A Filtering approach used in a massive data context to reduce the set of choices in a multicriteria decision aid process: Pareto solutions

Lamrini, Loubna; Abounaima, Mohammed Chaouki; Alaoui, Mohammed Talibi; Mazouri, Fatima Zahra El; Makhfi, Noureddine El; Ouzarf, Mohamed

doi:10.1109/icecocs50124.2020.9314445

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Several solutions have been proposed to cope with the increase in data flow and the large number of alternatives to be processed. In [11][12][13], screening techniques were proposed to eliminate, from the beginning of the decision-making process, the alternatives deemed irrelevant. Screening makes it possible to reduce the size of the decision matrix, but in the case of a ranking process, the filtered alternatives will not appear in the final list, which contains the sorted alternatives.…”

Section: Introductionmentioning

confidence: 99%

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

2023

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Nowadays, the online environment is extra information-rich and allows companies to offer and receive more and more options and opportunities in multiple areas. Thus, decision-makers have abundantly available alternatives to choose from the best one or rank from the most to the least preferred. However, in the multicriteria decision-making field, most tools support a limited number of alternatives with as narrow criteria as possible. Decision-makers are forced to apply a screening or filtering method to reduce the size of the problem, which will slow down the process and eliminate some potential alternatives from the rest of the decision-making process. Implementing MCDM methods in high-performance parallel and distributed computing environments becomes crucial to ensure the scalability of multicriteria decision-making solutions in Big Data contexts, where one can consider a vast number of alternatives, each being described on the basis of a number of criteria.In this context, we consider TOPSIS one of the most widely used MCDM methods. We present a parallel implementation of TOPSIS based on the MapReduce paradigm. This solution will reduce the response time of the decision-making process and facilitate the analysis of the robustness and sensitivity of the method in a high-dimension problem at a reasonable response time.Three multicriteria analysis problems were evaluated to show the proposed approach's computational efficiency and performance. All experiments are carried out within GCP's Dataproc, a service allowing the execution of Apache Hadoop and Spark tasks in Google Cloud. The results of the tests obtained are very significant and promising.

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Section: Introductionmentioning

confidence: 99%

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

2023

Self Cite

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“…Several solutions have been proposed to cope with the increase in data flow and the large number of alternatives to be processed. In [12] [13] [14], screening techniques were proposed to eliminate, from the beginning of the decision-making process, the alternatives deemed irrelevant. Screening makes it possible to reduce the size of the decision matrix, but in the case of a ranking process, the filtered alternatives will not appear in the final list, which contains the sorted alternatives.…”

Section: Introductionmentioning

confidence: 99%

New Distributed-TOPSIS Approach for Multi-Criteria Decision-Making Problems in a Big Data Context

Lamrini

Abounaima

Alaoui

2022

Preprint

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Nowadays, the online environment is extra information-rich and allows companies to offer and receive more and more options and opportunities in multiple areas. Thus, decision-makers have abundantly available alternatives to choose from the best one or rank from the most to the least preferred. However, in the multicriteria decision-making field, most tools support a limited number of alternatives with as narrow criteria as possible. Decision-makers are forced to apply a screening or filtering method to reduce the size of the problem, which will slow down the process and eliminate some potential alternatives from the rest of the decision-making process. Implementing MCDM methods in high-performance parallel and distributed computing environments becomes crucial to ensure the scalability of multicriteria decision-making solutions in Big Data contexts, where one can consider a vast number of alternatives, each being described on the basis of a number of criteria. In this context, we consider TOPSIS one of the most widely used MCDM methods. We present a parallel implementation of TOPSIS based on the MapReduce paradigm. This solution will reduce the response time of the decision-making process and facilitate the analysis of the robustness and sensitivity of the method in a high-dimension problem at a reasonable response time. Three multicriteria analysis problems were evaluated to show the proposed approach's computational efficiency and performance. All experiments are carried out within GCP's Dataproc, a service allowing the execution of Apache Hadoop and Spark tasks in Google Cloud. The results of the tests obtained are very significant and promising.

show abstract

A Filtering approach used in a massive data context to reduce the set of choices in a multicriteria decision aid process: Pareto solutions

Cited by 2 publications

References 6 publications

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

New Distributed-TOPSIS Approach for Multi-Criteria Decision-Making Problems in a Big Data Context

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