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It is well known that XML has been widely adopted for its flexible and self-describing nature. However, relational data will continue to co-exist with XML for several different reasons one of which is the high cost of transferring everything to XML. In this context, data designers face the problem of modeling both relational and XML data within an integrated environment. This chapter highlights important questions on hybrid XML-relational database design and discusses use cases, requirements, and deficiencies in existing design methodologies especially in the light of data and schema evolution. The authors’ analysis results in several design guidelines and a series of challenges to be addressed by future research.

The wide availability of commodity multi-core systems presents an opportunity to address the latency issues that have plaqued XML query processing. However, simply executing multiple XML queries over multiple cores merely addresses the throughput issue: intra-query parallelization is needed to exploit multiple processing cores for better latency. Toward this effort, this paper investigates the parallelization of individual XPath queries over shared-address space multi-core processors. Much previous work on parallelizing XPath in a distributed setting failed to exploit the shared memory parallelism of multi-core systems. We propose a novel, end-to-end parallelization framework that determines the optimal way of parallelizing an XML query. This decision is based on a statistics-based approach that relies both on the query specifics and the data statistics. At each stage of the parallelization process, we evaluate three alternative approaches, namely, data-, query-, and hybrid-partitioning. For a given XPath query, our parallelization algorithm uses XML statistics to estimate the relative efficiencies of these different alternatives and find an optimal parallel XPath processing plan. Our experiments using well-known XML documents validate our parallel cost model and optimization framework, and demonstrate that it is possible to accelerate XPath processing using commodity multi-core systems.

In this study, we present experiences of parallelizing XPath queries using the Xalan XPath engine on shared-address space multi-core systems. For our evaluation, we consider a scenario where an XPath processor uses multiple threads to concurrently navigate and execute individual XPath queries on a shared XML document. Given the constraints of the XML execution and data models, we propose three strategies for parallelizing individual XPath queries: Data partitioning, Query partitioning, and Hybrid (query and data) partitioning. We experimentally evaluated these strategies on an x86 Linux multi-core system using a set of XPath queries, invoked on a variety of XML documents using the Xalan XPath APIs. Experimental results demonstrate that the proposed parallelization strategies work very effectively in practice; for a majority of XPath queries under evaluation, the execution performance scaled linearly as the number of threads was increased. Results also revealed the pros and cons of the different parallelization strategies for different XPath query patterns.

Remote monitoring of patients' biometric data streams offers the possibility to physicians to extend and improve their services to chronically ill patients who are away from medical institutions. This emerging technology is a promising way to address important aspects of the cost issues that most health care systems are experiencing. In order to fulfill its potential, several challenges need to be overcome. First, the data collected needs to be filtered and annotated intelligently to help physicians cope with and navigate the large amount of patient sensor data received as a result of large scale remote health monitoring deployments. Secondly, efficient stream persistence and query mechanisms for these data need to be designed to satisfy health care regulations and help physicians track patient health histories accurately and efficiently. In this paper, we concentrate on the second challenge. We leverage emerging hybrid relational-XML database management systems to design a storage sub-system for remote health monitoring. We evaluate this approach by performing series of performance tests to assess the ability of the proposed system to handle the huge amount of biometric data streams requiring persistence.

In response to the widespread use of the XML format for document representation and message exchange, major database vendors support XML in terms of persistence, querying and indexing. Specifically, the recently released IBM DB2 9 (for Linux, Unix and Windows) is a hybrid data server with optimized management of both XML and relational data. With the new option of storing and querying XML in a relational DBMS, data architects face the the decision of what portion of their data to persist as XML and what portion as relational data. This problem has not been addressed yet and represents a serious need in the industry. Hence, this paper describes ReXSA, a schema advisor tool that is being prototyped for IBM DB2 9. ReXSA proposes candidate database schemas given an information model of the enterprise data. It has the advantage of considering qualitative properties of the information model such as reuse, evolution and performance profiles for deciding how to persist the data. Finally, we show the viability and practicality of ReXSA by applying it to custom and real usecases.

In XML databases, new schema versions may be released as frequently as once every two weeks. This poster describes a taxonomy of changes for XML schema evolution. It examines the impact of those changes on schema validation and query evaluation. Based on that study, it proposes guidelines for XML schema evolution and for writing queries in such a way that they continue to operate as expected across evolving schemas.

Relational database management systems are constantly being extended and augmented to accommodate data in different domains. Recently, with the increasing use of ontology in various applications, the need to support ontology, especially the related inferencing operation, in DBMS has become more concrete and urgent. However, manipulating knowledge along with relational data in DBMSs is not a trivial undertaking due to the mismatch in data models. In this paper, we introduce a framework for managing relational data and hierarchical domain knowledge together. Our framework persists taxonomies contained in ontologies by leveraging XML support in hybrid relational-XML DBMSs (e.g., IBM's DB2 v9) and rewrites ontology-based semantic matching queries using the industry-standard query languages, SQL/XML and XQuery. Compared with previous approaches, our approach does not materialize transitive closures of ontological relationships to support inferencing. Consequently, our method has wide applicability and good performance.

Electronic commerce is emerging as a major application area for database systems. A large number of e-commerce stores provide electronic product catalogs that allow customers to search products of interest and store owners to manage various product information. Due to the constant schema evolution and the sparsity of e-commerce data, most commercial e-commerce systems use the so-called vertical schema for data storage. However, query processing for data stored using vertical schema is extremely inefficient because current RDBMSs, especially its cost-based query optimizer, are specifically designed to deal with traditional horizontal schemas. In this paper, we show that e-catalog management can be naturally supported in IBM's system RX, the first DBMS that truly supports both XML and relational data in their native forms. By leveraging on system RX's hybrid nature, we present a novel solution for storing, managing, and querying e-catalog data. In addition to traditional queries, we show that our solution supports semantic queries as well. Our solution does not require a separate query optimization layer, because query optimization is handled within the hybrid DBMS engine itself

The extensible mark-up language (XML) is gaining widespread use as a format for data exchange and storage on the World Wide Web. Queries over XML data require accurate selectivity estimation of path expressions to optimize query execution plans. Selectivity estimation of XML path expression is usually done based on summary statistics about the structure of the underlying XML repository. All previous methods require an off-line scan of the XML repository to collect the statistics. In this paper, we propose XPathLearner, a method for estimating selectivity of the most commonly used types of path expressions without looking at the XML data. XPathLearner gathers and refines the statistics using query feedback in an on-line manner and is especially suited to queries in Internet scale applications since the underlying XML repository is either inaccessible or too large to be scanned in its entirety. Besides the on-line property, our method also has two other novel features: (a) XPathLearner is workload-aware in collecting the statistics and thus can be more accurate than the more costly off-line method under tight memory constraints, and (b) XPathLearner automatically adjusts the statistics using query feedback when the underlying XML data change. We show empirically the estimation accuracy of our method using several real data sets.