MapReduce based frequent itemset mining algorithm on stream data

Chaudhary, Hemant; Yadav, Deepak; Bhatnagar, Rajat; Chandrasekhar, Uddagiri

doi:10.1109/gcct.2015.7342732

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…It was found that the increasing stock size did not give much impact on execution time. Execution time is also inversely proportional to the number of nodes [10]. The MapReduce framework can be used for mining frequent itemsets to infer greater scalability and speed in order to find out the meaningful information from large datasets [11].…”

Section: Previous Workmentioning

confidence: 99%

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The MapReduce Model on Cascading Platform for Frequent Itemset Mining

Rokhman¹,

Nursanti²

2018

Indonesian J. Comput. Cybern. Syst.

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Section: Previous Workmentioning

confidence: 99%

“…It There are many other application which use FIM on Hadoop MapReduce. Among of this generates the association rules in the transactional data stream [10] and handles FIM in Social Network Data [11].…”

Section: Previous Workmentioning

confidence: 99%

The MapReduce Model on Cascading Platform for Frequent Itemset Mining

Rokhman¹,

Nursanti²

2018

Indonesian J. Comput. Cybern. Syst.

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“…China's Ministry of Science and Technology announced the special plan for the development of electric vehicle technology. The development of electric vehicles is an important way to improve the competitiveness of the automotive industry, ensure energy security and develop low carbon economy [6][7][8]. In the coming years, it will be a strategic opportunity for research and industrialization of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Energy Consumption Model and Energy Consumption Characteristics of Electric Vehicle

Jia¹

2018

Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)

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The modeling of energy consumption of electric vehicles is of great significance for meticulous depiction and evaluation of energy saving driving control effect of electric vehicles. Compared with the mesoscopic energy consumption model based on the driving segment, the proposed model can be more accurately estimated. The energy consumption of electric vehicles is generally suitable for detailed characterization of the energy consumption characteristics of electric vehicles, as well as the quantitative estimation of the energy consumption of electric vehicles in small areas such as roads and intersections. In this paper, the driving data of electric vehicle is acquired by the actual driving mode of outdoor roads. The energy consumption model of electric vehicle is established based on the analysis data. Finally, various driving control modes and their energy efficiency improvement are analyzed and evaluated.

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