Abstract

Data mining is increasingly performed by people who are not computer scientists or professional programmers. It is often done as an iterative process involving multiple ad-hoc tasks, as well as data pre- and post-processing, all of which must be executed over large databases. In order to make data mining more accessible, it is critical to provide a simple, easy-to-use language that allows the user to specify ad-hoc data processing, model construction, and model manipulation. Simultaneously, it is necessary for the underlying system to scale up to large datasets. Unfortunately, while each of these requirements can be satisfied, individually, by existing systems, no system fully satisfies all criteria.

In this paper, we present a system called Splash to fill this void. Splash supports an extended relational data model and SQL query language, which allows for the natural integration of statistical modeling and ad-hoc data processing. It also supports a novel representatives operator to help explain models using a limited number of examples. We have developed a prototype implementation of Splash. Our experimental study indicates that it scales well to large input datasets. Further, to demonstrate the simplicity of the language, we conducted a case study using Splash to perform a series of exploratory analyses using network log data. Our study indicates that the query-based interface is simpler than a common data mining software package, and it often requires less programming effort to use.