Detecting Data Stream Dependencies on High Dimensional Data

Boidol, Jonathan and Hapfelmeier, Andreas (2016): Detecting Data Stream Dependencies on High Dimensional Data. International Conference on Internet of Things and Big Data IoTBD, Rome, Italy, April 23-25, 2016. Ramachandran, Muthu (ed.) : In: Proceedings of the International Conference on Internet of Things and Big Data, Setúbal: Scitepress. pp. 383-390

Abstract

Intelligent production in smart factories or wearable devices that measure our activities produce on an ever growing amount of sensor data. In these environments, the validation of measurements to distinguish sensor flukes from significant events is of particular importance. We developed an algorithm that detects dependencies between sensor readings. These can be used for instance to verify or analyze large scale measurements. An entropy based approach allows us to detect dependencies beyond linear correlation and is well suited to deal with high dimensional and high volume data streams. Results show statistically significant improvements in reliability and on-par execution time over other stream monitoring systems.

• Item Type: Conference or Workshop Item (Report)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Place of Publication: Setúbal
• Annotation: ISBN Vol. 1 - 978-989-758-183-0
• Language: English