Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Daniel Rammer"'
Autor:
Daniel Rammer, Matthew Malensek, Thilina Buddhika, Shrideep Pallickara, Sangmi Lee Pallickara
Publikováno v:
IEEE Transactions on Big Data. 8:213-228
Fueled by the proliferation of IoT devices and increased adoption of sensing environments the collection of spatiotemporal data has exploded in recent years. Disk based storage systems provide reliable archives but are far too slow for efficient anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd940ddd071e48e352dc4ee5948c7863
https://doi.org/10.1109/tbdata.2019.2939834
https://doi.org/10.1109/tbdata.2019.2939834
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61f5a7cc10164792d41588b542ebfab6
https://doi.org/10.1109/bigdata52589.2021.9671537
https://doi.org/10.1109/bigdata52589.2021.9671537
Autor:
Menuka Warushavithana, Caleb Carlson, Saptashwa Mitra, Daniel Rammer, Mazdak Arabi, Jay Breidt, Sangmi Lee Pallickara, Shrideep Pallickara
Publikováno v:
2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies (BDCAT '21).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c33f7673af23619109302ad7b8150b8c
https://doi.org/10.1145/3492324.3494164
https://doi.org/10.1145/3492324.3494164
Publikováno v:
CLUSTER
We propose EVOKE, a model based on progressive Generative Adversarial Networks, that dynamically reconstructs high-resolution imagery during zoom-in operations using in-memory historical low-resolution images and is space-efficient to facilitate memo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::810f30fffae0366b3739d867010aa083
https://doi.org/10.1109/cluster48925.2021.00079
https://doi.org/10.1109/cluster48925.2021.00079
Publikováno v:
CCGRID
Hyperspectral satellite data collections have been successfully leveraged in many domains such as meteorology, agriculture, forestry, and disaster management. There is also a collection of publicly available satellite observation networks. However, g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::471609e7e9be4f50d3aa2b05526ec2ca
https://doi.org/10.1109/ccgrid51090.2021.00019
https://doi.org/10.1109/ccgrid51090.2021.00019
Autor:
Daniel Rammer, Sangmi Lee Pallickara, Shrideep Pallickara, Samuel Armstrong, Kevin Bruhwiler, Paahuni Khandelwal
Publikováno v:
IEEE BigData
There has been a substantial growth in remotely sensed hyperspectral satellite imagery. These data offer opportunities to understand phenomena and inform decision making. The nature of these collections introduces challenges stemming from their volum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfd98020553793181cb3f6e2c984a23f
https://doi.org/10.1109/bigdata50022.2020.9377764
https://doi.org/10.1109/bigdata50022.2020.9377764
Publikováno v:
UCC
Across several domains there has been a substantial growth in data volumes. A majority of the generated data are geotagged. This data includes a wealth of information that can inform insights, planning, and decision-making. The proliferation of open-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::403f9624e96c1472891a9bbafc587a11
https://doi.org/10.1109/ucc48980.2020.00024
https://doi.org/10.1109/ucc48980.2020.00024
Autor:
Shrideep Pallickara, Daniel Rammer, Kevin Bruhwiler, Paahuni Khandelwal, Samuel Armstrong, Sangmi Lee Pallickara
Publikováno v:
UCC
Several domains such as agriculture, urban sustainability, and meteorology entail processing satellite imagery for modeling and decision-making. In this study, we describe our novel methodology to train deep learning models over collections of satell
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::866d5f3b30ba2671863f129fd1b55954
https://doi.org/10.1109/ucc48980.2020.00029
https://doi.org/10.1109/ucc48980.2020.00029
Publikováno v:
UCC
A majority of the data generated in several domains is geotagged. These data also have a chronological component associated with them. Pervasive data generation and collection efforts have led to an increase in data volumes. These data hold the poten
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0012eafbc44d6c799b404bcdd9593cd
https://doi.org/10.1145/3344341.3368802
https://doi.org/10.1145/3344341.3368802
Publikováno v:
IEEE BigData
There has been an exponential growth in data volumes in several domains. Often these voluminous datasets encompass a large number of features. Fitting models to such high-dimensional, voluminous data allows us to understand phenomena and inform decis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c20de38bf1023ed44312eb0e3f0d3a9
https://doi.org/10.1109/bigdata.2018.8622279
https://doi.org/10.1109/bigdata.2018.8622279