Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Aaron Elmore"'
Autor:
Paul Boniol, John Paparrizos, Yuhao Kang, Themis Palpanas, Tsay, Ruey S., Aaron Elmore, Franklin, Michael J.
Publikováno v:
Aaron Elmore
The detection of anomalies in time series has gained ample academic and industrial attention, yet, no comprehensive benchmark exists to evaluate time-series anomaly detection methods. Therefore, there is no final verdict on which method performs the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::597aafb8bef5a91a72442b05e2e85d9e
https://doi.org/10.14778/3554821.3554879
https://doi.org/10.14778/3554821.3554879
Publikováno v:
Aaron Elmore
Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where AD mainly focuses on point-based anomalies (i.e., outliers in s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f3a09699c7d885804aa00f496958ba8
https://doi.org/10.14778/3551793.3551830
https://doi.org/10.14778/3551793.3551830
Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage
Publikováno v:
Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bac2006a871cbe941b1b4536d35e70d0
https://doi.org/10.1145/3526113.3545704
https://doi.org/10.1145/3526113.3545704
Autor:
Mohammed Suhail Rehman, Aaron Elmore
Publikováno v:
Proceedings of the 2022 workshop on 9th International Workshop of Testing Database Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bb53ccc4763bd67eafc28b86daa8c6b
https://doi.org/10.1145/3531348.3532178
https://doi.org/10.1145/3531348.3532178
Publikováno v:
Aaron Elmore
The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We explore artificially constraining the frequency spectra of these filt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9004b10e215830f079248db56ff7041
http://arxiv.org/abs/1911.09287
http://arxiv.org/abs/1911.09287
Publikováno v:
Aaron Elmore
As scientific data repositories and filesystems grow in size and complexity, they become increasingly disorganized. The coupling of massive quantities of data with poor organization makes it challenging for scientists to locate and utilize relevant d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea9aee522e709d332eaab9e697893add
Autor:
Vijay Gadepally, Jennie Duggan, Aaron Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Michael Stonebraker
Publikováno v:
Aaron Elmore
BigDAWG is a polystore system designed to work on complex problems that naturally span across different processing or storage engines. BigDAWG provides an architecture that supports diverse database systems working with different data models, support
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::142491cb4fe4347be837053f360a469e
Autor:
Zamanzadeh Darban, Zahra1 (AUTHOR) zahra.zamanzadeh@monash.edu, Webb, Geoffrey I.1 (AUTHOR) geoff.webb@monash.edu, Pan, Shirui2 (AUTHOR) s.pan@griffith.edu.au, Aggarwal, Charu3 (AUTHOR) charu@us.ibm.com, Salehi, Mahsa1 (AUTHOR) mahsa.salehi@monash.edu
Publikováno v:
ACM Computing Surveys. Jan2025, Vol. 57 Issue 1, p1-42. 42p.
Publikováno v:
Aaron Elmore
Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2cef723f8ea2f50f0c8d17b4b5b1fe38
http://arxiv.org/abs/1703.02475
http://arxiv.org/abs/1703.02475
Publikováno v:
Aaron Elmore
Carefully selected materialized views can greatly improve the performance of OLAP workloads. We study using deep reinforcement learning to learn adaptive view materialization and eviction policies. Our insight is that such selection policies can be e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04a4640f10b192a55bd857476eef6d85
http://arxiv.org/abs/1903.01363
http://arxiv.org/abs/1903.01363