Section: Application Domains

Big data

Big data analytics

The amount of data digitally produced is increasing at an exponential rate. Having a dedicated programming model and runtime, such as Hadoop-MapReduce, has proved very useful to build efficient big data mining and analysis applications albeit for very static environments. However, if we consider that not only the environment is dynamic (node sharing, failures...) but so are the data (variation in popularity, arrival rate...), it becomes a much more complex problem. This domain is thus a very good candidate as an application field for our work.

More precisely, we plan to contribute at the deployment level, runtime level, and at the analytics programming model for the end-user level. We already worked on close topics with the distributed P2P storage and publish/subscribe system for Semantic Web data (named EventCloud). However, expressing a particular interest about data through simple or even more complex subscriptions (CEP) is only a first step in data analytics. Going further requires the full expressivity of a programming language to express how to mine into the real-time data streams, aggregate intermediate analytics results, combine with past data when relevant, etc. We intend to enlarge this effort about extracting meaningful information by also creating tighter collaborations with groups specialized in data mining algorithms (e.g. the Mind team at I3S).

We think that the approach advocated in Scale is particularly adapted to the programming and support of analytics. Indeed, the mix of computational aspects and of large amount of data make the computation of analytics the perfect target for our programming paradigms. We aim at illustrating the effectiveness of our approach by experimenting on different computations of analytics, but we will put a particular focus on the case of data streams, where the analysis is made of chains (even cyclic graphs) of parallel and distributed operators. These operators can naturally be expressed as coarse grained composition of fine grained parallel entities, both granularity levels featuring autonomic adaptation. Also, the underlying execution platform that supports this execution also has to feature autonomic adaptation in order to deal with an unstable and heterogeneous execution environment. Here autonomic adaptation is also crucial because the programmer of analytics is not expected to be an expert in distributed systems.

Overall, this second application domain target should illustrate the effectiveness of our runtime platform and of our methodology for dynamic and autonomic adaptation.