RILCA: Collecting and Analyzing User-Behavior Information in Instant Search Using Relational DBMS

Abstract

An instant-search engine computes answers immediately as a user types a query character by character. In this paper, we study how to systematically collect information about user behaviors when they interact with an instant search engine, especially in a real-time environment. We present a solution, called RILCA, which uses front-end techniques to keep track of rich information about user activities. This information provides more insights than methods based on traditional Web servers such as Apache. We store the log records in a relational DBMS system, and leverage the existing powerful capabilities of the DBMS system to analyze the log records efficiently. We study how to use a dashboard to monitor and analyze log records in real time. We conducted experiments on real data sets collected from two live systems to show the benefits and efficiency of these techniques.