@conference {Ajallouda2020169,
	title = {K-means hac and fcm which clustering approach for arabic text?},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {169-174},
	abstract = {Today we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas opinions and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which in reality is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint clustering approaches such as K-mean HAC or FCM can be exploited. In this paper we present and compare these approaches. And to show the importance of exploiting clustering algorithms to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco which is the case of teachers contracting with academies. {\textcopyright} 2020 ACM.},
	keywords = {Arabic texts, Classification (of information), Classification approach, Clustering approach, Intelligent systems, Internet users, K-means clustering, Rapid growth, Real case, Textual data, Web resources},
	doi = {10.1145/3419604.3419779},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096415898\&doi=10.1145\%2f3419604.3419779\&partnerID=40\&md5=8aca383d8f485d15b3da191f03c5bf62},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@conference {Yousfi202085,
	title = {Spm: Preparing hierarchical data structures for schema matching},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {85-90},
	abstract = {Schema pre-matching approaches prepare different schema elements for the matching step. Ideally words are extracted from schema elements{\textquoteright} labels and the semantic correspondences are generated. Identifying the sense of words based on their contexts and before schema matching is critical for increasing the total amount of true matches and decreasing significantly the total amount of both missed matches and false matches. However this problem is very challenging since we often do not have complete and precise information about the meaning behind each element. In this paper we present SPM a Sense-based Pre-Matching approach. SPM has two key modules. First the Sets of Words Generator module generates from every schema element a set of words that describes its meaning. Second the Sense Identifier module selects the most appropriate meaning of words based on their contexts. Experimental results on real-world datasets show high effectiveness achieved by SPM. {\textcopyright} 2020 ACM.},
	keywords = {False matches, Hierarchical data structure, Intelligent systems, Pre-matching, Real-world datasets, Schema matching, Semantic correspondence, Semantics},
	doi = {10.1145/3419604.3419782},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096412741\&doi=10.1145\%2f3419604.3419782\&partnerID=40\&md5=0ace2c18d2f9dd1d741d0d08acb83cfa},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@conference {Reda202091,
	title = {Towards a data quality assessment in big data},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 4},
	pages = {91-96},
	abstract = {In recent years, as more and more data sources have become available and the volumes of data potentially accessible have increased, the assessment of data quality has taken a central role whether at the academic, professional or any other sector. Given that users are often concerned with the need to filter a large amount of data to better satisfy their requirements and needs, and that data analysis can be based on inaccurate, incomplete, ambiguous, duplicated and of poor quality, it makes everyone wonder what the results of these analyses will really be like. However, there is a very complex process involved in the identification of new, valid, potentially useful and meaningful data from a large data collection and various information systems, and is critically dependent on a number of measures to be developed to ensure data quality. To this end, the main objective of this paper is to introduce a general study on data quality related with big data, by providing what other researchers came up with on that subject. The paper will be finalized by a comparative study between the different existing data quality models. {\textcopyright} 2020 ACM.},
	keywords = {Big data, Comparative studies, Complex Processes, data quality, Data quality assessment, Data quality models, Data reduction, Data-sources, Intelligent systems, Large amounts, Large data},
	doi = {10.1145/3419604.3419803},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096426287\&doi=10.1145\%2f3419604.3419803\&partnerID=40\&md5=7542ef0e44f4d94b808c64ce466a5eca},
	author = {Reda, O. and Sassi, I. and Zellou, A. and Anter, S.}
}
