Study on LBS for Characterization and Analysis of Big Data Benchmarks

In the past few years, most organizations are gradually diverting their applications and services to Cloud. This is because Cloud paradigm enables (a) on-demand accessed and (b) large data processing for their applications and users on Internet anywhere in the world. The rapid growth of urbanization in developed and developing countries leads a new emerging concept called Urban Computing, one of the application domains that is rapidly deployed to the Cloud. More precisely, in the concept of Urban Computing, sensors, vehicles, devices, buildings, and roads are used as a component to probe city dynamics. Their data representation is widely available including GPS traces of vehicles. However, their applications are more towards data processing and storage hungry, which is due to their data increment in large volume starts from few dozen of TB (Tera Bytes) to thousands of PT (Peta Bytes) (i.e. Big Data). To increase the development and the assessment of the applications such as LBS (Location Based Services), a benchmark of Big Data is urgently needed. This research is a novel research on LBS to characterize and analyze the Big Data benchmarks. We focused on map-matching, which is being used as pre-processing step in many LBS applications. In this preliminary work, this paper also describes current status of Big Data benchmarks and our future direction