Outlier detection using Spark StreamingOutlier detection using Spark StreamingΑνίχνευση δεδομένων άτυπης συμπεριφοράς με το σύστημα Spark Streaming Διπλωματική Εργασία Diploma Work 2017-12-192017enData 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.http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνPsarakis_Kyriakos_Dip_2017.pdfChania [Greece]Library of TUC2017-12-19application/pdf2.3 MBfree Psarakis Kyriakos Ψαρακης Κυριακος Deligiannakis Antonios Δεληγιαννακης Αντωνιος Garofalakis Minos Γαροφαλακης Μινως Lagoudakis Michael Λαγουδακης Μιχαηλ Πολυτεχνείο Κρήτης Technical University of Crete Data streams Outlier detection