Security Issues in Data Warehouse: A Systematic Review

Gosain, Anjana; Arora, Amar

doi:10.1016/j.procs.2015.04.164

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…3. Main algorithm of WINEPI [7] The algorithm runs multiple passes over the data. First it determines individual frequency count for each element in collection L1 and counts the number of occurrences present in each window.…”

Section: B Main Algorithmmentioning

confidence: 99%

“…We need tools to mine the produced data so that it can instantly show the value for its users. We define data mining as discovering interesting, and potentially useful patterns from data through applying algorithms in order to extract novel, previously hidden knowledge and relations [7]. The WINEPI algorithm is applied to the experimental dataset in order to test the possibilities of mining frequent sequential patterns [8].…”

Section: Introductionmentioning

confidence: 99%

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Applying Sequential Pattern Mining to Portable RFID System Data

Sjöman¹,

Steinert²

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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Abstract-This paper presents how data mining can be applied to RFID proximity tracking data captured in a production setting. The WINEPI algorithm is explained and used for mining sequential patterns from transaction data produced by portable RF transceivers that can be attached, for example, to the personnel and machines of a production facility. The contribution of this paper is the additional mindset of how data can be produced for a data warehouse with dedicated sensors in order to prototype the data warehouse itself -and how we can use this created knowledge as a help when designing intelligent manufacturing systems.

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Section: B Main Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%