adequate for combination with LP technology. Instead, we propose a new query optimization approach, based on statistical estimations and iterated trials of potentially better evaluation plans. The resulting algorithms are not only effective on LCDBs, but also applicable to existing query languages. A number of specific constraint algebra algorithms are also developed: select-project-join for two relations, constraint sort-join and constraint multi-join.     

Using a relational database for scalable XML search

XML is a flexible and powerful tool that enables information and security sharing in heterogeneous environments. Scalable technologies are needed to effectively manage the growing volumes of XML data. A wide variety of methods exist for storing and searching XML data; the two most common techniques are conventional tree-based and relational approaches. Tree-based approaches represent XML as a tree and use indexes and path join algorithms to process queries. In contrast, the relational approach utilizes the power of a mature relational database to store and search XML. This method relationally maps XML queries to SQL and reconstructs the XML from the database results. To date, the limited acceptance of the relational approach to XML processing is due to the need to redesign the relational schema each time a new XML hierarchy is defined. We, in contrast, describe a relational approach that is fixed schema eliminating the need for schema redesign at the expense of potentially longer runtimes. We show, however, that these potentially longer runtimes are still significantly shorter than those of the tree approach. We use a popular XML benchmark to compare the scalability of both approaches. We generated large collections of heterogeneous XML documents ranging in size from 500 MB to 8 GB using the XBench benchmark. The scalability of each method was measured by running XML queries that cover a wide range of XML search features on each collection. We measure the scalability of each method over different query features as the collection size increases. In addition, we examine the performance of each method as the result size and the number of predicates increase. Our results show that our relational approach provides a scalable approach to XML retrieval by leveraging existing relational database optimizations. Furthermore, we show that the relational approach typically outperforms the tree-based approach while scaling consistently over all collections studied.

The aditi deductive database system

Deductive databases generalize relational databases by providing support for recursive views and non-atomic data. Aditi is a deductive system based on the client-server model; it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses relational technology for efficiency in the management of disk-based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.     

基于DOM的XML文档到关系数据库的数据转换方法

随着Internet的普及和Web技术的快速发展,XML正迅速成为事实上的数据表示和交换标准,大量XML数据的涌现出来,为了实现XML数据的快速查询和有效的数据交换,需要将XML文档数据转存到关系数据库中。该文介绍了基于DOM的XML文档到关系数据库的数据转换方法。     

TQuery: a context-sensitive temporal query language.

Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.     

移动对象XML数据库的存储与索引

如何对移动对象的XML数据记录进行快速的查找,关键在于合理地存储模型与索引结构。为了减少时空条件索引时的文件I/O操作,提出一个移动对象XML数据存储模型（时空XML存储模型）,基于这个模型给出了通过一定时空条件对XML数据记录进行聚集的ATS（Append Track node to Spatial node）算法。针对3DR树的缺点与时态条件在移动对象索引中的重要性,提出了HSTR（Hashing-Spatio-Temporal-Rtree）与HC3DR（Hashing-Changing-3DRtree）两种复合索引结构,能够有效地支持涉及时空条件的查询。实验结果表明,时空XML存储模型与两种索引提高了查询效率。     

ERL: Logic for entity-relationship databases

We develop a logic for entity-relationship databases, ERL, that is a generalization of database logic. ERL provides advantages to the ER model much as FOL (first-order logic) does to the relational model: a uniform language for expressing database schema, integrity constraints, and database manipulation; clearly defined semantics; the capability to express database transformations; and deductive capabilities. We propose three query languages for ER databases called ERRC, ERSQL, and ERQBE, which are generalizations of the relational calculus, SQL, and QBE, respectively. We use example queries and updates to demonstrate the capabilities of these languages. We apply database transformations to introduce the notion of views and to show that both ERRC and ERSQL are relationally complete.Research sponsored in part by the National Science Foundation under grant IRI-8921951 and by Towson State University.     

GIS权籍空间数据的存储与查询

探讨了基于关系型数据库存储XML权籍对象的方法和技术。分析了数据库存储XML数据的类型,论述了运用XML存储和管理权籍对象的可行性。基于GML规范建立了权籍对象的概念模型和逻辑模型,并根据XML权籍对象之间的关系,给出了将权籍对象转换到关系型数据库的方法,基于此实现了权籍对象空间关系的查询与应用。     

XML materialized views and schema evolution in VIREX

Web-based databases are gaining increased popularity. This has positively influenced the availability of structured and semi-structured databases for access by a variety of users ranging from professionals to naive users. The number of users accessing online databases will continue to increase if the visual tools connected to web-based databases are flexible and user-friendly enough to meet the expectations of naive users and professionals. Further, XML is accepted as the standard for platform independent data exchange. This motivated for the development of the conversion tools between structured databases and XML. Realizing that such a need has not been well handled by the available tools, including Clio from IBM, we developed VIREX as a visual tool for converting relational databases into XML, and since then has been empowered with further capabilities to manipulate the produced XML schema including the maintenance of materialized views and schema evolution functions. VIREX provides an interactive approach for querying and integrating relational databases to produce XML documents and the corresponding XML schema(s). VIREX supports VRXQuery as a visual naive users-oriented query language that allows users to specify queries and define views directly on the interactive diagram as a sequence of mouse clicks with minimum keyboard input. As the query result, VIREX displays on the screen the XML schema that satisfies the specified characteristics and generates colored (easy to read) XML document(s). The main contribution described in this paper is the novel approach for turning query results into materialized views which are maintained to remain consistent with the underlying database. VIREX supports deferred update of XML views by keeping an ordered summary of the necessary and sufficient information required for the process. Each view has a corresponding marker in the ordered summary to indicate the start of the information to be reflected onto the view when it is accessed. When a view is accessed, its marker moves to the head of the list to mark for the next update. In addition, VIREX supports some basic schema evolution functions include renaming, adding and dropping of elements and attributes, among others. The supported schema evolution functions add flexibility to the view maintenance and materialization process.     

Producing Interoperable Queries for Relational and Object-Oriented Databases

In this paper, we develop techniques to produce interoperable queries with object and relational databases. A user poses a local query in a local query language, against a local object or relational schema. We transparently produce appropriate queries with respect to a remote target object or relational schema, corresponding to some remote database which contains data relevant to the user's query. Mapping knowledge to resolve representational heterogeneities in local and remote schemas is expressed in a canonical representation, CRmapping, and is independent of the particular data model. A canonical representation CRquery is also used to resolve heterogeneities of query languages. A set of heterogeneous transformation algorithms define the appropriate transformations from the local queries to the remote queries. The use of canonical representations (CR) allows us to represent queries independent of the particular query language, and to resolve representational conflicts in a uniform manner, independent of models and query languages.     

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.