Parallel Approaches for Data Mining in the Internet of Things Realm

Excerpt

Recent studies show that 2.5 quintillion bytes of data per day are generated, and this is set to explode to 40 yottabytes by 2020. Much of this data is and will be generated from Internet of Things (IoT) devices and sensors. Billions of connected objects use the Internet every day capturing and producing data to be processed and excessive data is making great troubles to human beings. Data coming from IoT systems have a great diversity of types and therefore it becomes difficult to process by using state-of-the-art data processing techniques or traditional data processing platforms. In this scenario, the IoT realm requires more efficient and scalable data processing methods and, at the same time, raises additional challenges on data processing, mining and analytics. The first impression of the data produced by the IoT is the volume, a major challenge is how to process and cope with the massive amounts of rapidly scaling data deriving from sensors and devices. Collecting, storing and processing this data efficiently and quickly is vital to producing actionable, real-time insights. As it is well known, parallel and distributed computing have emerged in the last decades as well-developed research areas in computer science and information technology. In this perspective, parallel and distributed computing techniques can be opportunely exploited in order to solve large-scale problems and process the data coming from the IoT paradigm. …