Works (10)
Updated: July 5th, 2023 15:39
2021 article
Predictive models with end user preference
Zhao, Y., Yang, X., Bolnykh, C., Harenberg, S., Korchiev, N., Yerramsetty, S. R., … Samatova, N. F. (2021, August 26). STATISTICAL ANALYSIS AND DATA MINING, Vol. 8.
author keywords: child support; decision tree; predictive model; regularization; relative ranking; user preference
TL;DR:
A generic modeling method that respects end user preferences via a relative ranking system to express multi‐criteria preferences and a regularization term in the model's objective function to incorporate the ranked preferences is proposed.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
10. Reduced Inequalities
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: September 7, 2021
2017 article
A Lifelong Learning Topic Model Structured Using Latent Embeddings
2017 11TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), pp. 260–261.
Contributors: M. Xu n, R. Yang n , n & N. Samatova n
author keywords: Lifelong learning; Topic modeling; Latent embeddings
TL;DR:
A latent-embedding-structured lifelong learning topic model, called the LLT model, to discover coherent topics from a corpus and exploit latent word embeddings to structure the model and mine word correlation knowledge to assist in topic modeling.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: August 6, 2018
2016 article
AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications
2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), pp. 116–125.
TL;DR:
AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores, in the best case, the framework achieves a 46% performance improvement.
(via Semantic Scholar)
www.escholarship.org (repository)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2016 article
In situ Storage Layout Optimization for AMR Spatio-temporal Read Accesses
PROCEEDINGS 45TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING - ICPP 2016, pp. 406–415.
TL;DR:
This work develops an in situ data layout optimization framework that automatically selects from a set of candidate layouts based on a performance model, and reorganizes the data before writing to storage to enable efficient AMR read accesses.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2016 article
Usage Pattern-Driven Dynamic Data Layout Reorganization
2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), pp. 356–365.
TL;DR:
This work proposes a framework that dynamically recognizes the data usage patterns, replicates the data of interest in multiple reorganized layouts that would benefit common read patterns, and makes runtime decisions on selecting a favorable layout for a given read pattern.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2015 review
Anomaly detection in dynamic networks: a survey
[Review of ]. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 7(3), 223–247.
author keywords: anomaly detection; dynamic networks; outlier detection; graph mining; dynamic network anomaly detection; network anomaly detection
TL;DR:
This work focuses on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data, but as real‐world networks are constantly changing, there has been a shift in focus to dynamic graphs,Which evolve over time.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018
2015 article
Exploring Memory Hierarchy to Improve Scientific Data Read Performance
2015 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING - CLUSTER 2015, pp. 66–69.
author keywords: scientific data; read contention; memory hierarchy; SSD
TL;DR:
This paper proposes a framework that exploits the memory hierarchy resource to address the read contention issues involved with SSDs and achieves up to 50% read performance improvement when tested on datasets from real-world scientific simulations.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2013 conference paper
A generic high-performance method for deinterleaving scientific data
Euro-par 2013 parallel processing, 8097, 571–582.
TL;DR:
To the best of the knowledge, this is the first deinterleaving method that exploits data cache prefetching, reduces memory accesses, and optimizes the use of complete cache line writes.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018
journal article
Characterizing gene and protein crosstalks in subjects at risk of developing Alzheimer's disease: A new computational approach
Padmanabhan, K., Nudelman, K., Harenberg, S., Bello, G., Sohn, D., Shpanskaya, K., … Samatova, N. F. Processes, 5(3).
Source: NC State University Libraries
Added: August 6, 2018
report
Community detection in large-scale networks: A Survey and empirical evaluation
Harenberg, S., Bello, G. A., Gjeltema, L., Ranshous, S., Harlalka, J., Seay, R., … Samatova, N. In Technical Report- Not held in TRLN member libraries (p. 2014).
Source: NC State University Libraries
Added: August 6, 2018
Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.
Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.