A Spark Based Big Data Analytics Framework for Competitive Intelligence

Adil, Bouktaib; Fennan, Abdelhadi; Mohamed, Bahra; Haytam, Hmami

doi:10.1109/icssd47982.2019.9002837

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…This framework is gaining a lot of attention in the big data community and its use in a variety of applications [23] proved to give efficient results when dealing with large datasets. Hence we chose this framework in our attempt to develop a competitive intelligence system [24] to analyze and extract strategic information from the increasing amounts of data available in the environment of companies and organizations.…”

Section: Apache Sparkmentioning

confidence: 99%

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

Adil¹,

Abdelhadi²

2021

IJACSA

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Companies nowadays are sharing a lot of data on the web in structured and unstructured format, the data holds many signals from which we can analyze and detect innovation using weak signal detection approaches. To gain a competitive advantage over competitors, the velocity and volume of data available online must be exploited and processed to extract and monitor any type of strategic challenge or surprise whether it is in form of opportunities or threats. To capture early signs of a change in the environment in a big data context where data is voluminous and unstructured, we present in this paper a framework for weak signal detection relying on the crawling of a variety of web sources and big data based implementation of text mining techniques for the automatic detection and monitoring of weak signals using an aggregation approach of semantic clustering algorithms. The novelty of this paper resides in the capability of the framework to extend to an unlimited amount of unstructured data, that needs novel approaches to analyze, and the aggregation of semantic clustering algorithms for better automation and higher accuracy of weak signal detection. A corpus of scientific articles and patents is collected in order to validate the framework and provide a use case for future interested researchers in identifying weak signals in a corpus of data of a specific technological domain.

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Section: Apache Sparkmentioning

confidence: 99%

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

Adil¹,

Abdelhadi²

2021

IJACSA

Self Cite

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An efficient ACO-PSO-based framework for data classification and preprocessing in big data

2020

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Grey Wolf Shuffled Shepherd Optimization Algorithm-Based Hybrid Deep Learning Classifier for Big Data Classification

Banchhor¹,

Srinivasu

2022

International Journal of Swarm Intelligence Research

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In recent days, big data is a vital role in information knowledge analysis, predicting and manipulating process. Moreover, big data is well-known for systematic extraction and analysis of large or difficult databases. Furthermore, it is widely useful in data management as compared with conventional data processing approach. The development in big data is highly increasing gradually, such that traditional software tool faced various issues during big data handling. However, data imbalance in huge databases is a main limitation in research area. The scaling evolution up to huge scale database is very challenging task in big data era. In this paper, Grey wolf Shuffled Shepherd Optimization Algorithm (GWSSOA)-based Deep Recurrent Neural Network (DRNN) algorithm is devised for big data classification. In this technique, hybrid classifier, termed as Holoentropy based Correlative Naive Bayes classifier (HCNB) and DRNN classifier is introduced for the classification of big data.

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A Spark Based Big Data Analytics Framework for Competitive Intelligence

Cited by 3 publications

References 15 publications

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

An efficient ACO-PSO-based framework for data classification and preprocessing in big data

Grey Wolf Shuffled Shepherd Optimization Algorithm-Based Hybrid Deep Learning Classifier for Big Data Classification

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