Making Big-Data Programming Easy
Recent trends, from social media to algorithmic trading to the internet of things, have made available a deluge of data. This data comes at both high volume and high velocity, making it difficult to handle efficiently. Those who quickly extract insights from it gain an edge. Unfortunately, with existing systems and languages, it is hard to write efficient big-data applications. The challenge is bridging the gap between a high-level programming experience on the one hand and low-level incremental and parallel algorithms on the other hand. This talk describes three programming models we built at IBM.
First, the SPL language allows library writers to implement streaming operators as compiler extensions. Second, ActiveSheets offers a spreadsheet interface for programming streaming operators. And third, META offers a unified rule-based programming model both for online event processing and for batch analytics in the same system. This talk describes our research innovations as well as productization experiences.
Bio: Martin Hirzel is a research staff member and the manager of the Programming Languages research group at the IBM T.J. Watson Research Center. Martin received his PhD from the University of Colorado at Boulder in 2004; his thesis adviser was Amer Diwan. At IBM, Martin works on programming languages and on stream and event processing. Martin is an ACM Distinguished Scientist.