Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Aoqian Zhang"'
Publikováno v:
IEEE Access, Vol 8, Pp 4122-4132 (2020)
GPS trajectories are always embedded with errors, due to the weather or environmental variables. Existing trajectory repairing methods have employed Kalman filters or sequential data cleaning methods. Kalman filter or its variants change all observed
Externí odkaz:
https://doaj.org/article/ed1d2525a76f49c0996ca8502154ad83
Autor:
Zhicheng Liu, Aoqian Zhang
Publikováno v:
IEEE Access, Vol 8, Pp 72713-72726 (2020)
Due to the development of internet technology and computer science, data is exploding at an exponential rate. Big data brings us new opportunities and challenges. On the one hand, we can analyze and mine big data to discover hidden information and ge
Externí odkaz:
https://doaj.org/article/5fa26a3ea3ea4ef9b715813b4a3f822e
Autor:
Zhiwei Chen, Aoqian Zhang
Publikováno v:
IEEE Access, Vol 8, Pp 34585-34597 (2020)
As data volume grows extensively, data profiling helps to extract metadata of large-scale data. However, one kind of metadata, order statistics, is difficult to be computed because they are not mergeable or incremental. Thus, the limitation of time a
Externí odkaz:
https://doaj.org/article/71907f627f88489b9727dabbfe167e15
Publikováno v:
The VLDB Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03306a0f1b96b2a3de5ddfd8c3c189ea
https://doi.org/10.1007/s00778-023-00796-y
https://doi.org/10.1007/s00778-023-00796-y
Publikováno v:
ACM Transactions on Database Systems. 46:1-44
Stream data are often dirty, for example, owing to unreliable sensor reading or erroneous extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter, which may seriously alter the data without preserving the original i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15b15b1e6d2794f4b068a11282cb83bb
https://doi.org/10.1145/3465740
https://doi.org/10.1145/3465740
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::427ce381838520f8ce6e35bb08c22809
https://doi.org/10.1109/icde53745.2022.00235
https://doi.org/10.1109/icde53745.2022.00235
Publikováno v:
The 2022 4th International Conference on Blockchain Technology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e0c3a49ae7ba4f60a2f5cb025d6f626
https://doi.org/10.1145/3532640.3532644
https://doi.org/10.1145/3532640.3532644
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 32:275-287
Incomplete information often occurs along with many database applications, e.g., in data integration, data cleaning, or data exchange. The idea of data imputation is often to fill the missing data with the values of its neighbors who share the same/s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8235302c9e8edac5e54810c9766b70e0
https://doi.org/10.1109/tkde.2018.2883103
https://doi.org/10.1109/tkde.2018.2883103
Publikováno v:
IEEE Access, Vol 8, Pp 4122-4132 (2020)
GPS trajectories are always embedded with errors, due to the weather or environmental variables. Existing trajectory repairing methods have employed Kalman filters or sequential data cleaning methods. Kalman filter or its variants change all observed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee779804dbd5ed57315adcd3fcb38afa
https://ieeexplore.ieee.org/document/8943289/
https://ieeexplore.ieee.org/document/8943289/
Autor:
Aoqian Zhang
Publikováno v:
2021 2nd International Conference on Computing and Data Science (CDS).
This paper presents a design of a housekeeping robot that can automatically navigate in a room while both vacuum cleaning floors and picking up garbage. First, the background and history of housekeeping robots are summarized. Then, current housekeepi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58dabce808e4282fb8bf11340873d3e9
https://doi.org/10.1109/cds52072.2021.00050
https://doi.org/10.1109/cds52072.2021.00050