Abstract
Today sensors are widely used in many monitoring applications. Due to some random environmental effects and/or sensing failures,
the collected sensor data is typically noisy. Thus, it is critical to cleanse
the sensor data before using it to answer queries or conduct data analysis. Popular data cleansing approaches, such as classification, prediction
and moving average are not suited for embedded sensor devices, due to
the limited storage and processing capabilities. In this paper, we propose
a sensor data cleansing approach using the relational-based technologies,
including constraints, triggers and granularity-based data aggregation.
The proposed approach is simple but effective to cleanse different types
of dirty data, including delayed data, incomplete data, incorrect data,
duplicate data and missing data. We evaluate the proposed strategy to
verify its efficiency, effectiveness and adaptability.
Original language | English |
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Title of host publication | Proceedings. 19th East-European Conference on Advances in Databases and Information Systems |
Number of pages | 14 |
Publication date | 2015 |
Publication status | Published - 2015 |
Event | 19th East-European Conference on Advances in Databases and Information Systems - Futuroscope, Poitiers, France Duration: 8 Sept 2015 → 11 Sept 2015 Conference number: 19 http://adbis2015.ensma.fr/index.html |
Conference
Conference | 19th East-European Conference on Advances in Databases and Information Systems |
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Number | 19 |
Location | Futuroscope |
Country/Territory | France |
City | Poitiers |
Period | 08/09/2015 → 11/09/2015 |
Internet address |
Keywords
- Data cleansing
- Sensor data
- Relational-based
- Simple
- Effective