ABSTRACT Exploiting Native XML Indexing Techniques for XML Retrieval in Relational Database Systems

In XML retrieval, two distinct approaches have been established and pursued without much cross-fertilization taking place so far. On the one hand, native XML databases tailored to the semistructured data model have received considerable attention, and a wealth of index structures, join algorithms, tree encodings and query rewriting techniques for XML have been proposed. On the other hand, the question how to make XML fit the relational data model has been studied in great detail, giving rise to a multitude of storage schemes for XML in relational database systems (RDBSs). In this paper we examine how native XML indexing techniques can boost the retrieval of XML stored in an RDBS. We present the Relational CADG (RCADG), an adaptation of several native indexing approaches to the relational model, and show how it supports the evaluation of a clean formal language of conjunctive XML queries. Unlike relational storage schemes for XML, the RCADG largely preserves the underlying tree structure of the data in the RDBS, thus addressing several open problems known from the literature. Experiments show that the RCADG accelerates retrieval by up to two or even three orders of magnitude compared to both native and relational approaches

An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks

Healthcare informatics still lacks wide-scale adoption of intelligent decision support methods, despite continuous increases in computing power and methodological advances in scalable computation and machine learning, over recent decades. The potential has long been recognised, as evidenced in the literature of the domain, which is extensively reviewed. The thesis identifies and explores key barriers to adoption of clinical decision support, through computational experiments encompassing a number of technical platforms. Building on previous research, it implements and tests a novel platform architecture capable of processing and reasoning with clinical data. The key components of this platform are the now widely implemented openEHR electronic health record specifications and Bayesian Belief Networks. Substantial software implementations are used to explore the integration of these components, guided and supplemented by input from clinician experts and using clinical data models derived in hospital settings at Moorfields Eye Hospital. Data quality and quantity issues are highlighted. Insights thus gained are used to design and build a novel graph-based representation and processing model for the clinical data, based on the openEHR specifications. The approach can be implemented using diverse modern database and platform technologies. Computational experiments with the platform, using data from two clinical domains – a preliminary study with published thyroid metabolism data and a substantial study of cataract surgery – explore fundamental barriers that must be overcome in intelligent healthcare systems developments for clinical settings. These have often been neglected, or misunderstood as implementation procedures of secondary importance. The results confirm that the methods developed have the potential to overcome a number of these barriers. The findings lead to proposals for improvements to the openEHR specifications, in the context of machine learning applications, and in particular for integrating them with Bayesian Networks. The thesis concludes with a roadmap for future research, building on progress and findings to date

Structural Summaries as a Core Technology for Efficient XML Retrieval

The Extensible Markup Language (XML) is extremely popular as a generic markup language for text documents with an explicit hierarchical structure. The different types of XML data found in today’s document repositories, digital libraries, intranets and on the web range from flat text with little meaningful structure to be queried, over truly semistructured data with a rich and often irregular structure, to rather rigidly structured documents with little text that would also fit a relational database system (RDBS). Not surprisingly, various ways of storing and retrieving XML data have been investigated, including native XML systems, relational engines based on RDBSs, and hybrid combinations thereof. Over the years a number of native XML indexing techniques have emerged, the most important ones being structure indices and labelling schemes. Structure indices represent the document schema (i.e., the hierarchy of nested tags that occur in the documents) in a compact central data structure so that structural query constraints (e.g., path or tree patterns) can be efficiently matched without accessing the documents. Labelling schemes specify ways to assign unique identifiers, or labels, to the document nodes so that specific relations (e.g., parent/child) between individual nodes can be inferred from their labels alone in a decentralized manner, again without accessing the documents themselves. Since both structure indices and labelling schemes provide compact approximate views on the document structure, we collectively refer to them as structural summaries. This work presents new structural summaries that enable highly efficient and scalable XML retrieval in native, relational and hybrid systems. The key contribution of our approach is threefold. (1) We introduce BIRD, a very efficient and expressive labelling scheme for XML, and the CADG, a combined text and structure index, and combine them as two complementary building blocks of the same XML retrieval system. (2) We propose a purely relational variant of BIRD and the CADG, called RCADG, that is extremely fast and scales up to large document collections. (3) We present the RCADG Cache, a hybrid system that enhances the RCADG with incremental query evaluation based on cached results of earlier queries. The RCADG Cache exploits schema information in the RCADG to detect cached query results that can supply some or all matches to a new query with little or no computational and I/O effort. A main-memory cache index ensures that reusable query results are quickly retrieved even in a huge cache. Our work shows that structural summaries significantly improve the efficiency and scalability of XML retrieval systems in several ways. Former relational approaches have largely ignored structural summaries. The RCADG shows that these native indexing techniques are equally effective for XML retrieval in RDBSs. BIRD, unlike some other labelling schemes, achieves high retrieval performance with a fairly modest storage overhead. To the best of our knowledge, the RCADG Cache is the only approach to take advantage of structural summaries for effectively detecting query containment or overlap. Moreover, no other XML cache we know of exploits intermediate results that are produced as a by-product during the evaluation from scratch. These are valuable cache contents that increase the effectiveness of the cache at no extra computational cost. Extensive experiments quantify the practical benefit of all of the proposed techniques, which amounts to a performance gain of several orders of magnitude compared to various other approaches