Proceedings of the 5th International Workshop on Cloud Data and Platforms

Abstract

Processing of very large data sets requires a unique combination of data management and distributed systems engineering knowledge. The data management challenges include, among others, the development of new approaches and algorithms that can reduce the complexity of the data processing and allow incremental, continuous, and as accurate as possible result production. Simultaneously, the sheer volume and velocity of the data require support of systems which can automatically and adaptively scale up and out in order to accommodate big data processing algorithms. The focus of this workshop is on new cloud-based data management and processing systems which span tens of thousands of machines in order to support processing of contemporary, very large data sets. Such systems require novel architectures, programming models and designs that go beyond approaches used in fixed-sized compute clusters. The focus of such systems is to support the work of users who interactively explore and analyze large and quickly changing data sets. The right platforms and techniques can simplify and accelerate the design, implementation, and execution of new "big data" applications. In the past, data processing in the cloud has been dominated by batch processing paradigms such as MapReduce, but increasingly users seek to consume their results in near real-time. In order to efficiently support these new types of applications, it is necessary to overcome challenges when supporting adaptive, near real-time processing of data in cloud environments. Ultimately, adaptive low-latency data processing across large number of machines brings a new set of problems related to systems, distributed systems and geo-distribution, networking, fault-tolerance, and data management research.