A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

Location analytics offers statistical analysis of any geo- or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers' social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

Evaluating a Pattern-Based Visual Support Approach for Humanitarian Landmine Clearance

Unexploded landmines have severe post-conflict humanitarian repercussions: landmines cost lives, limbs and land. For deminers engaged in humanitarian landmine clearance, metal detectors remain the primary detection tool as more sophisticated technologies fail to get adopted due to restrictive cost, low reliability, and limited robustness. Metal detectors are, however, of limited effectiveness, as modern landmines contain only minimal amounts of metal, making them difficult to distinguish from the ubiquitous but harmless metallic clutter littering post-combat areas. We seek to improve the safety and efficiency of the demining process by developing support tools that will enable deminers to make better decisions using feedback from existing metal detectors. To this end, in this paper we propose and evaluate a novel, pattern-based visual support approach inspired by the documented strategies employed by expert deminers. In our laboratory study, participants provided with a prototype of our support tool were 80% less likely to mistake a mine for harmless clutter. A follow-up study demonstrates the potential of our pattern-based approach to enable peer decision-making support during landmine clearance. Lastly, we identify several design opportunities for further improving deminers' decision making capabilities.Engineering and Applied Science

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

Grounding Design of Instruction: An exploration of the uses of Scientific-Based Research and Theory in the Design of Online Instruction by Faculty in Higher Education

This study was conducted to explore the processes educators in higher education used to ground their design of online instruction using scientific-based research and theory. Literature reviewed suggested reasons educators fail to ground the design of instruction were a lack of formal training in instructional methods, skills, support, and research understanding. The rationale for the study was to (a) increase understanding of how educators use scientific research and theory as a basis in decision-making during design and creation of online instruction,(b)identify best practices, and (c) add to the conversation in the instructional design field. A qualitative case study research design was utilized to interview, review course, and review documents of four participants to capture their viewpoints as to the (a) meaning of; (b) evidence; (c) step by step processes; and (d) problems associated with the processes of grounding the design of online instruction in scientific-based research and theory. Data obtained were analyzed through detailed case description, direct interpretation, cross-case analysis, pattern establishment, and naturalistic generalization. Pedagogy, instructional design, instructional technology, support, and problems emerged as key thematic issues. Findings suggested that although educators were consistent in defining meaning, followed step-by-step processes, and had evidence to support their decisions, they encountered logistical challenges of time, technology and design in the process of using scientific-based research and theory to ground the design of online instruction. The implications for practice from this research were similar to recommendations of other researchers. For this process to be smoother, regular training, peer professional interactions, and support must be present