We are seeing exponential growth in data generated from various platforms like social media, multimedia, enterprises, internet of things etc. It is becoming increasingly difficult to manage, analyze, visualize, model, store, search big data systems. However, we also witness growth in the complexity, diversity, number of deployments and capabilities of big data processing systems such as Map-Reduce, Spark, HBase, Hive, Cassandra, Big Table, Pregel and Mongo DB. The big data system may use new operating system designs, advanced data processing algorithms, parallelization of application, high performance computing architectures such as GPUs etc. and clusters to improve the performance. Traditional systems are also upgrading themselves to co-locate with popular big data technologies.
The workshop on performance analysis of big data systems (PABS) aims at providing a platform for scientific researchers, academicians and practitioners to discuss techniques, models, benchmarks, tools, case studies and experiences while dealing with performance issues in traditional and big data systems. The primary objective is to discuss performance bottlenecks and improvements during big data analysis using different paradigms, architectures and big data technologies. We propose to use this platform as an opportunity to discuss systems, architectures, tools, and optimization algorithms that are parallel in nature and hence make use of advancements to improve the system performance. This workshop shall focus on the performance challenges imposed by big data systems and on the different state-of-the-art solutions proposed to overcome these challenges. The accepted papers shall be published in ACM proceedings and digital library.