Integrated Tool for Assisted Predictive Analytics

Abstract

Organizations use predictive analysis in CRM (customer relationship management) applications for marketing campaigns, sales, and customer services, in manufacturing to predict the location and rate of machine failures, in financial services to forecast financial market trends, predict the impact of new policies, laws and regulations on businesses and markets, etc. Predictive analytics is a business process which consists of collecting the data, developing accurate predictive model and making the analytics available to the business users through a data visualization application. The reliability of a business process can be increased by modeling the process and formally verifying its correctness. Formal verification of business process models aims checking for process correctness and business compliance. Typically, data warehouses are usually used to build mathematical models that capture important trends. Predictive models are the foundation of predictive analytics and involve advanced machine learning techniques to dig into data and allow analysts to make predictions. We propose to extend the capability of the Oracle database with the automatic verification of business processes by adapting and embedding our Alternating-time Temporal Logic (ATL) model checking tool. The ATL model checker tool will be used to guide the business users in the process of data preparation (build, test, and scoring data).