Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter
Abstract Launching new products in the consumer electronics market is challenging. Developing and marketing the same in limited time affect the sustainability of such companies. This research work introduces a model that can predict the success of a product. A Feature Information Gain (FIG) measure...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-02-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-020-00292-y |
| _version_ | 1818179085229096960 |
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| author | Sandhya Narayanan Philip Samuel Mariamma Chacko |
| author_facet | Sandhya Narayanan Philip Samuel Mariamma Chacko |
| author_sort | Sandhya Narayanan |
| collection | DOAJ |
| description | Abstract Launching new products in the consumer electronics market is challenging. Developing and marketing the same in limited time affect the sustainability of such companies. This research work introduces a model that can predict the success of a product. A Feature Information Gain (FIG) measure is used for significant feature identification and Distributed Memory-based Resilient Dataset Filter (DMRDF) is used to eliminate duplicate reviews, which in turn improves the reliability of the product reviews. The pre-processed dataset is used for prediction of product pre-launch in the market using classifiers such as Logistic regression and Support vector machine. DMRDF method is fault-tolerant because of its resilience property and also reduces the dataset redundancy; hence, it increases the prediction accuracy of the model. The proposed model works in a distributed environment to handle a massive volume of the dataset and therefore, it is scalable. The output of this feature modelling and prediction allows the manufacturer to optimize the design of his new product. |
| first_indexed | 2024-12-11T20:58:16Z |
| format | Article |
| id | doaj.art-3b7a7d59b92641ce9b044d3e25f48e97 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-11T20:58:16Z |
| publishDate | 2020-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-3b7a7d59b92641ce9b044d3e25f48e972022-12-22T00:51:03ZengSpringerOpenJournal of Big Data2196-11152020-02-017111510.1186/s40537-020-00292-yImproving prediction with enhanced Distributed Memory-based Resilient Dataset FilterSandhya Narayanan0Philip Samuel1Mariamma Chacko2Information Technology, School of Engineering, Cochin University of Science & TechnologyDepartment of Computer Science, Cochin University of Science & TechnologyDepartment of Ship Technology, Cochin University of Science & TechnologyAbstract Launching new products in the consumer electronics market is challenging. Developing and marketing the same in limited time affect the sustainability of such companies. This research work introduces a model that can predict the success of a product. A Feature Information Gain (FIG) measure is used for significant feature identification and Distributed Memory-based Resilient Dataset Filter (DMRDF) is used to eliminate duplicate reviews, which in turn improves the reliability of the product reviews. The pre-processed dataset is used for prediction of product pre-launch in the market using classifiers such as Logistic regression and Support vector machine. DMRDF method is fault-tolerant because of its resilience property and also reduces the dataset redundancy; hence, it increases the prediction accuracy of the model. The proposed model works in a distributed environment to handle a massive volume of the dataset and therefore, it is scalable. The output of this feature modelling and prediction allows the manufacturer to optimize the design of his new product.http://link.springer.com/article/10.1186/s40537-020-00292-yDistributed Memory-basedResilient Distribution DatasetRedundancy |
| spellingShingle | Sandhya Narayanan Philip Samuel Mariamma Chacko Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter Journal of Big Data Distributed Memory-based Resilient Distribution Dataset Redundancy |
| title | Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter |
| title_full | Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter |
| title_fullStr | Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter |
| title_full_unstemmed | Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter |
| title_short | Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter |
| title_sort | improving prediction with enhanced distributed memory based resilient dataset filter |
| topic | Distributed Memory-based Resilient Distribution Dataset Redundancy |
| url | http://link.springer.com/article/10.1186/s40537-020-00292-y |
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