Probabilistic Top- ${k}$ Dominating Query Monitoring Over Multiple Uncertain IoT Data Streams in Edge Computing Environments

@article{Lai2019ProbabilisticT,
  title={Probabilistic Top- \$\{k\}\$ Dominating Query Monitoring Over Multiple Uncertain IoT Data Streams in Edge Computing Environments},
  author={Chuan-Chi Lai and Tien-Chun Wang and Chuan-Ming Liu and Li-Chun Wang},
  journal={IEEE Internet of Things Journal},
  year={2019},
  volume={6},
  pages={8563-8576}
}
Extracting the valuable features and information in big data has become one of the important research issues in data science. In most Internet of Things (IoT) applications, the collected data are uncertain and imprecise due to sensor device variations or transmission errors. In addition, the sensing data may change as time evolves. We refer an uncertain data stream as a dataset that has velocity, veracity, and volume properties simultaneously. This paper employs the parallelism in edge… Expand
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References

SHOWING 1-10 OF 32 REFERENCES
Continuous Monitoring of Top-k Dominating Queries over Uncertain Data Streams
TLDR
This work formally defines the problem of continuous probabilistic top-k dominating (PTOPK) query processing over uncertain data streams based on a count-based sliding window model and proposes an efficient postponed examination algorithm (PEA) to solve the problem. Expand
Identifying Top k Dominating Objects over Uncertain Data
TLDR
The top k dominating model is formally introduced based on the state-of-the-art top k semantic over uncertain data and novel pruning techniques are proposed by utilizing the spatial indexing and statistic information, which significantly improve the performance of the algorithms in terms of CPU and I/O costs. Expand
A survey of queries over uncertain data
TLDR
This paper presents and analyzes several typical uncertain queries, such as skyline queries, top-$$k$$ queries, nearest-neighbor queries, aggregate queries, join queries, range queries, and threshold queries over uncertain data, and summarizes the main features of uncertain queries. Expand
Sliding window top-k dominating query processing over distributed data streams
TLDR
Two algorithms are presented that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. Expand
An Effective Probabilistic Skyline Query Process on Uncertain Data Streams
TLDR
This paper proposes an effective approach, Efficient Probabilistic Skyline Update (EPSU), using a new data structure by augmenting the R-tree structure and shows that EPSU can effectively compute the probabilistic skyline query in terms of the time and space and outperforms the existing ones. Expand
Top-k Dominating Queries on Incomplete Data
TLDR
This paper carries out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s), and proposes a suite of efficient algorithms for answering TkD queries over incomplete data. Expand
Space Filling Approach for Distributed Processing of Top-k Dominating Queries
TLDR
An efficient decentralized algorithm that exploits virtual points and returns the exact answer is proposed, which is utilized to focus on the data space to be preferentially searched and also to limit the search space to prune unnecessary computation and data forwarding. Expand
Finding Top- $k$ Dominance on Incomplete Big Data Using MapReduce Framework
TLDR
The proposed MapReduced Enhanced Bitmap Index Guided Algorithm (MRBIG) uses the MapReduce framework to enhance the performance of applying top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-Math></inline- formula> dominance queries on large incomplete datasets. Expand
Threshold-based probabilistic top-k dominating queries
TLDR
This paper formally defines the problem of efficiently computing top-k dominating queries on uncertain data, and develops an efficient, threshold-based algorithm to compute the exact solution and an efficient randomized algorithm with an accuracy guarantee. Expand
Continuous monitoring of top-k queries over sliding windows
TLDR
This paper presents two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. Expand
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