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Anomaly Detection and Exploratory Causal Analysis for SAP HANA

Jin, Jianqiao

Abstract:
Nowadays, the good functioning of the equipment, networks and systems will be the key for the business of a company to continue operating because it is never avoidable for the companies to use information technology to support their business in the era of big data. However, the technology is never infallible, faults that give rise to sometimes critical situations may appear at any time. To detect and prevent failures, it is very essential to have a good monitoring system which is responsible for controlling the technology used by a company (hardware, networks and communications, operating systems or applications, among others) in order to analyze their operation and performance, and to detect and alert about possible errors. The aim of this thesis is thus to further advance the field of anomaly detection and exploratory causal inference which are two major research areas in a monitoring system, to provide efficient algorithms with regards to the usability, maintainability and scalability. The analyzed results can be viewed as a starting point for the root cause analysis of the system performance issues and to avoid falls in the syst ... mehr

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Volltext §
DOI: 10.5445/IR/1000089289
Veröffentlicht am 11.01.2019
Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Publikationstyp Hochschulschrift
Jahr 2019
Sprache Englisch
Identifikator urn:nbn:de:swb:90-892890
KITopen-ID: 1000089289
Verlag KIT, Karlsruhe
Umfang 96 S.
Abschlussart Abschlussarbeit - Master
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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