Data Summarization and Modelling

Abstract

Data analytics find a myriad of applications in all walks of human endeavour. The modern world is characterized by huge amounts of data emerging from all human-made systems, including sensors, communication devices, signal processors and conditioners. The plethora of data originating from several sources often have to be cleaned and filtered to make it useful for further processing. In this paper, we discuss a very important domain in Big Data processing-namely, Data Summarization and its modelling. Big Data refers to the fairly huge chunks of data originating from several real-world sources, including those from all communication systems (including voice, text-data, images, and video), sensing devices (for example, those from sensors in a wired or wireless sensor network), digital data processors and several other natural sources including those from galactic sources from other planets and stars outside our galaxy. To effectively process the data, even with the almost seamless processing power of modern-day digital computers, one has to resort to Data Summarization. Modelling is another key component in visualizing the impact of such pre-processed data to arrive at meaningful conclusions on the processes which are being studied.