Building a Data Warehouse for Twitter Stream Exploration
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Decision-making in the field of academic planning involves extensive analysis of large data volumes originating from multiple systems. With the many new technology application areas evolving from the domain of electrical engineering, computer engineering, and computer science, deans and department chairs must ensure that new specializations and programs are adequately supported. Academic workload management is concerned with distributing teaching resources to support the university's educational framework adequately (faculties, degrees, courses, admission policies, teaching workload, etc.). This work presents a methodology for assessing educational capacity and planning its distribution and utilization, implemented as a decision support system allowing simulation and evaluation of various proposals and scenarios. The system integrates input data from relevant sources into an autonomous data warehouse. Graphical client front-end ensures adequate output presentation to the decision-makers by revealing significant details and dependencies in the data. Applying the system as an "on-the-fly" decision-support utility by the policy-makers leads to significant acceleration of planning procedures, deepens the insight into the data and the underlying methodology, and, consequently, provides for more efficient academic administration