Defining and implementing domains with multiple types using mesodata modelling techniques

The integration of data from different sources often leads to the adoption of schemata that entail a loss of information in respect of one or more of the data sets being combined. The coercion of data to conform to the type of the unified attribute is one of the major reasons for this information loss. We argue that for maximal information retention it would be useful to be able to define attributes over domains capable of accommodating multiple types, that is, domains that potentially allow an attribute to take its values from more than one base type. Mesodata is a concept that provides an intermediate conceptual layer between the definition of a relational structure and that of attribute definition to aid the specification of complex domain structures within the database. Mesodata modelling techniques involve the use of data types and operations for common data structures defined in the mesodata layer to facilitate accurate modelling of complex data domains, so that any commonality between similar domains used for different purposes can be exploited. This paper shows how the mesodata concept can be extended to facilitate the creation of domains defined over multiple base types, and also allow the same set of base values to be used for domains with different semantics. Using an example domain containing values representing three different types of incomplete knowledge about the data item (coarse granularity, vague terms, or intervals) we show how operations and data structures for types already existing within the mesodata can simplify the task of developing a new intelligent domain.Sydney, NS

Health intelligence: Discovering the process model using process mining by constructing Start-to-End patient journeys

Archived with the publisher's permission. Copyright © 2014, Australian Computer Society, Inc. This paper appeared at the Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2014), Auckland, New Zealand. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 153. J. Warren and K. Gray, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.Australian Public Hospitals are continually engaged in various process improvement activities to improve patient care and to improve hospital efficiency as the demand for service intensifies. As a consequence there are many initiatives within the health sector focusing on gaining insight into the underlying health processes which are assessed for compliance with specified Key Performance Indicators (KPIs). Process Mining is classified as a Business Intelligence (BI) tool. The aim of process mining activities is to gain insight into the underlying process or processes. The fundamental element needed for process mining is a historical event log of a process. Generally, these event logs are easily sourced from Process Aware Information Systems (PAIS). Simulation is widely used by hospitals as a tool to study the complex hospital setting and for prediction. Generally, simulation models are constructed by ‘hand’. This paper presents a novel way of deriving event logs for health data in the absence of PAIS. The constructed event log is then used as an input for process mining activities taking advantage of existing process mining algorithms aiding the discovery of knowledge of the underlying processes which leads to Health Intelligence (HI). One such output of process mining activity, presented in this paper, is the discovery of process model for simulation using the derived event log as an input for process mining by constructing start-to-end patient journey. The study was undertaken using data from Flinders Medical Centre to gain insight into patient journeys from the point of admission to the Emergency Department (ED) until the patient is discharged from the hospital.

Towards active conceptual modelling for sudden events

There are a number of issues for information systems which are required to collect data urgently that are not well accommodated by current conceptual modelling methodologies and as a result the modelling step (and the use of databases) is often omitted. Such issues include the fact that • the number of instances for each entity are relatively low resulting in data definition taking a disproportionate amount of effort, • the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data, • they undergo regular structural change and are thus subject to information loss as a result of changes to the schema’s information capacity, • finally, the structure of the information is likely to be only partially known or for which there are multiple, perhaps contradictory, competing hypotheses as to the underlying structure. This paper presents the Low Instance-to-Entity Ratio (LItER) Model, which attempts to circumvent some of the problems encountered by these types of application and to provide a platform and modelling technique to handle rapidly occurring phenomena. The two-part LItER modelling process possesses an overarching architecture which provides hypothesis, knowledge base and ontology support together with a common conceptual schema. This allows data to be stored immediately and for a more refined conceptual schema to be developed later. LItER modelling also aims to facilitate later translation to EER, ORM and UML models and the use of (a form of) SQL. Moreover, an additional benefit of the model is that it provides a partial solution to a number of outstanding issues in current conceptual modelling systems.Sydney, NS

Discovering itemset interactions

Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering item-set interactions, a task which cannot be undertaken by current association mining techniques

Mesodata Modelling

Saarbrucken German

Reduce, reuse, recycle: practical approaches to schema integration, evolution and versioning

Heidelberg, German

Collecting and conserving code: challenges and strategies

The collection and conservation of code is still in its infancy in Australia. Even where coded items do exist, they are almost completely invisible within local cultural institutions and archives. Born-digital heritage faces unique risks - the degradation of hardware and software, obsolete operating systems, and intellectual property laws that restrict digital preservation activities. Too often, governments and cultural institutions either fail to recognise the precarious situation of historic code-based media, or are not able to respond in an appropriate fashion, due to a lack of resources, know-how, or sometimes, will. After outlining some of the challenges - for institutions and researchers - of developing collections of games and other software, this article will detail two current research initiatives. The Play It Again project is conducting research into the largely unknown histories of 1980s game development in Australia and New Zealand, ensuring that local titles are documented, preserved and make it into national collections. The Australasian Heritage Software Database seeks to: draw together existing knowledge about locally-developed software, marshal a network of supporters, and develop an enabling discourse that supports research into histories of software and digital preservation. Whilst these projects do not provide complete solutions by any means, a local discourse about the importance of collecting and conserving code is emerging.‘Play It Again: Creating a Playable History of Australasian Digital Games, for Industry, Community and Research Purposes’ is supported under the Australian Research Council’s Linkage Projects funding scheme (project number LP120100218)

How lemmings on wheels can make a u-turn through social inclusion and democracy

Londo