Solutions for decision support in university management

The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors’ experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.university management, decision support, multidimensional analysis, data warehouse, OLAP

Developing a Research Culture and Scholarship Plan in Information Studies

Information research may take many forms. When the researchers are situated within an information technology faculty, there is a natural orientation towards the technology and the systems that make possible the use of the technology. Despite this, a focus on information itself and its effective utilisation can be achieved in an environment that may otherwise be more concerned with the technology than the information that the technology carries. This focus can contribute to research that has a systems orientation, as well as both foster and be fostered by interdisciplinary work in areas such as education, management and psychology. Here we explain the development of a research program in ‘information use’ within the Socio-technical systems theme of the School of Information Systems at QUT. Our emphasis is on the processes – research supervision, industry linkage, consultancy, grant development, conference contribution and publication - that have advanced the development of the research group. We also provide a summary of research projects in the form of models that are being developed to help illuminate the research frameworks

A Few Implementation Solutions for Business Intelligence

To succeed in the context of a global and dynamic economic environment, the companies must use all the information they have, as efficiently as possible, in order to gain competitive advantages and to consolidate their position on the market. They have to respond quickly to the changes in the business environment and to adapt themselves to the market’s requirements. To achieve these goals, the companies must use modern informatics technologies for data acquiring, storing, accessing and analyzing. These technologies are to be integrated into innovative solutions, such as Business Intelligence systems, which can help managers to better control the business practices and processes, to improve the company’s performance and to conserve it’s competitive advantages.Business Intelligence, competitive advantage, OLAP, data mining, key performance indicators.

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Enterprise engineering using semantic technologies

Modern Enterprises are facing unprecedented challenges in every aspect of their businesses: from marketing research, invention of products, prototyping, production, sales to billing. Innovation is the key to enhancing enterprise performances and knowledge is the main driving force in creating innovation. The identification and effective management of valuable knowledge, however, remains an illusive topic. Knowledge management (KM) techniques, such as enterprise process modelling, have long been recognised for their value and practiced as part of normal business. There are plentiful of KM techniques. However, what is still lacking is a holistic KM approach that enables one to fully connect KM efforts with existing business knowledge and practices already in IT systems, such as organisational memories. To address this problem, we present an integrated three-dimensional KM approach that supports innovative semantics technologies. Its automated formal methods allow us to tap into modern business practices and capitalise on existing knowledge. It closes the knowledge management cycle with user feedback loops. Since we are making use of reliable existing knowledge and methods, new knowledge can be extracted with less effort comparing with another method where new information has to be created from scratch

Technique for improving care integration models

Recent developments in technologies and improved life style have had a positive impact on prolonging human life contributing to the increasing elderly population. As a consequence, many countries (particularly developed ones) started to experience higher proportions of elderly people (over 65). This has consequently generated the need for care for the elderly that is necessitating the integration of health and social care to accommodate their complex needs. A number of modelling methods have been employed to assist those concerned to cope with health and social care but albeit separately. The literatures so far, identified several techniques that have been employed mostly to model the care integration. However, literatures also suggest that there are some challenges still persist when modelling integrated care. It can be argued that these techniques are not capable of handling the complexities associated with the requirements of integrated systems. This paper attempts to prove the reason why despite the fact that many models of integrated care have been developed, problems are still exist. Based on the literatures, the problems exist due to the unsuitable techniques used to model the IC systems as most of the developed models are using single technique. Therefore, new technique to improve the care integration model is suggested

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Critical business intelligence practices to create meta-knowledge

In order to successfully implement strategies and respond to business variations in real-time, business intelligence (BI) systems have been deployed by organisations that assist in focused analytical assessments for execution of critical decisions. Although businesses have realised the significance of BI, few studies have explored their analytical decision-enabling capabilities linked to organisational practices. This study investigates the BI practices critical in creating meta-knowledge successfully for strategy-focused analytical decision-making. First, key BI suppliers are interviewed to develop an understanding of their BI capabilities and current deployment practices. Subsequently, two large BI implementation case studies are conducted to examine their practices in data transformation process. Findings reveal that BI practices are highly context-specific in mapping decisions with data assets. Complimentary static and dynamic evaluations provide holistic intelligence in predicting and prescribing a more complete picture of the enterprise. These practices vary across firms in their effectiveness reflecting numerous challenges and improvement opportunities.Publishe