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Efficient change detection for high dimensional data streams
| dc.creator | Georgakopoulos S.V., Tasoulis S.K., Plagianakos V.P. | en |
| dc.date.accessioned | 2023-01-31T07:40:22Z | |
| dc.date.available | 2023-01-31T07:40:22Z | |
| dc.date.issued | 2015 | |
| dc.identifier | 10.1109/BigData.2015.7364010 | |
| dc.identifier.isbn | 9781479999255 | |
| dc.identifier.uri | http://hdl.handle.net/11615/72071 | |
| dc.description.abstract | The recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high dimensional this is indispensable, since the mobile device has to balance its processing functionalities to additional services. However, developing efficient algorithms could allow various types of analysis to be performed locally, avoiding the necessity of a constantly connected device. In this work, we present a methodology that combines lightweight dimensionality reduction and change detection techniques. The experimental results justify its impressive performance and subsequently its usefulness in several tasks. © 2015 IEEE. | en |
| dc.language.iso | en | en |
| dc.source | Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963744687&doi=10.1109%2fBigData.2015.7364010&partnerID=40&md5=4950b3e39ca57d605333141083286a58 | |
| dc.subject | Algorithms | en |
| dc.subject | Clustering algorithms | en |
| dc.subject | Data communication systems | en |
| dc.subject | Data handling | en |
| dc.subject | Data mining | en |
| dc.subject | Mobile devices | en |
| dc.subject | Principal component analysis | en |
| dc.subject | Signal detection | en |
| dc.subject | Cumulative sums | en |
| dc.subject | Data stream | en |
| dc.subject | Dimensionality reduction | en |
| dc.subject | High dimensional data | en |
| dc.subject | High-dimensional data streams | en |
| dc.subject | Incremental principal component analysis | en |
| dc.subject | Processing functionality | en |
| dc.subject | Technological advancement | en |
| dc.subject | Big data | en |
| dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
| dc.title | Efficient change detection for high dimensional data streams | en |
| dc.type | conferenceItem | en |
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