3 research outputs found
Adding Logical Operators to Tree Pattern Queries on Graph-Structured Data
As data are increasingly modeled as graphs for expressing complex
relationships, the tree pattern query on graph-structured data becomes an
important type of queries in real-world applications. Most practical query
languages, such as XQuery and SPARQL, support logical expressions using
logical-AND/OR/NOT operators to define structural constraints of tree patterns.
In this paper, (1) we propose generalized tree pattern queries (GTPQs) over
graph-structured data, which fully support propositional logic of structural
constraints. (2) We make a thorough study of fundamental problems including
satisfiability, containment and minimization, and analyze the computational
complexity and the decision procedures of these problems. (3) We propose a
compact graph representation of intermediate results and a pruning approach to
reduce the size of intermediate results and the number of join operations --
two factors that often impair the efficiency of traditional algorithms for
evaluating tree pattern queries. (4) We present an efficient algorithm for
evaluating GTPQs using 3-hop as the underlying reachability index. (5)
Experiments on both real-life and synthetic data sets demonstrate the
effectiveness and efficiency of our algorithm, from several times to orders of
magnitude faster than state-of-the-art algorithms in terms of evaluation time,
even for traditional tree pattern queries with only conjunctive operations.Comment: 16 page
Intuitionistic fuzzy XML query matching and rewriting
With the emergence of XML as a standard for data representation, particularly on the web, the need for intelligent query languages that can operate on XML documents with structural heterogeneity has recently gained a lot of popularity. Traditional Information Retrieval and Database approaches have limitations when dealing with such scenarios. Therefore, fuzzy (flexible) approaches have become the predominant. In this thesis, we propose a new approach for approximate XML query matching and rewriting which aims at achieving soft matching of XML queries with XML data sources following different schemas. Unlike traditional querying approaches, which require exact matching, the proposed approach makes use of Intuitionistic Fuzzy Trees to achieve approximate (soft) query matching. Through this new approach, not only the exact answer of a query, but also approximate answers are retrieved. Furthermore, partial results can be obtained from multiple data sources and merged together to produce a single answer to a query. The proposed approach introduced a new tree similarity measure that considers the minimum and maximum degrees of similarity/inclusion of trees that are based on arc matching. New techniques for soft node and arc matching were presented for matching queries against data sources with highly varied structures. A prototype was developed to test the proposed ideas and it proved the ability to achieve approximate matching for pattern queries with a number of XML schemas and rewrite the original query so that it obtain results from the underlying data sources. This has been achieved through several novel algorithms which were tested and proved efficiency and low CPU/Memory cost even for big number of data sources
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 β a preliminary study with published thyroid metabolism data and a
substantial study of cataract surgery β 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