Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Steve Harenberg"'
Autor:
Carolina Bolnykh, Saavan Raj Yerramsetty, Nodirbek Korchiev, Bhanu Prasad Vellanki, Ramakanth Kodumagulla, Yifan Zhao, Nagiza F. Samatova, Xian Yang, Steve Harenberg
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 15:69-82
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e5041ff00d3922140ab1cc64222d76a
https://doi.org/10.1002/sam.11545
https://doi.org/10.1002/sam.11545
Autor:
Stephen Ranshous, Shitian Shen, Steve Harenberg, Danai Koutra, Christos Faloutsos, Nagiza F. Samatova
Publikováno v:
Wiley Interdisciplinary Reviews: Computational Statistics. 7:223-247
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 gra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2ee7d7413e75f07a49bcb9384fae342
https://doi.org/10.1002/wics.1347
https://doi.org/10.1002/wics.1347
Autor:
Priyanka Tiwari Dikshit, Kelly N.H. Nudelman, Jeffrey R. Petrella, Gonzalo A. Bello, Pallavi S. Yerramsetty, Nagiza F. Samatova, Steve Harenberg, Andrew J. Saykin, Alzheimer’s Disease Neuroimaging Initiative, P. Murali Doraiswamy, Katie Shpanskaya, Rudolph E. Tanzi, Kanchana Padmanabhan, Dongwha Sohn
Publikováno v:
Processes, Vol 5, Iss 3, p 47 (2017)
Processes; Volume 5; Issue 3; Pages: 47
Processes; Volume 5; Issue 3; Pages: 47
Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38620a0e873d9d5339eda4fdc5443e23
https://www.mdpi.com/2227-9717/5/3/47
https://www.mdpi.com/2227-9717/5/3/47
Publikováno v:
SSDBM
Detecting all communities in a single graph is a prevalent task in graph data analytics. However, many scientific applications naturally create data as an ensemble of graphs. For example, graph ensembles can be created from multiple: social networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cacd923e8bd3922354fa5b09a7a2e283
https://doi.org/10.1145/3085504.3085532
https://doi.org/10.1145/3085504.3085532
Publikováno v:
ICSC
We propose a latent-embedding-structured lifelong learning topic model, called the LLT model, to discover coherent topics from a corpus. Specifically, we exploit latent word embeddings to structure our model and mine word correlation knowledge to ass
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec6483e297b02ddc93b22f640b6d24c3
https://doi.org/10.1109/icsc.2017.15
https://doi.org/10.1109/icsc.2017.15
Autor:
Ramona G. Seay, Kanchana Padmanabhan, Gonzalo A. Bello, L. Gjeltema, Jitendra K. Harlalka, Nagiza F. Samatova, Stephen Ranshous, Steve Harenberg
Publikováno v:
WIREs Computational Statistics. 6:426-439
Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an import
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58cb47c589126c994bf459393e0fe311
https://doi.org/10.1002/wics.1319
https://doi.org/10.1002/wics.1319
Autor:
Bin Dong, Dharshi Devendran, Xiaocheng Zou, Kesheng Wu, Houjun Tang, Scott Klasky, Steve Harenberg, Daniel F. Martin, Nagiza F. Samatova, Wenzhao Zhang, Suren Byna, David Trebotich
Publikováno v:
ICPP
Analyses of large simulation data often concentrate on regions in space and in time that contain important information. As simulations adopt Adaptive Mesh Refinement (AMR), the data records from a region of interest could be widely scattered on stora
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20d21ce690796a3d99277bac6ba42870
https://doi.org/10.1109/icpp.2016.53
https://doi.org/10.1109/icpp.2016.53
Publikováno v:
SDM
Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8587b413299463c3d3517478bd0de94a
https://doi.org/10.1137/1.9781611974348.22
https://doi.org/10.1137/1.9781611974348.22
Autor:
Xiaocheng Zou, Houjun Tang, Kesheng Wu, Steve Harenberg, Kristofer E. Bouchard, Scott Klasky, Oliver Rubel, Nagiza F. Samatova, Wenzhao Zhang, Bin Dong, Suren Byna
Publikováno v:
Tang, Houjun; Byna, Suren; Harenberg, Steven; Zou, Xiaocheng; Zhang, Wenzhao; Wu, Kesheng; et al.(2016). Usage Pattern-Driven Dynamic Data Layout Reorganization:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/7dq7t3zq
CCGrid
CCGrid
As scientific simulations and experiments move toward extremely large scales and generate massive amounts of data, the data access performance of analytic applications becomes crucial. A mismatch often happens between write and read patterns of data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::beb2beae49736fdd640c4b90051b716d
http://www.escholarship.org/uc/item/7dq7t3zq
http://www.escholarship.org/uc/item/7dq7t3zq
