Study of Smart Transportation Data Center Virtualization Based on VMware vSphere and Parallel Continuous Query Algorithm over Massive Data Streams

Jia, Chaolong; Wang, Hanning; Li-li, Wei

doi:10.1016/j.proeng.2016.01.309

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…In the second step of the preprocessing phase, the threshold index positions and thresholds are selected according to ( 4)- (7). Therefore, this operation requires a computation time of only O(1).…”

Section: Complexity Discussionmentioning

confidence: 99%

“…The update of P k-sky (u sw ) in ( 3) is implemented from Lines 10-16. Moreover, lines 3 and 11 of Algorithm 1 use the proposed thresholds in ( 5) and (7) to review the conditions M I min (u old , k) > M I max (u sw , k) and M I min (u new , k) > M I max (u sw , k), respectively. If M I min (u old , k) > M I max (u sw , k) holds at Line 3, u old cannot k-dominates any other items stored in following slots of sorted middle index table, M IT max (k).…”

Section: The Procedures Of Updating K-dominant Skyline Probability Us...mentioning

confidence: 99%

“…If U ′ sw = ∅, the system uses (7) with the data items in U ′ sw to derive P k-sky (u new ). The procedure for this step is presented in Algorithms 6 and 7.…”

Section: The Procedures Of Updating K-dominant Skyline Probability Us...mentioning

confidence: 99%

“…S KYLINE is an efficient analysis tool [2], [3], [4] for solving multiobjective optimization and multi-criteria decisionmaking problems in the big data of the Internet of Things (IoT). It also has been widely studied and applied in numerous applications, such as location-based services [5], [6], transportation [7], [8], mobile computing [9], [10], Internet of Mobile Things [11], [12], [13], and social networks [14], [15]. As no singularly best answer exists for multi-criteria decision-making applications, the skyline (or Pareto-optimal front) has become a popular approach.…”

Section: Introductionmentioning

confidence: 99%

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Highly Efficient Indexing Scheme for k-Dominant Skyline Processing over Uncertain Data Streams

Lai

Lin

Liu

2021

2021 30th Wireless and Optical Communications Conference (WOCC)

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Skyline queries typically search a Pareto-optimal set from a given data set to solve the corresponding multiobjective optimization problem. As the number of criteria increases, the skyline presumes excessive data items, which yield a meaningless result. To address this curse of dimensionality, we proposed a k-dominant skyline in which the number of skyline members was reduced by relaxing the restriction on the number of dimensions, considering the uncertainty of data. Specifically, each data item was associated with a probability of appearance, which represented the probability of becoming a member of the k-dominant skyline. As data items appear continuously in data streams, the corresponding k-dominant skyline may vary with time. Therefore, an effective and rapid mechanism of updating the k-dominant skyline becomes crucial. Herein, we proposed two time-efficient schemes, Middle Indexing (MI) and All Indexing (AI), for k-dominant skyline in distributed edge-computing environments, where irrelevant data items can be effectively excluded from the compute to reduce the processing duration. Furthermore, the proposed schemes were validated with extensive experimental simulations. The experimental results demonstrated that the proposed MI and AI schemes reduced the computation time by approximately 13% and 56%, respectively, compared with the existing method.

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Section: Complexity Discussionmentioning

confidence: 99%

Section: The Procedures Of Updating K-dominant Skyline Probability Us...mentioning

confidence: 99%

“…If U ′ sw = ∅, the system uses (7) with the data items in U ′ sw to derive P k-sky (u new ). The procedure for this step is presented in Algorithms 6 and 7.…”

Section: The Procedures Of Updating K-dominant Skyline Probability Us...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Highly Efficient Indexing Scheme for k-Dominant Skyline Processing over Uncertain Data Streams

Lai

Lin

Liu

2021

2021 30th Wireless and Optical Communications Conference (WOCC)

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“…As a new branch research field in clustering, data stream clustering can incrementally process new data in the streaming environment. It has been gradually applied in several application domains such as medical care and transportation [15][16][17]. In general, data stream clustering algorithms consist of two phases: online and offline.…”

Section: Introductionmentioning

confidence: 99%

DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream

Chen

Zhou

et al. 2022

Int J Comput Intell Syst

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Identifying clusters of arbitrary shapes and constantly processing the newly arrived data points are two critical challenges in the study of clustering. This paper proposes a dynamic weight and density peaks clustering algorithm to simultaneously solve these two key issues. An online–offline framework is used, creating and maintaining micro-clusters in the online phase, and treating the micro-clusters as pseudo-points to form the final cluster in the offline phase. In the online phase, when a new data point is merged into the corresponding micro-cluster, a dynamic weight method is proposed to update the weight of the micro-cluster according to the distance between the point and the center of the micro-cluster, so as to more accurately describe the information of the micro-cluster. In the offline phase, the density peak clustering algorithm is improved, natural neighbors are introduced to adaptively obtain the local density of the data point, and the allocation process is improved to reduce the probability of allocation errors. The algorithm is evaluated on different synthetic and real-world datasets using different quality metrics. The experimental results show that the proposed algorithm improves the clustering quality in both static and streaming environments.

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Using 5 parallel passive data streams to report on a wide range of mobility options

Morency

Trépanier

Saunier

et al. 2018

Transportation Research Procedia

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Study of Smart Transportation Data Center Virtualization Based on VMware vSphere and Parallel Continuous Query Algorithm over Massive Data Streams

Cited by 10 publications

References 7 publications

Highly Efficient Indexing Scheme for k-Dominant Skyline Processing over Uncertain Data Streams

Highly Efficient Indexing Scheme for k-Dominant Skyline Processing over Uncertain Data Streams

DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream

Using 5 parallel passive data streams to report on a wide range of mobility options

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