Information Management and PSM Evaluation System

Information Management and PSM Evaluation System is a system developed to replace the existing system at the Faculty of Computing. The existing system at the Faculty of Computing is a manual system in which all the evaluation process still utilises paper forms. PSM is divided into two phases; PSM1 and PSM2 and each phase has a different form for evaluation. This process is seen to be less systematic and imposes much time on the evaluator, coordinator and supervisor who are also lecturers. Information Management and PSM Evaluation System is designed to automate information management and evaluation of PSM to keep the information in the database. The scope of these systems focuses on admin, supervisor, evaluator and coordinator bound to PSM1 and PSM2. Some of the functions that can be operated on the system are evaluation, updating PSM students’ information and generating reports. The chosen methodology is an Evolutionary Prototype which needs are taken care of the system during modifications. Requirements established during the interview is employed to form a common structure with the essential basic functions of the system. Therefore, Information Management and PSM Evaluation System was developed to automate the manual system to increase efficiency. The system was developed using ASP.net technology and Microsoft Visual Studio 2010 and has been successfully completed within the specified time.

Management of Data Streams for Large-Scale Data Mining

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.

Management of Data Streams for Large-Scale Data Mining

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.