@article{hong_vatsavai_2016, title={A Scalable Probabilistic Change Detection Algorithm for Very High Resolution (VHR) Satellite Imagery}, ISSN={["2379-7703"]}, DOI={10.1109/bigdatacongress.2016.42}, abstractNote={Detecting landscape changes using very high-resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based change detection approaches, however, have several drawbacks, which render them ineffective for VHR imagery analysis. A recent probabilistic change detection framework provides more accurate assessment of changes than traditional approaches by analyzing image patches than pixels. However, this patch (grid)-based approach produces coarse-resolution (patch size) changes. In this work we present a sliding window based approach that produces changes at the native image resolution. The increased computational demand of the sliding window based approach is addressed through thread-level parallelization on shared memory architectures. Our experimental evaluation showed a 91% performance improvement compared to its sequential counterpart on a sq. KM aerial image with varying window sizes on a 16-core (32 virtual threads) Intel Xeon processor.}, journal={2016 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2016}, author={Hong, Seokyong and Vatsavai, Ranga Raju}, year={2016}, pages={275–282} }
 @article{lee_sukumar_hong_lim_2016, title={Enabling graph mining in RDF triplestores using SPARQL for holistic in-situ graph analysis}, volume={48}, ISSN={["1873-6793"]}, DOI={10.1016/j.eswa.2015.11.010}, abstractNote={Graph analysis is now considered as a promising technique to discover useful knowledge from data. We posit that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery. As these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF datasets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimental results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Lee, Sangkeun and Sukumar, Sreenivas R. and Hong, Seokyong and Lim, Seung-Hwan}, year={2016}, month={Apr}, pages={9–25} }