Collaborative Analytics with Genetic Programming for Workflow Recommendation

Formulation of appropriate data analytics workflows requires intricate knowledge and rich experiences of data analytics experts. This problem is further compounded by continuous advancement and improvement in analytical algorithms. In this paper, a generic non-domain specific solution for the creation of appropriate workflows targeted at supervised learning problems is proposed. Our adaptive workflow recommendation engine based on collaborative analytics matches analytics needs with relevant workflows in repository. It is capable of picking workflows with better performance as compared to randomly selected workflows. The recommendation engine is now augmented by a workflow optimizer that applies genetic programming to further improve the recommended workflows through iterative evolution, leading to better alternative workflows. This unique Collaborative Analytics Recommender System is tested on seven UCI benchmark datasets. It is shown that the final workflows produced by the system could closely approximate, in terms of accuracy, the best workflows that analytics experts could possibly design.