Zobrazeno 1 - 10
of 1 219
pro vyhledávání: '"Pedersen, Torben"'
Autor:
Dzaferagic, Merim, Ruffini, Marco, Slamnik-Krijestorac, Nina, Santos, Joao F., Marquez-Barja, Johann, Tranoris, Christos, Denazis, Spyros, Kyriakakis, Thomas, Karafotis, Panagiotis, DaSilva, Luiz, Pandey, Shashi Raj, Shiraishi, Junya, Popovski, Petar, Jensen, Soren Kejser, Thomsen, Christian, Pedersen, Torben Bach, Claussen, Holger, Du, Jinfeng, Zussman, Gil, Chen, Tingjun, Chen, Yiran, Tirupathi, Seshu, Seskar, Ivan, Kilper, Daniel
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical dom
Externí odkaz:
http://arxiv.org/abs/2407.01544
The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. However, like other deep learning models, Transformers face limitations in
Externí odkaz:
http://arxiv.org/abs/2401.06524
Data cubes are used for analyzing large data sets usually contained in data warehouses. The most popular data cube tools use graphical user interfaces (GUI) to do the data analysis. Traditionally this was fine since data analysts were not expected to
Externí odkaz:
http://arxiv.org/abs/2312.08557
Big time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in various environments. Significant insights can be gained by mining temporal patterns from these time series. Temporal pattern mining (TPM) extends
Externí odkaz:
http://arxiv.org/abs/2306.10994
Autor:
Holm, Josefine, Chiariotti, Federico, Kalør, Anders E., Soret, Beatriz, Pedersen, Torben Bach, Popovski, Petar
Publikováno v:
IEEE Transactions on Communications, 2023
Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multip
Externí odkaz:
http://arxiv.org/abs/2306.03750
Autor:
Perera, Kasun S., Hahmann, Martin, Lehner, Wolfgang, Pedersen, Torben Bach, Thomsen, Christian
The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of busin
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A79865
https://tud.qucosa.de/api/qucosa%3A79865/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A79865/attachment/ATT-0/
Autor:
Perera, Kasun S., Hahmann, Martin, Lehner, Wolfgang, Pedersen, Torben Bach, Thomsen, Christian
Evolving customer requirements and increasing competition force business organizations to store increasing amounts of data and query them for information at any given time. Due to the current growth of data volumes, timely extraction of relevant info
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A83471
https://tud.qucosa.de/api/qucosa%3A83471/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A83471/attachment/ATT-0/
Autor:
Zhao, Yan, Deng, Liwei, Chen, Xuanhao, Guo, Chenjuan, Yang, Bin, Kieu, Tung, Huang, Feiteng, Pedersen, Torben Bach, Zheng, Kai, Jensen, Christian S.
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable rel
Externí odkaz:
http://arxiv.org/abs/2209.04635
This panel paper aims at initiating discussion at the Second International Workshop on Energy Data Management (EnDM 2013) about the important research challenges within Energy Data Management. The authors are the panel organizers, extra panelists wil
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A80370
https://tud.qucosa.de/api/qucosa%3A80370/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A80370/attachment/ATT-0/
Very large time series are increasingly available from an ever wider range of IoT-enabled sensors, from which significant insights can be obtained through mining temporal patterns from them. A useful type of patterns found in many real-world applicat
Externí odkaz:
http://arxiv.org/abs/2206.14604