Web 2.0-based Collaborative Multicriteria Spatial Decision Support System: A Case Study of Human-Computer Interaction Patterns

The integration of GIS and Multicriteria Decision Analysis (MCDA) capabilities into the Web 2.0 platform offers an effective Multicriteria Spatial Decision Support System (MC-SDSS) with which to involve the public, or a particular group of individuals, in collaborative spatial decision making. Understanding how decision makers acquire and integrate decision-related information within the Web 2.0-based collaborative MC-SDSS has been one of the major concerns of MC-SDSS designers for a long time. This study focuses on examining human-computer interaction patterns (information acquisition behavior) within the Web 2.0-based MC-SDSS environment. It reports the results of an experimental study that investigated the effects of task complexity, information aids, and decision modes on information acquisition metrics and their relations. The research involved three major steps: (1) developing a Web 2.0-based analytic-deliberative MC-SDSS for parking site selection in Tehran, Iran to analyze human-computer interaction patterns, (2) conducting experiments using this system and collecting the human-computer interaction data, and (3) analyzing the log data to detect the human-computer interaction patterns (information acquisition metrics). Using task complexity, decision aid, and decision mode as the independent factors, and the information acquisition metrics as the dependent variables, the study adopted a repeated-measures experimental design (or within-subjects design) to test the relevant hypotheses. Task complexity was manipulated in terms of the number of alternatives and attributes at four levels. At each level of task complexity, the participants carried out the decision making process in two different GIS-MCDA modes: individual and group modes. The decision information was conveyed to participants through common map and decision table information structures. The map and table were used, respectively, for the exploration of the geographic (or decision) and criterion outcome spaces. The study employed a process-tracing method to directly monitor and record the decision makers’ activities during the experiments. The data on the decision makers’ activities were recorded as Web-based event logs using a database logging technique. Concerningiv task complexity effects, the results of the study suggest that an increase in task complexity results in a decrease in the proportion of information searched and proportion of attribute ranges searched, as well as an increase in the variability of information searched per attribute. This finding implies that as task complexity increases decision makers use a more non-compensatory strategy. Regarding the decision mode effects, it was found that the two decision modes are significantly different in terms of: (1) the proportion of information search, (2) the proportion of attribute ranges examined, (3) the variability of information search per attribute, (4) the total time spent acquiring the information in the decision table, and (5) the average time spent acquiring each piece of information. Regarding the effect of the information aids (map and decision table) on the information acquisition behavior, the findings suggest that, in both of the decision modes, there is a significant difference between information acquisition using the map and decision table. The results show that decision participants have a higher number of moves and spend more time on the decision table than map. The study presented in this dissertation has implications for formulating behavioral theories in the spatial decision context and practical implications for the development of MC-SDSS. Specifically, the findings provide a new perspective on the use of decision support aids, and important clues for designers to develop an appropriate user-centered Web-based collaborative MC-SDSS. The study’s implications can advance public participatory planning and allow for more informed and democratic land-use allocation decisions

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management