2014/06/23 by Yogesh Simmhan, Neel Choudhury, Charith Wickramaarachchi +9
Computer Science · #Abstraction #Artificial intelligence #Big data #Cloud Computing and Resource Management #Cloud computing #Computer science #Data Management and Algorithms #Data mining #Database #Distributed computing #Graph #Graph Theory and Algorithms #Leverage (statistics) #Scalability #Series (stratigraphy) #Theoretical computer science #cs.DC
paper · pdf · doi:10.1109/ipdps.2015.66
published as Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2015) pp. 809-818
arxiv created 2014/06/23 · openalex publication_date 2015/05/01 · arxiv updated 2015/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Graphs are a key form of Big Data, and performing scalable analytics over them is invaluable to many domains. There is an emerging class of inter-connected data which accumulates or varies over time, and on which novel algorithms both over the network structure and across the time-variant attribute values is necessary. We formalize the notion of time-series graphs and propose a Temporally Iterative BSP programming abstraction to develop algorithms on such datasets using several design patterns. Our abstractions leverage a sub-graph centric programming model and extend it to the temporal dimension. We present three time-series graph algorithms based on these design patterns and abstractions, and analyze their performance using the Offish distributed platform on Amazon AWS Cloud. Our results demonstrate the efficacy of the abstractions to develop practical time-series graph algorithms, and scale them on commodity hardware.