Multiple MapReduce and derivative projected database: New approach for supporting PrefixSpan scalability

Sabrina, Puspita Nurul; Saptawati, Gusti Ayu Putri

doi:10.1109/icodse.2015.7436988

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…Sequential Pattern Mining algorithms are also implemented and parallelized on Hadoop and Spark to get rid of the load and serial operation overhead. [20][21][22]48,49 Yu et al proposed a distributed GSP (DGSP) algorithm based on MapReduce on Hadoop. 20 The DGSP algorithm partitions the database and assign jobs to map workers and so optimizes the workload balance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Parallel PrefixSpan algorithms based on Hadoop were proposed by Wei et al 48 and Sabrina et al 49 which process big data by using MapReduce jobs. Since Hadoop requires IO operations on the HDFS, they have a high IO overhead.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sequential Pattern Mining algorithms are also implemented and parallelized on Hadoop and Spark to get rid of the load and serial operation overhead 20‐22,48,49 . Yu et al proposed a distributed GSP (DGSP) algorithm based on MapReduce on Hadoop 20 .…”

Section: Fundamental Concepts and Literature Reviewmentioning

confidence: 99%

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Scalable recommendation systems based on finding similar items and sequences

Uzun‐Per

Gurel²,

Can³

et al. 2022

Concurrency and Computation

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The rapid growth in the airline industry, which started in 2009, continued until the COVID-19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi-Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application. We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Scalable recommendation systems based on finding similar items and sequences

Uzun‐Per

Gurel²,

Can³

et al. 2022

Concurrency and Computation

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“…Data sources can be derived and obtained from the various application. Social media and social networking data [4]. Sales history data [5], Benefiting from brand society initiatives and the prospective benefits of social media can be one of the sources.…”

Section: Analysis and Review Of Data Sourcesmentioning

confidence: 99%

“…Then presents a revision of a new methodology designed to enable efficient mining of data and the incorporation of information from social media and from new applications and frameworks that nowadays appear below the "umbrella" of social networks, social media and the big data paradigm [3]. The purchasing pattern can be used by the marketing department to build an optimal marketing strategy [4].…”

Section: Big Data and Machine Learningmentioning

confidence: 99%

Social Influence Data Analytic For Supply Chain Management in Fashion Industry

Sabrina¹,

Umbara²,

Ashaury³

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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Supply chain management in the area of fashion is a flow that is interconnected from upstream which is a supplier of fashion production to the downstream who are users of fashion products. In the downstream data of customer experience is one of the concepts (use case) which avoids out of stock as a competitive advantage factor. The analytic data process involves various entities in line with the massive growth of electronic commerce and social media which provides a social influence on customer purchases. Data analysis requires relevant data and data resources of customer activities and trends and appropriate methods to produce effective knowledge for the needs of the supply chain process in the midstream and downstream. This research produces an architectural model that describes the objectives, data/data resources and relevant methods used in the analytical data process of social influence on the supply chain of the fashion industry.

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On the big data processing algorithms for finding frequent sequences

Can¹,

Zaval

Uzun‐Per

et al. 2023

Concurrency and Computation

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Sequential pattern mining algorithms extract trendy sequence appearances inside ordered transactional datasets such as market basket datasets. There is a lack of research employing big data processing techniques to locate frequent sequences on large‐scale datasets. Furthermore, there is a need for optimized sequential pattern mining algorithms that run on ordered one‐dimensional sequences. We also observe a lack of sequential pattern search studies in the literature, where the focus is centered around multi‐dimensional data sequences. Existing approaches that deal with ordered one‐dimensional datasets suffer from scalability issues as the amount of data to be analyzed is enormous. This research investigates the big data processing techniques used to find frequent sequences in large‐scale datasets. It also proposes a scalable sequence pattern mining algorithm called Sequential Pattern Acquisition by Reducing Search Space (SPARSS) designed for distributed data processing systems that efficiently handle large datasets containing sequential one‐element data. It introduces a prototype implementation of SPARSS and provides information on the SPARSS's memory and time requirements, which were calculated as part of experimental studies on a real‐world dataset. The results confirm our expectations and demonstrate SPARSS's superior scalability and run‐time efficiency compared to other distributed algorithms.

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Multiple MapReduce and derivative projected database: New approach for supporting PrefixSpan scalability

Cited by 6 publications

References 3 publications

Scalable recommendation systems based on finding similar items and sequences

Scalable recommendation systems based on finding similar items and sequences

Social Influence Data Analytic For Supply Chain Management in Fashion Industry

On the big data processing algorithms for finding frequent sequences

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