Arnak Poghosyan Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, Clement Pang, George Oganesyan, Sirak Ghazaryan, Narek Hovhannisyan VMware, Inc.   Abstract:   One of the main targets of application performance managers is monitoring of cloud environments with high-velocity custom metrics and analytics. The key components of time series data analytics are forecasting and anomaly detection. The classical methods of time series forecasting were recently empowered by neural network-based models which gain increasing popularity due to their flexibility and ability to tackle complex non-linear problems. Meanwhile, some of the disadvantages of that approach mitigate expectations and require specific solution for SaaS applications. The first challenge for network-based models is resource utilization due…

Arnak Poghosyan   Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, and Nicholas Kushmerick VMware, Inc.   Abstract:   Identification of a problem fingerprint or incident in a data center is of crucial importance for the system administrators. Automated discovery of such important patterns in cloud environments is recently gaining a lot of popularity for effective and efficient root cause analysis of business-critical is-sues. A problem incident is a group of alerts with sufficient historical evidence in reoccurrence and similarity. Presumably, all known incidents should be stored in user’s knowledge base together with available annotations regarding the problem description and its possible resolutions. The knowledge base of incidents with enough coverage of…

Daniel Biella Nelson Baloian, Daniel Biella, Wolfram Luther, Belisario Panay, Sergio Peñafiel, José A. Pino University of Duisburg-Essen   Abstract:   This contribution is about khachkar segmentation and classification. Using existing non-automated concepts, the authors suggest extensions to a classification scheme for khachkar metadata and describe an approach for an automated khachkar segmentation and classification.       Discussion Room: Armenian Khachkars: Towards an Automated Handling of Segmentation and Classification   [email protected]

Wolfram Luther Daniel Biella, Nelson Baloian, Wolfram Luther University of Duisburg-Essen   Abstract:   This contribution provides important fundamentals of digital geometry and algorithms based on polygonal coverage and chain codes to generate GPS drawings. Illustrative examples created with the GPSVisualizer and GoopleMaps show the difficulty in finding suitable road networks.       Discussion Room: GPS Drawing on Street Networks: Extracting Routes from Polygonal Coverings     [email protected]