An Efficient Incremental Indexing Mechanism for Extracting Top-k Representative Queries Over Continuous Data-streams

by Sysco LABS Data & Analytics 21 January 2016

The annual ACM/IFIP/USENIX Middleware conference is a major forum for the discussion of innovations and recent advances in the design, construction and use of middleware systems.  The scope of the conference is the design, implementation, deployment, and evaluation of distributed system platforms and architectures for computing, storage, and communication environments. Highlights of the conference includes a high quality single-track technical program, invited speakers, an industrial track, panel discussions involving academic and industry leaders, poster and demonstration presentations, a doctoral symposium, and workshops.

Yasanka Horawalavithana, Software Engineer from CAKE LABS presented his research, titled “An Efficient Incremental Indexing Mechanism for Extracting Top-k Representative Queries Over Continuous Data-streams” at ARM workshop, collocated with ACM/IFIP/USENIX Middleware 2015, Vancover, Canda. Top-k publish/subscribe (pub/sub) models have gained traction as an expressive alternative to extend the binary notion of matching. In Yasanka’s study, the focus is on the problem of extracting the k-most representative set of publications in the dynamic case where the results are updated over a stream of matching publications. This can be observed as the minimum independent dominating set problem in graph theory, when streaming publications are represented as dynamic graph spaces. Due to the inherent complexity of solving this problem over continuous data, an incremental indexing mechanism is proposed for handling a stream of publications. The proposed mechanism is based on Locality Sensitive Hashing (LSH) to avoid the overhead of recalculating neighborhoods over consecutive sliding windows. The experimental results show that the incremental version of LSH indexing mechanism reduces the computational cost of naive greedy approach significantly, while producing Top-k representative results at 70% accuracy compared to the naive optimal method. Further this project has won the Best Computer Science Thesis Award at the University of Colombo convocation last year, sponsored by the Council for Information Technology (CINTEC), Sri Lanka.

View the presentation at:

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