Enhanced Data Processing Using Positive Negative Association Mining on AJAX Data

Abstract

Knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. (1) Association rule mining is a data mining process used widely in traditional databases to find the positive association rules. Association rules are created by analyzing data for frequent patterns and using the criteria support and confidence to identify the most important relationships. However, there are some other challenging rule mining topics like negative association rule mining. Organizations want to concentrate on their own business and outsource the rest of their work. This approach is named ''Software as a Service Concept'' and provides lots of benefits to data owner, but, at the same time, brings out some security problems. In this research, a rule mining approach has been proposed that provides efficient and secure solution to positive and negative association rule computation on AJAX data streams in software as a service (SaaS) concept. It is important to note that SaaS typically encapsulates enterprise as opposed to consumer- oriented web-hosted software. Software as a service (SaaS) is a way of delivering applications over the Internet—as a service. Instead of installing and maintaining software, you simply access it via the Internet, freeing yourself from complex software and hardware management. SaaS applications are sometimes called Web-based software, on-demand software, or hosted software. By using (3) AJAX, we get the search result in the form of semantic. Index Terms: Association rules mining, Ajax data stream, horizontal tree approach, Apriori algorithm