A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario

Verma, Neha; Singh, Jatinder

doi:10.1080/23270012.2017.1373261

Cited by 21 publications

(7 citation statements)

References 29 publications

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“…A resurgence in Artificial Intelligence and Machine Learning seems to have accelerated out of innovations such as "Hadoop" [41] with algorithms such as "Map reduce" [42] becoming available from the open source community. This led to a widespread recognition of value from data mining across multiple sources and stimulated the emergence of Big Data [43].…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Digital construction: From point solutions to IoT ecosystem

Woodhead

Stephenson

Morrey

2018

Automation in Construction

239

176

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This paper takes a longitudinal view of literature to explain the current period as disruptive technology drives an evolutionary adaptation of the construction industry in a historical socio-technological process. The authors argue the way Internet of Things (IoT) solutions are conceived as singularly focused "point solutions" undermine future opportunities. An evolutionary view is overlooked because extant literature describes technology in a particular epoch. An ecosystem perspective needs to influence IT strategy as an emerging "digital layer" transcends a smart city and continues to function long after a traditional construction project completes. We describe innovation as a succession of transformational waves in an evolutionary process that is currently manifesting as "Industry 4.0" and changing expectations for the construction industry. The paper concludes by listing emerging trends and warns existing UK construction companies must understand the transformational process they are in and learn how to adapt with a stronger drive for R&D.

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Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Digital construction: From point solutions to IoT ecosystem

Woodhead

Stephenson

Morrey

2018

Automation in Construction

239

176

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“…On a more practical level the findings in [23], obtained through surveys with leading retailers showed that MBA is being used by most progressive retailers to understand customer habits and adjust operations to obtain maximum success. This fact was also noted more recently in [12] and [65]. Further, all three studies ( [12], [23] and [65]) noted that providing retail operational teams with real-time MBA is allowing for rapid adjustments to be made to the business thereby capitalising on every opportunity and enabling the entire organisation to work smarter.…”

Section: Introductionmentioning

confidence: 58%

A product-centric data mining algorithm for targeted promotions

Moodley

Chiclana

Caraffini

et al. 2020

Journal of Retailing and Consumer Services

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Targeted promotions in retail are becoming increasingly popular, particularly in UK grocery retail sector where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimising items to target items and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector show that the algorithm performs well in finding the best items and customers to target whilst eliminating "false positives" (targeting customers who do not buy a product) and reducing "false negatives" (not targeting customers who could buy). The algorithm also shows better performance when compared to a similar published framework, particularly in handling "false positives" and "false negatives". The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.

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“…It also describes business analytics that shows many companies are still looking for better ways to gain value from their data to be competitive in the market. Recently, Verma and Singh () presented a review article on sequential association computing to Hadoop‐MapReduce parallel computing. The objective of this study is to identify gaps, providing opportunities for new research and to recognize expansion of big data analytics with retail environment and its future directions.…”

Section: Related Workmentioning

confidence: 99%

Mining of high‐utility itemsets with negative utility

et al. 2018

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High‐utility itemset (HUI) mining is an important tasks during data mining. Recently, many algorithms have been proposed to discover HUIs. Most of the algorithms work only for itemsets with positive utility values. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Efficient High‐utility Itemsets mining with Negative utility (EHIN) to find all HUIs with negative utility. EHIN utilises 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilised to calculate upper‐bound efficiently. EHIN utilises various properties and pruning strategies to mine HUIs with negative utility. The experimental results show that the proposed algorithm is 28 times faster, and it consumes up to 10 times less memory than the state‐of‐the‐art algorithm FHN. Moreover, a key advantage is that EHIN always performs better for dense datasets.

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A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario

Cited by 21 publications

References 29 publications

Digital construction: From point solutions to IoT ecosystem

Digital construction: From point solutions to IoT ecosystem

A product-centric data mining algorithm for targeted promotions

Mining of high‐utility itemsets with negative utility

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