Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI | http://purl.tuc.gr/dl/dias/02E820B1-D8E6-45DF-ACFA-7C37D76F29DA | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.70468 | - |
| Language | en | - |
| Extent | 50 pages | en |
| Title | Outlier detection using Spark Streaming | en |
| Title | Ανίχνευση δεδομένων άτυπης συμπεριφοράς με το σύστημα Spark Streaming | el |
| Creator | Psarakis Kyriakos | en |
| Creator | Ψαρακης Κυριακος | el |
| Contributor [Thesis Supervisor] | Deligiannakis Antonios | en |
| Contributor [Thesis Supervisor] | Δεληγιαννακης Αντωνιος | el |
| Contributor [Committee Member] | Garofalakis Minos | en |
| Contributor [Committee Member] | Γαροφαλακης Μινως | el |
| Contributor [Committee Member] | Lagoudakis Michael | en |
| Contributor [Committee Member] | Λαγουδακης Μιχαηλ | el |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Electrical and Computer Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
| Content Summary | Data is continuously being generated from sources such as machines, network traffic, sensor networks, etc. Timely and accurate detection of outliers in massive data streams has important applications such as in preventing machine failures, intrusion detection, and financial fraud detection. In this thesis, we implement an outlier detection algorithm inside the Spark Streaming environment that, makes only one pass over the data while utilizing limited storage. We chose the Spark Streaming environment because it offers scalable, high-throughput, fault-tolerant stream processing of live data streams. The algorithm adapts ideas from matrix sketching to maintain a set of few orthogonal vectors that form a good approximate basis for all the observed data. Using this constructed orthogonal basis, outliers in new incoming data are detected based on a simple reconstruction error test. Additionally, we have implemented two methods for updating the orthogonal vectors one deterministic and one randomized to further speedup the algorithm with a small cost to accuracy. | en |
| Type of Item | Διπλωματική Εργασία | el |
| Type of Item | Diploma Work | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2017-12-19 | - |
| Date of Publication | 2017 | - |
| Subject | Data streams | en |
| Subject | Outlier detection | en |
| Bibliographic Citation | Kyriakos Psarakis, "Outlier detection using Spark Streaming", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017 | en |
| Bibliographic Citation | Κυριάκος Ψαράκης, "Ανίχνευση δεδομένων άτυπης συμπεριφοράς με το σύστημα Spark Streaming", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017 | el |
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