Smart4Job: A Big Data Framework for Intelligent Job Offers Broadcasting Using Time Series Forecasting and Semantic Classification

Benabderrahmane, Sidahmed; Mellouli, Nédra; Lamolle, Myriam; Paroubek, Patrick

doi:10.1016/j.bdr.2016.11.001

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…Due to the high computational cost and the large volume of spatiotemporal data available, some works in Big Data [ de Oliveira et al 2019;Chi et al 2016;Comber and Wulder 2019] introduced parallel and distributed solutions for processing spatiotemporal data, particularly to support the demand for real-time processing [Ma et al 2015]. Some researchers describe the use of Big Data in several domains of temporal and spatial data processing, such as Smart Farming [Wolfert et al 2017;Braga et al 2019], monitoring of water resources [Wagner et al 2014], remote sensing image analysis [Rathore et al 2015], IoT [Wang et al 2015], recommendation systems [Benabderrahmane et al 2017] and time series data mining [Fawaz et al 2019;Amaral and de Sousa 2020;Romani et al 2010].…”

Section: Related Workmentioning

confidence: 99%

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Oliveira

Rodrigues

Martins

2021

jidm

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Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

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Section: Related Workmentioning

confidence: 99%

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Oliveira

Rodrigues

Martins

2021

jidm

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“…To this aim, several recommendation systems have been presented in the literature. These systems are generally classified into three main categories: textual recommendation systems [38], collaborative filtering recommendation systems, and hybrid recommendation systems [39], [40].…”

Section: E-recruitment Recommender Systemsmentioning

confidence: 99%

On the predictive analysis of behavioral massive job data using embedded clustering and deep recurrent neural networks

Benabderrahmane

Mellouli

Lamolle

2018

Knowledge-Based Systems

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The recent proliferation of social networks as a main source of information and interaction has led to a huge expansion of automatic e-recruitment systems and by consequence the multiplication of web channels (job boards) that are dedicated to job offers disseminating. In a strategic and economic context where cost control is fundamental, it has become necessary to identify the relevant job board for a given new job offer has become necessary. The purpose of this work is to present the recent results that we have obtained on a new job board recommendation system that is a decision-making tool intended to guide recruiters while they are posting a job on the Internet. Firstly, the Doc2Vec embedded representation is used to analyse the textual content of the job offers, then the job applicant clickstreams history on various job boards are stored in a large learning database, and then represented as time series. Secondly, a deep neural network architecture is used to predict future values of the clicks on the job boards. Third, and in parallel, dimensionality reduction techniques are used to transform the clicks numerical time series into temporal symbolic sequences. Forecasting algorithms are then used to predict future symbols for each sequence. Finally, a list of top ranked job boards are kept by maximizing the clickstreams forecasting in both representations. Our experiments are tested on a real dataset, coming from a job-posting database of an industrial partner. The promising results have shown that using deep learning, the recommendation system outperforms standard multivariate models.

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“…The data size is increased because of the technological developments in the new data computing field. Big data refers to the large dataset unable to manage and handle the classical database systems [1]. Big data handling is a major challenge in every application due to rapid data gathering and storage, networking, and other related techniques [2,3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

Banchhor

Srinivasu

2021

J Big Data

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The process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for overall handling. The classifiers analyzed in this paper are correlative naïve Bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB), and Holoentropy CNB (HCNB). These classifiers are based on the Bayesian principle and work accordingly. The CNB is developed by extending the standard naïve Bayes classifier with applied correlation among the attributes to become a dependent hypothesis. The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier, and significant performance improvement is achieved. The resulting classifier is called a cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). Also, the performance of the FCNB and HCNB classifiers are analyzed with CNB and CGCNB by considering accuracy, sensitivity, specificity, memory, and execution time.

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Smart4Job: A Big Data Framework for Intelligent Job Offers Broadcasting Using Time Series Forecasting and Semantic Classification

Cited by 20 publications

References 8 publications

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

On the predictive analysis of behavioral massive job data using embedded clustering and deep recurrent neural networks

Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

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