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With a greater ability for Big Data to obtain and compute such data sets, came a loss in the ability to ensure accurate data collection using traditional methods. This study aims to develop a model for data integrity, accurately detecting and compensating for errors and data drift. We will be working with a real-time water monitoring device that measures data in regular intervals. The data is managed in a decentralized manner by a Cloud-based Database Management System. We begin by exploring various drift compensation techniques. Afterwards, several algorithms implemented in similar studies are investigated. Upon choosing the optimal algorithm with any proper modifications, it is then implemented into an environmental sensing web application. In addition, new data visualization techniques are proposed to promote awareness for when data begins to drift. |
