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In the area of e-commerce one of the key success factors for companies is personalization and its integration into customer-related processes. The data and datasources necessary are broadly available due to e.g. the increasing role of computer assisted process management, falling prices for computing power and storage and the proliferation of data warehouses. In order to use the data a variety of tools and frameworks are available. This development has been recently coined by the term "Big Data".

Nevertheless the generation of possibly useful information out of large data sets is only the first step. In order for a company to create a measurable business value from the utilization of "Big Data" it is also necessary to reintegrate the results of the data mining process seamlessly and in real time back into its core business workflows. SQUIDD attempts to fills this gap by combining a framework for distributable, horizontally scalable machine learning algorithms with the provision of data mining functionality over web services tied together in a modular architecture.

[2] C HAPMAN, Pete ; C LINTON, Julian ; K ERBER, Randy ; K HABAZA, Thomas ; R EINARTZ, Thomas ; S HEARER, Colin ; W IRTH, Rudiger: CRISP-DM 1.0 Step-by-step data mining guide / The CRISP-DM consortium. Version: August 2000. http://www.crisp-dm.org/ CRISPWP-0800.pdf. 2000. – Forschungsbericht