Heterogeneity and Context in Semantic-Web-Enabled HCLS Systems ⋆

Abstract. The need for semantics preserving integration of complex data has been widely recognized in the healthcare domain. While standards such as Health Level Seven (HL7) have been developed in this direction, they have mostly been applied in limited, controlled environments, still being used incoherently across countries, organizations, or hospitals. In a more mobile and global society, data and knowledge are going to be commonly exchanged between various systems at Web scale. Specialists in this domain have increasingly argued in favor of using Semantic Web technologies for modeling healthcare data in a well formalized way. This paper provides a reality check in how far current Semantic Web standards can tackle interoperability issues arising in such systems driven by the modeling of concrete use cases on exchanging clinical data and practices. Recognizing the insufficiency of standard OWL to model our scenario, we survey theoretical approaches to extend OWL by modularity and context towards handling heterogeneity in Semantic-Webenabled health care and life sciences (HCLS) systems. We come to the conclusion that none of these approaches addresses all of our use case heterogeneity aspects in its entirety. We finally sketch paths on how better approaches could be devised by combining several existing techniques.

Improving Salience Retention and Identification in the Automated Filtering of Event Log Messages

Event log messages are currently the only genuine interface through which computer systems administrators can effectively monitor their systems and assemble a mental perception of system state. The popularisation of the Internet and the accompanying meteoric growth of business-critical systems has resulted in an overwhelming volume of event log messages, channeled through mechanisms whose designers could not have envisaged the scale of the problem. Messages regarding intrusion detection, hardware status, operating system status changes, database tablespaces, and so on, are being produced at the rate of many gigabytes per day for a significant computing environment. Filtering technologies have not been able to keep up. Most messages go unnoticed; no filtering whatsoever is performed on them, at least in part due to the difficulty of implementing and maintaining an effective filtering solution. The most commonly-deployed filtering alternatives rely on regular expressions to match pre-defi ned strings, with 100% accuracy, which can then become ineffective as the code base for the software producing the messages 'drifts' away from those strings. The exactness requirement means all possible failure scenarios must be accurately anticipated and their events catered for with regular expressions, in order to make full use of this technique. Alternatives to regular expressions remain largely academic. Data mining, automated corpus construction, and neural networks, to name the highest-profi le ones, only produce probabilistic results and are either difficult or impossible to alter in any deterministic way. Policies are therefore not supported under these alternatives. This thesis explores a new architecture which utilises rich metadata in order to avoid the burden of message interpretation. The metadata itself is based on an intention to improve end-to-end communication and reduce ambiguity. A simple yet effective filtering scheme is also presented which fi lters log messages through a short and easily-customisable set of rules. With such an architecture, it is envisaged that systems administrators could signi ficantly improve their awareness of their systems while avoiding many of the false-positives and -negatives which plague today's fi ltering solutions