Clustering of Spatial Data with DBSCAN: An Assessment of STARK The everincreasing diffusion rate of mobile devices, able to continuously gather sensing data, creates favorable conditions for the development of smart city infrastructures. In this field the analysis of spatial data plays a pivotal role, due to the relevance they assume in urban scenarios. To satisfy this need, the usage of large distributed computing infrastructures comes into play, supported by efficient frameworks, such as Apache Spark, one of the most relevant platforms to date. However, in order to better take advantage of data and computing resources, it is also necessary to have at disposal flexible and easytouse specialized instruments, granting domain specific capabilities for the analysis of spatial data. This paper focuses on a novel framework for processing of spatial data called STARK, giving an overview of its functionalities and presenting an indepth assessment study of its performances when implementing spatial data clustering, namely DBSCAN. In particular, we focus on two implementations, called MRDBSCAN and NGDBSCAN. Of the latter we introduced an implementation in STARK, in order to enrich the framework and to test its capabilities.

A Rapid Prototyping Approach for High Performance DensityBased Clustering Big Data has significantly increased the dependence of data analytics community on High Performance Computing (HPC) systems. However, efficiently programming an HPC system is still a tedious task requiring specialized skills in parallelization and the use of platformspecific languages as well as mechanisms. We present a framework for quickly prototyping newexisting densitybased clustering algorithms while obtaining low running times and high speedups via automatic parallelization. The user is required only to specify the sequential algorithm in a Domain Specific Language (DSL) for clustering at a very high level of abstraction. The parallelizing compiler for the DSL does the rest to leverage distributed systems  in particular, typical scaleout clusters made of commodity hardware. Our approach is based on recurring, parallelizable programming patterns known as Kernels, which are identified and parallelized by the compiler. We demonstrate the ease of programming and scalable performance for DBSCAN, SNN, and RECOME algorithms. We also establish that the proposed approach can achieve performance comparable to stateoftheart manually parallelized implementations while requiring minimal programming effort that is several orders of magnitude smaller than those required on other parallel platforms like MPISpark.