22 research outputs found
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
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
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.
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
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)