p-medicine: a medical informatics platform for integrated large scale heterogeneous patient data

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare

X-IM Framework to Overcome Semantic Heterogeneity Across XBRL Filings

Semantic heterogeneity in XBRL precludes the full automation of the business reporting pipeline, a key motivation for the SEC’s XBRL mandate. To mitigate this problem, several approaches leveraging Semantic Web technologies have emerged. While some approaches are promising, their mapping accuracy in resolving semantic heterogeneity must be improved to realize the promised benefits of XBRL. Considering this limitation and following the design science research methodology (DSRM), we develop a novel framework, XBRL indexing-based mapping (X-IM), which takes advantage of the representational model of representation theory to map heterogeneous XBRL elements across diverse XBRL filings. The application of representation theory to the design process informs the use of XBRL label linkbases as a repository of regularities constitutive of the relationships between financial item names and the concepts they describe along a set of equivalent financial terms of interest to investors. The instantiated design artifact is thoroughly evaluated using standard information retrieval metrics. Our experiments show that X-IM significantly outperforms existing methods

Static code analysis of data-driven applications through common lingua and the Semantic Web technologies

Web applications have become increasingly popular due to their potential for businesses' high revenue gain through global reach. Along with these opportunities, also come challenges in terms of Web application security. The increased rise in the number of datadriven applications has also seen an increased rise in their systematic attacks. Cyberattacks exploit Web application vulnerabilities. Attack trends show a major increase in Web application vulnerabilities caused by improper implementation of information-flow control methods and they account for more than 50% of all Web application vulnerabilities found in the year 2013. Static code analysis using methods of information-flow control is a widely acknowledged technique to secure Web applications. Whilst this technique has been found to be both very effective and efficient in finding Web application vulnerabilities, specific tools are highly dependent on the programming language. This thesis leverages Semantic Web technologies in order to offer a common language through source code represented using the Resource Description Framework format, whereby reasoning can be applied to securely test Web applications. In this thesis, we present a framework that extracts source code facts from various programming languages at a variable-level of granularity using Abstract Syntax Trees (ASTs) generated using language grammars and the ANTLR parser generator. The methodology for detecting Web application vulnerabilities implements three phases: entry points identification, tracing information-flow and vulnerability detection using the Jena framework inference mechanism and rules describing patterns of source code. The approach discussed in this thesis is found to be effective and practical in finding Web application vulnerabilities with the limitation that it can only detect patterns that are used as training data or very similar patterns. False positives are caused by limitations of the language grammar, but they do not affect the accuracy of the security vulnerability detection method in identifying the correct Web application vulnerability.Doctor of Philosoph