MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Proceedings of the VLDB Endowment
Nephele/PACTs: a programming model and execution framework for web-scale analytical processing
Proceedings of the 1st ACM symposium on Cloud computing
Ontology paper: Community analysis through semantic rules and role composition derivation
Web Semantics: Science, Services and Agents on the World Wide Web
Iterative parallel data processing with stratosphere: an inside look
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Hi-index | 0.00 |
The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows.