PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Practical Comparative Study of Data Mining Query Languages
Hendrik Blockeel, Toon Calders, Elisa Fromont, Bart Goethals and Adriana Prado
In: Inductive Databases and Constraint-Based Data Mining (2010) Springer , pp. 59-78. ISBN 978-1-4419-7737-3


An important motivation for the development of inductive databases and query languages for data mining is that such an approach will increase the flexibility with which data mining can be performed. By integrating data mining more closely into a database querying framework, separate steps such as data preprocessing, data mining, and postprocessing of the results, can all be handled using one query language. In this chapter, we compare 6 existing data mining query languages, all extensions of the standard relational query language SQL, from this point of view: how flexible are they with respect to the tasks they can be used for, and how easily can those tasks be per- formed? We verify whether and how these languages can be used to perform four prototypical data mining tasks in the domain of itemset and associa- tion rule mining, and summarize their stronger and weaker points. Besides offering a comparative evaluation of different data mining query languages, this chapter also provides a motivation for the next chapter, where a deeper integration of data mining into databases is proposed, one that does not rely on the development of a new query language, but where the structure of the database itself is extended.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5730
Deposited By:Elisa Fromont
Deposited On:17 March 2011