2. Machine Learning and Knowledge Discovery

The field of data mining has made significant advances in recent years. Because of its ability to solve complex problems, data mining has been applied in diverse fields related to engineering, biological science, social media, medicine, and business intelligence. The primary objective for most of the applications is to characterize patterns in a complex stream of data. These patterns are then coupled with knowledge discovery and decision making. In the Internet age, information gathering and dynamic analysis of spatiotemporal data are key to innovation and developing better products and processes. When datasets are large and complex, it becomes difficult to process and analyze patterns using traditional statistical methods. Big data are data collected in volumes so large, and forms so complex and unstructured, that they cannot be handled using standard database management systems, such as DBMS and RDBMS. The emerging challenges associated with big data include dealing not only with increased volume, but also the wide variety and complexity of the data streams that need to be extracted, transformed, analyzed, stored, and visualized. Big data analysis uses inferential statistics to draw conclusions related to dependencies, behaviors, and predictions from large sets of data with low information density that are subject to random variations. Such systems are expected to model knowledge discovery in a format that produces reasonable answers when applied across a wide range of situations. The characteristics of big data are as follows: