Seif Haridi - Research in Continuous Deep Analytics
Research in Continuous Deep Analytics
After briefly describing the research environment and projects being done by the joint research group in distributing computing at SICS and KTH. We focus on the research that we have done on uniting batch and stream processing in a single unified continuous computational model. In particular the work on iterative stream processing work that is currently being integrated in Apache Flink. We then describe our new project on continuous deep analytics that aims at creating a platform for continuous real-time decision making supporting traditional relational analytics and machine learning.
Seif Haridi
Seif Haridi is Chair-Professor of Computer Systems specialized in parallel and distributed computing at KTH Royal Institute of Technology, Stockholm, Sweden. He is also the Chief Scientist of the Swedish Institute of Computer Science (RISE SICS). He led a European research program on Cloud Computing and Big Data by EIT-Digital between 2010 to 2013, and is a co-founder of a number of start-ups in the area of distributed and cloud computing including HiveStreaming (https://www.hivestreaming.com/) and LogicalClocks (https://www.logicalclocks.com/ ). He is well-known for the textbook “Concepts, Techniques, and Models of Computer Programming” which explains many programming concepts in a simple and insightful way. For more than ten years he has been teaching two popular courses on distributed algorithms and peer-to-peer computing at KTH. His research is focused on the combination of systems research and theory in the areas of programming systems and distributed and parallel computing. He is a codesigner of SICStus Prolog, the most well known logic programming system, and the Mozart Programming System, a high-quality open-source development platform based on the Oz multi-paradigm programming language. Recently, together with his research group, he has been contributing to the design of Apache Flink, a Big Data distributed engine for streaming analytics, and HOPS the first European complete platform for dataanalytics and machine learning. HOPS-FS the file system of HOPS won IEEE Scale Prize 2017 as the most scalable HDFS filesystem.
