@inproceedings{zhang_tang_ranshous_byna_martin_wu_dong_klasky_samatova_2016, title={Exploring memory hierarchy and network topology for runtime AMR data sharing across scientific applications}, DOI={10.1109/bigdata.2016.7840743}, abstractNote={Runtime data sharing across applications is of great importance for avoiding high I/O overhead for scientific data analytics. Sharing data on a staging space running on a set of dedicated compute nodes is faster than writing data to a slow disk-based parallel file system (PFS) and then reading it back for post-processing. Originally, the staging space has been purely based on main memory (DRAM), and thus was several orders of magnitude faster than the PFS approach. However, storing all the data produced by large-scale simulations on DRAM is impractical. Moving data from memory to SSD-based burst buffers is a potential approach to address this issue. However, SSDs are about one order of magnitude slower than DRAM. To optimize data access performance over the staging space, methods such as prefetching data from SSDs according to detected spatial access patterns and distributing data across the network topology have been explored. Although these methods work well for uniform mesh data, which they were designed for, they are not well suited for adaptive mesh refinement (AMR) data. Two mąjor issues must be addressed before constructing such a memory hierarchy and topology-aware runtime AMR data sharing framework: (1) spatial access pattern detection and prefetching for AMR data; (2) AMR data distribution across the network topology at runtime. We propose a framework that addresses these challenges and demonstrate its effectiveness with extensive experiments on AMR data. Our results show the framework's spatial access pattern detection and prefetching methods demonstrate about 26% performance improvement for client analytical processes. Moreover, the framework's topology-aware data placement can improve overall data access performance by up to 18%.}, booktitle={2016 IEEE International Conference on Big Data (Big Data)}, author={Zhang, W. Z. and Tang, H. J. and Ranshous, S. and Byna, S. and Martin, D. F. and Wu, K. S. and Dong, B. and Klasky, S. and Samatova, N. F.}, year={2016}, pages={1359–1366} }
 @misc{ranshous_shen_koutra_harenberg_faloutsos_samatova_2015, title={Anomaly detection in dynamic networks: a survey}, volume={7}, ISSN={["1939-0068"]}, DOI={10.1002/wics.1347}, abstractNote={Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real‐world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.}, number={3}, journal={WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS}, author={Ranshous, Stephen and Shen, Shitian and Koutra, Danai and Harenberg, Steve and Faloutsos, Christos and Samatova, Nagiza F.}, year={2015}, pages={223–247} }
 @book{ranshous_shen_koutra_faloutsos_samatova, title={Anomaly detection in dynamic networks: A survey}, journal={Technical Report- Not held in TRLN member libraries}, author={Ranshous, S. and Shen, S. and Koutra, D. and Faloutsos, C. and Samatova, N. F.} }
 @book{harenberg_bello_gjeltema_ranshous_harlalka_seay_padmanabhan_samatova, title={Community detection in large-scale networks: A Survey and empirical evaluation}, journal={Technical Report- Not held in TRLN member libraries}, author={Harenberg, S. and Bello, G. A. and Gjeltema, L. and Ranshous, S. and Harlalka, J. and Seay, R. and Padmanabhan, K. and Samatova, N.}, pages={2014} }