Information Fusion for Improving Decision Making in Big Data Applications
The danger involved in oil and gas industry allied to, the not rare, world spread accidents have promoted the concerns toward achieving and demonstrating good performance with regard to occupational, health and safety (OHS) issues. There are international OHS compliance policies that must be followed by any petroleum company to be able to operate. One of these policies is the register, at the spur of the moment, any anomaly that occurs during operation including environmental accidents, human accidents or, even, simply noncompliance behavior of the work force. In addition to register the anomaly, the entire process of analyzing, finding the root cause and solving the problem must get registered. As a consequence, an increasingly huge database has been created in many companies with these reports. The data may or may not be structured, but for sure is composed of different sources and types. For instance, whenever needed, data from the workforce will be registered side by side with data from the involved equipment. Human manipulation of this huge and diversified data is a difficult, or even impossible, task. We present a data fusion architecture coupled with a machine learning layer for providing abstractions and inferences over the data. The idea is to prove that our approach allows analysts to infer the relevant root cause and effect relations that underlie the domain. We developed a system according to our model and used with data from a petroleum company. In addition to prove the feasibility of our approach we have compared with state of the art data mining techniques. Results have shown the efficiency in terms of accuracy and recall of our approach.
Resource Management for Big Data Platforms