Name
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 data before using it for answering queries or for data analysis. Popular data cleansing approaches, such as classification, prediction and moving average, are not suited for embedded sensor devices, due to their limit 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 and effectiveness. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23201-0_13 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Data cleansing,Sensor data,Relational-based,Dirty data | Data mining,Data cleansing,Information retrieval,Computer science,Dirty data,Missing data,Moving average,Data aggregator,Database | Conference |
Volume | ISSN | Citations |
539 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadeem Iftikhar | 1 | 80 | 11.50 |
| Xiufeng Liu | 2 | 108 | 14.69 |
| Finn Ebertsen Nordbjerg | 3 | 4 | 1.83 |