Smart process analytics for predictive modeling

Supporting Smart Interactions with Predictive Analytics

Predictive analytics, which has grown out of Business Intelligence (BI), employs knowledge discovery on existing data to predict or recommend future actions. It differs from BI in that BI focuses on past performance and the discovery of trends while predictive analytics forecasts behaviour and results in order to guide specific decisions. Predictive analytics is used to analyze large amounts of...

Data-intensive analytics for predictive modeling

The Data Abstraction Research Group was formed in the early 1990s, to bring focus to the work of the Mathematical Sciences Department in the emerging area of knowledge discovery and data mining (KD & DM). Most activities in this group have been performed in the technical area of predictive modeling, roughly at the intersection of machine learning, statistical modeling, and database technology. ...

Reinforcement Learning for Predictive Analytics in Smart Cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the case...

Multi-state PLS based Data-driven Predictive Modeling for Continuous Process Analytics APPROVED BY SUPERVISING COMMITTEE:

Framework for a Smart Process Data Analytics Platform

The fusion of information from disparate sources of data is the key step in devising strategies for a smart analytics platform. In the context of the application of analytics in the process industry, this paper provides a framework for seamless integration of information from process and alarm databases complimented with process connectivity information. The discovery of information from such d...