A Survey: Framework to Develop Retrieval Algorithms of Indexing Techniques on Learning Material

Abstract

This paper presents a review on indexing techniques to develop retrieval algorithms framework on learning material. Analysis of the framework was drawn from surveys on literature review and experiment on online campus Learning Materials. Data indexing problem of online learning material occurs as online data comprising many types, formats and words of documents on the system become larger daily. Thus, searching capability for relevant information becomes slower. Further, it becomes more difficult to get the correct information as the learning materials consists of multiple forms of documents such as words, images and videos. The objective of this research is to analyze the existing indexing technique in modeling new retrieval indexing algorithms framework mainly for data mining. Four existing indexing techniques for learning material were reviewed. It is identified that the best used technique are Inverted File, Suffix Array, Suffix Tree and Signature File. Based on the four techniques, characterizations and parameters to enhance a new indexing technique (NIT) was identified and five User Acceptance Tests (UAT) were performed. A framework for NIT was designed and experiments are done on a Campus Learning Material. Identified parameters were successfully inserted in the five test experiments. The conceptual framework was continuously applied to develop NIT for retrieval algorithms on learning material. This research is significant for fast accessing on real life campus learning material system that benefits users and fast retrieval of needed information.