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Outlier detection over streams using statistical modeling and density neighborhoods
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| Identifier | 000342551 | |||
| Title | Outlier detection over streams using statistical modeling and density neighborhoods | |||
| Alternative Title | Εύρεση Εκτόπων σε Ροές Δεδομένων χρησιμοποιώντας Στατιστική Μοντελοποίηση και Γειτονιές Πυκνότητας | |||
| Author | Βελεγράκης, Δημήτριος | |||
| Thesis advisor | Πλεξουσάκης, Δημήτρης | |||
| Abstract |
In this work, we unify these two thrusts by using insights gleaned from novel, realistic theoretic models in the design of egoist – a distributed overlay routing system that we implemented, deployed, and evaluated on PlanetLab. Using extensive measurements of paths between nodes, we demonstrate that egoist’s neighbor selection primitives significantly outperform existing heuristics on a variety of performance metrics, including delay, available bandwidth, and node utilization. Moreover, we demonstrate that egoist is competitive with an optimal, but unscalable full-mesh approach, remains highly effective under significant churn, is robust to cheating, and incurs minimal overhead. Finally, we use a multi-player peer-to-peer game to demonstrate the value of egoist to end-user applications. |
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| Language | English | |||
| Issue date | 2009-04-02 | |||
| Collection | School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses | |||
| Type of Work--Post-graduate theses | ||||
| Views | 471 | |||
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