Abstract
The application of the Internet of Things has produced large amounts of data in different scenarios, which are accompanied with problems, such as consistency and integrity violations. Existing research on dealing with data availability violations is insufficient. In this work, the detection and repair of data availability violations (DRAV) framework is proposed to detect and repair data violations in Internet of Things with a distributed parallel computing environment. DRAV uses algorithms in the MapReduce programming framework, and these include detection and repair algorithms based on enhanced conditional function dependency for data consistency violation, MapJoin, and ReduceJoin algorithms based on master data for k-nearest neighbor–based integrity violation detection, and repair algorithms. Experiments are conducted to determine the effect of the algorithms. Results show that DRAV improves data availability in Internet of Things compared with existing methods by detecting and repairing violations.
Highlights
The Internet of Things (IoT) connect multiple information producers with sensors and actuators
DRAV proposes algorithms in the MapReduce programming framework, including the consistency violation detection and repair algorithm based on enhanced conditional functional dependencies, MapJoin, and ReduceJoin algorithms based on primary data, and a processing algorithm that deals with integrity violations based on k-nearest neighbor (k-NN)
Our work focused on the consistency and integrity of data availability in IoT and proposed a DRAV framework that contains a series of processing algorithms
Summary
The Internet of Things (IoT) connect multiple information producers with sensors and actuators. The concurrency and multi-distribution of the parallel environment in IoT complicate the problem of data availability violation in detection and repair. DRAV proposes algorithms in the MapReduce programming framework, including the consistency violation detection and repair algorithm based on enhanced conditional functional dependencies (xCFDs), MapJoin, and ReduceJoin algorithms based on primary data, and a processing algorithm that deals with integrity violations based on k-nearest neighbor (k-NN). The proposed xCFD extends existing functional dependencies, integrates high-quality data into the logic system of CFD, and further enhances the ability to detect and repair data consistency violations by eliminating conflicts. 3. The distributed solution of detecting and repairing data availability violations in IoT is realized by designing related algorithms in the MapReduce programming framework
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