First International Workshop on
Big Data Processing - Reloaded
Tuesday, March 15th, 2016
co-located with EDBT/ICDT 2016
Big data processing has gained much attention in the recent years raising the need for stream processing solutions. At the same time, there has been substantial research and development of methods and technologies that have high potential for further progress in stream processing. This includes (i) flexible stream processing infrastructures that go beyond MapReduce, such as Apache Storm and Apache Spark, (ii) generic and application-specific stream processing algorithms, (iii) reconfigurable hardware more accessible to programmers as well as initial experience of using such components as coprocessors when processing streams, (iv) scalable algorithms for social web analysis or new approaches to data visualization, etc.
It is the goal of this workshop to discuss latest developments in this area and to relate them to each other. To this end, we want to provide a forum to discuss challenges, advances, and directions in this area while also providing the right environment to network with people working on related topics and fostering future collaborations. In addition, the workshop is especially interested in exploring how those new developments can be exploited and combined, such that effective, scalable and adaptive stream processing systems can be build efficiently for applications in the financial and other domains.
This includes questions such as: What are useful development approaches for implementing and adapting complex and efficient processing pipelines on stream processing infrastructures? How can stream processing approaches and algorithms be applied in specific domains, such as the financial domain? How can evidences gained from different types of data streams (e.g., Twitter stream vs. financial data streams) be combined for improved and consistent analysis result? How can the processing tasks be seamlessly offloaded to co-processors including FPGAs, GPUs, etc., and which algorithms qualify for this?