Chronix: Long Term Storage and Retrieval Technology for Anomaly Detection in Operational Data - Lautenschlager, Florian; Philippsen, Michael; Kumlehn, Andreas; Adersberger, Josef (Proceedings of the 15th USENIX Conference on File and Storage Technologies (FAST 17), 2017)
Anomalies in the runtime behavior of software systems, especially in distributed systems, are inevitable, expensive, and hard to locate. To detect and correct such anomalies (like instability due to a growing memory consumption, failure due to load spikes, etc.) one has to automatically collect, store, and analyze the operational data of the runtime behavior, often represented as time series. There are efficient means both to collect and analyze the runtime behavior. But traditional time series databases do not yet focus on the specific needs of anomaly detection (generic data model, specific built-in functions, storage efficiency, and fast query execution). The paper presents Chronix, a domain specific time series database targeted at anomaly detection in operational data. Chronix uses an ideal compression and chunking of the time series data, a methodology for commissioning Chronix’ parameters to a sweet spot, a way of enhancing the data with attributes, an expandable set of analysis functions, and other techniques to achieve both faster query times and a significantly smaller memory footprint. On benchmarks Chronix saves 20%–68% of the space that other time series databases need to store the data and saves 80%–92% of the data retrieval time and 73%–97% of the runtime of analyzing functions.

Design for Diagnosability - Lautenschlager, Florian; Adersberger, Josef; Kumlehn, Andreas; Philippsen, Michael (Java Magazin, 2014)
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD2.design

Entwicklung einer konfigurierbaren Verschnittoptimierung für die Holzindustrie - Kumlehn, Andreas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2011)

Fast and efficient operational time series storage: The missing link in dynamic software analysis - Lautenschlager, Florian; Kumlehn, Andreas; Adersberger, Josef; Philippsen, Michael (Softwaretechnik-Trends (Band 35, Nr. 3): Proceedings of the Symposium on Software Performance (SSP 2015), 2015)
Distributed applications, cloud systems, the Internet of Things, etc. are generating increasing amounts of operational data, such as CPU loads, thread states, memory consumptions, method runtimes, or logs. Many tools continuously collect and analyze such data that is best represented as time series. Typical analyses try to nd and localize run-time incidents like outliers, leaks, or trend anomalies. However, these analyses need an ecient use of storage and a fast interactive query execution, that general purpose storage systems do not provide: neither storing operational time series data in general-purpose databases nor in conventional time series databases ful lls these requirements. We present Chronix, a novel time series storage that is optimized for operational time series and that improves the link between storage and analysis in a dynamic software analysis toolchain. With Chronix a toolchain not only stores data 433 times faster and it takes 5-171 times less storage space than with other time series databases, it also executes queries in 15-74% and analyses in 25-74% of the time.

Hard Real-time Scheduling mittels Column Generation - Kumlehn, Andreas (2007)

Rahmenwerk zur Ausreißererkennung in Zeitreihen von Software-Laufzeitdaten - Lautenschlager, Florian; Kumlehn, Andreas; Adersberger, Josef; Philippsen, Michael (Software Engineering & Management (SE 2015), 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD2.rahmen
Keywords: Time series; Database; Anomaly Detection