Authors:
Jan Sipke van der Veen
1
;
Bram van der Waaij
1
;
Matthijs Vonder
1
;
Marc de Jonge
1
;
Elena Lazovik
1
and
Robert Meijer
2
Affiliations:
1
TNO, Netherlands
;
2
TNO and Universiteit van Amsterdam, Netherlands
Keyword(s):
Data Analysis, Data Science, Sensor Data, Cloud Computing.
Related
Ontology
Subjects/Areas/Topics:
Cloud Application Architectures
;
Cloud Application Scalability and Availability
;
Cloud Computing
;
Cloud Middleware Frameworks
;
Platforms and Applications
Abstract:
Sensors have been used for many years to gather information about their environment. The number of sensors connected to the internet is increasing, which has led to a growing demand of data transport and storage capacity. In addition, evermore emphasis is put on processing the data to detect anomalous situations and to identify trends. This paper presents a sensor data analysis platform that executes statistical analysis programs on a cloud computing infrastructure. Compared to existing batch and stream processing platforms, it adds the notion of simulated time, i.e. time that differs from the actual, current time. Moreover, it adds the ability to dynamically schedule the analysis programs based on a single timestamp, recurring schedule, or on the sensor data itself.