2020
|
Salma El Hajjami; Jamal Malki; Mohammed Berrada; Bouziane Fourka Machine Learning for Anomaly Detection. Performance Study considering Anomaly Distribution in an Unbalanced Dataset Inproceedings IEEE, (Ed.): The 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, IEEE Xplore Digital Library, 2020, ISBN: 978-1-7281-6175-4. Résumé | Liens | BibTeX | Étiquettes: @inproceedings{salmaCloudtech2020,
title = {Machine Learning for Anomaly Detection. Performance Study considering Anomaly Distribution in an Unbalanced Dataset},
author = {Salma El Hajjami and Jamal Malki and Mohammed Berrada and Bouziane Fourka},
editor = {IEEE},
url = {http://www.macc.ma/cloudtech20},
isbn = {978-1-7281-6175-4},
year = {2020},
date = {2020-11-24},
booktitle = {The 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications},
publisher = {IEEE Xplore Digital Library},
abstract = {The continuous dematerialization of real-world data greatly contributes to the increase in the volume of data exchanged. In this case, anomaly detection is increasingly becoming an important task of data analysis in order to detect abnormal data, which is of particular interest and may require action. Recent advances in artificial intelligence approaches, such as machine learning, are making an important breakthrough in this area. Typically, these techniques have been designed for balanced data sets or that have certain assumptions about the distribution of data. However, the real applications are rather confronted with an imbalanced data distribution, where normal data are present in large quantities and abnormal cases are generally very few. This makes anomaly detection similar to looking for the needle in a haystack. In this article, we develop an experimental setup for comparative analysis of two types of machine learning techniques in their application to anomaly detection systems. We study their performance taking into account anomaly distribution in an imbalanced dataset.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The continuous dematerialization of real-world data greatly contributes to the increase in the volume of data exchanged. In this case, anomaly detection is increasingly becoming an important task of data analysis in order to detect abnormal data, which is of particular interest and may require action. Recent advances in artificial intelligence approaches, such as machine learning, are making an important breakthrough in this area. Typically, these techniques have been designed for balanced data sets or that have certain assumptions about the distribution of data. However, the real applications are rather confronted with an imbalanced data distribution, where normal data are present in large quantities and abnormal cases are generally very few. This makes anomaly detection similar to looking for the needle in a haystack. In this article, we develop an experimental setup for comparative analysis of two types of machine learning techniques in their application to anomaly detection systems. We study their performance taking into account anomaly distribution in an imbalanced dataset. |
Salma El Hajjami; Jamal Malki; Alain Bouju; Mohammed Berrada Machine Learning Facing Behavioral Noise Problem in an Imbalanced Data Using One Side Behavioral Noise Reduction: Application to a Fraud Detection Article de journal World Academy of Science, Engineering and Technology International. Journal of Computer and Information Engineering, 14 (9), 2020. Résumé | Liens | BibTeX | Étiquettes: @article{salma2020Lisbon,
title = {Machine Learning Facing Behavioral Noise Problem in an Imbalanced Data Using One Side Behavioral Noise Reduction: Application to a Fraud Detection},
author = {Salma El Hajjami and Jamal Malki and Alain Bouju and Mohammed Berrada},
editor = {publications.waset.org},
url = {https://publications.waset.org/abstracts/127869/pdf},
year = {2020},
date = {2020-09-14},
journal = {World Academy of Science, Engineering and Technology International. Journal of Computer and Information Engineering},
volume = {14},
number = {9},
abstract = {With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem
that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is
present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these
cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this
difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work
in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced
data, is the overlapping instances between the two classes. It’s commonly referred as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlaps with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem
that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is
present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these
cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this
difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work
in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced
data, is the overlapping instances between the two classes. It’s commonly referred as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlaps with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise. |
Thouraya Sakouhi; Jamal Malki; Jalel Akaichi A mobility data model for web-based tourists tracking Inproceedings CEUR-WS.org, (Ed.): The 14th international baltic conference on databases and information systems (balticdb&is 2020), Tallinn, Estonia, 2020, ISSN: 1613-0073. Résumé | Liens | BibTeX | Étiquettes: @inproceedings{sakouhi2020,
title = {A mobility data model for web-based tourists tracking},
author = {Thouraya Sakouhi and Jamal Malki and Jalel Akaichi},
editor = {CEUR-WS.org},
url = {http://ceur-ws.org/Vol-2620/paper1.pdf},
issn = {1613-0073},
year = {2020},
date = {2020-01-01},
booktitle = {The 14th international baltic conference on databases and information systems (balticdb&is 2020)},
volume = {2620},
number = {9},
address = {Tallinn, Estonia},
abstract = {Tracking tourists activities at different levels of their jour- neys provides an overview on their mobility and a comprehension of their behavior and preferences. Most information related to tourism services and tourists are collected and stored through web platforms. In fact, self- drive tourists access touristic information available on the web to plan for their trips. Accordingly, tourism professionals track their requirements in touristic information and then their mobility. Yet, since touristic in- formation is managed at a territorial level, tracking tourists’ movement by tourism professionals, out of their territory, is not a straightforward task. Accordingly, the latters do not have a complete overview of tourists movements. Throughout this paper authors will start by discussing mo- bility data capture through the web and the related challenges. Then, they’ll introduce an integrated mobility data model for tracking tourists.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tracking tourists activities at different levels of their jour- neys provides an overview on their mobility and a comprehension of their behavior and preferences. Most information related to tourism services and tourists are collected and stored through web platforms. In fact, self- drive tourists access touristic information available on the web to plan for their trips. Accordingly, tourism professionals track their requirements in touristic information and then their mobility. Yet, since touristic in- formation is managed at a territorial level, tracking tourists’ movement by tourism professionals, out of their territory, is not a straightforward task. Accordingly, the latters do not have a complete overview of tourists movements. Throughout this paper authors will start by discussing mo- bility data capture through the web and the related challenges. Then, they’ll introduce an integrated mobility data model for tracking tourists.
|
Issam Ghabri; Ladjel Bellatreche; Sadok Ben Yahia Selection of a Green Logical Data Warehouse Schema by Anti-monotonicity
Constraint Inproceedings Chatzigeorgiou, Alexander; Dondi, Riccardo; Herodotou, Herodotos; Kapoutsis, Christos A; Manolopoulos, Yannis; Papadopoulos, George A; Sikora, Florian (Ed.): SOFSEM 2020: Theory and Practice of Computer Science - 46th International
Conference on Current Trends in Theory and Practice of Informatics,
SOFSEM 2020, Limassol, Cyprus, January 20-24, 2020, Proceedings, p. 350–361, Springer, 2020. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/sofsem/GhabriBY20,
title = {Selection of a Green Logical Data Warehouse Schema by Anti-monotonicity
Constraint},
author = {Issam Ghabri and Ladjel Bellatreche and Sadok Ben Yahia},
editor = {Alexander Chatzigeorgiou and Riccardo Dondi and Herodotos Herodotou and Christos A Kapoutsis and Yannis Manolopoulos and George A Papadopoulos and Florian Sikora},
url = {https://doi.org/10.1007/978-3-030-38919-2_29},
doi = {10.1007/978-3-030-38919-2_29},
year = {2020},
date = {2020-01-01},
booktitle = {SOFSEM 2020: Theory and Practice of Computer Science - 46th International
Conference on Current Trends in Theory and Practice of Informatics,
SOFSEM 2020, Limassol, Cyprus, January 20-24, 2020, Proceedings},
volume = {12011},
pages = {350--361},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Salma El Hajjami; Jamal Malki; Alain Bouju; Mohammed Berrada A Machine Learning based Approach to Reduce Behavioral Noise Problem in an Imbalanced Data: Application to a fraud detection Inproceedings 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), p. 11-20, 2020. Résumé | Liens | BibTeX | Étiquettes: @inproceedings{9264114,
title = {A Machine Learning based Approach to Reduce Behavioral Noise Problem in an Imbalanced Data: Application to a fraud detection},
author = {Salma El Hajjami and Jamal Malki and Alain Bouju and Mohammed Berrada},
doi = {10.1109/IDSTA50958.2020.9264114},
year = {2020},
date = {2020-01-01},
booktitle = {2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)},
pages = {11-20},
abstract = {The question of class imbalance has become more pronounced with the application of learning algorithms in real applications. It has received significant attention in the machine learning and data mining community. This problem is present in fraud detection, medical diagnostics, and a number of other areas where training data contains significantly more representatives of one class (called the majority class) than the other class (called the minority class). Machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class, which is the most interesting from a learning point of view and also involves a high cost when it is not well classified. However, the imbalance ratio is not the only cause of poor performance when learning from imbalanced data. Another critical factor that accompanies imbalanced data in the real world is the presence of a number of instances of the two classes being overlapped in feature space. This problem is commonly referred to as class overlap and we have called it “behavioral noise”. In this paper, we propose One Side Behavioral Noise Reduction (OSBNR) approach to deal with the problem of class imbalance in the presence of a behavioral noise level. OSBNR is based on two stages. Firstly, a clustering is applied to groups similar instances of the minority class in multiple behavior clusters. Secondly, we select and eliminate instances of the majority class, considered as behavioral noise, which overlap with the behavior clusters of the minority class. The results of experiments conducted on a representative public dataset confirm that the proposed approach is effective for class imbalance problem in the presence of behavioral noise.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The question of class imbalance has become more pronounced with the application of learning algorithms in real applications. It has received significant attention in the machine learning and data mining community. This problem is present in fraud detection, medical diagnostics, and a number of other areas where training data contains significantly more representatives of one class (called the majority class) than the other class (called the minority class). Machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class, which is the most interesting from a learning point of view and also involves a high cost when it is not well classified. However, the imbalance ratio is not the only cause of poor performance when learning from imbalanced data. Another critical factor that accompanies imbalanced data in the real world is the presence of a number of instances of the two classes being overlapped in feature space. This problem is commonly referred to as class overlap and we have called it “behavioral noise”. In this paper, we propose One Side Behavioral Noise Reduction (OSBNR) approach to deal with the problem of class imbalance in the presence of a behavioral noise level. OSBNR is based on two stages. Firstly, a clustering is applied to groups similar instances of the minority class in multiple behavior clusters. Secondly, we select and eliminate instances of the majority class, considered as behavioral noise, which overlap with the behavior clusters of the minority class. The results of experiments conducted on a representative public dataset confirm that the proposed approach is effective for class imbalance problem in the presence of behavioral noise. |
S E Hajjami; J Malki; A Bouju; M Berrada A Machine Learning based Approach to Reduce Behavioral Noise Problem in an Imbalanced Data: Application to a fraud detection Inproceedings 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), p. 11-20, 2020. Liens | BibTeX | Étiquettes: @inproceedings{9264114b,
title = {A Machine Learning based Approach to Reduce Behavioral Noise Problem in an Imbalanced Data: Application to a fraud detection},
author = {S E Hajjami and J Malki and A Bouju and M Berrada},
doi = {10.1109/IDSTA50958.2020.9264114},
year = {2020},
date = {2020-01-01},
booktitle = {2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)},
pages = {11-20},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2019
|
Mahfoud Djedaini; Jamal Malki; Alain Bouju Architectures Big Data basées sur l'Open-Sources pour le Data Analytics Technical Report Lab. L3i et aYaline - Projet FEDER PLAIBDE 2019. Résumé | Liens | BibTeX | Étiquettes: @techreport{mahfoudDjedaini2019-1,
title = {Architectures Big Data basées sur l'Open-Sources pour le Data Analytics},
author = {Mahfoud Djedaini and Jamal Malki and Alain Bouju},
url = {https://plaibde.ayaline.com/wp-content/uploads/2020/08/PLAIBDE_L3i_Impact_du_Big_Data_sur_la_BI_Mahfoud_version_rapport_Janv-2019.pdf},
year = {2019},
date = {2019-01-01},
institution = {Lab. L3i et aYaline - Projet FEDER PLAIBDE},
abstract = {L'Informatique Décisionnelle (ou Business Intelligence BI) a toujours été au coeur de l'entreprise, et ce depuis les débuts de l'informatique. L'Informatique Décisionnelle peut être décrite comme un processus axé sur la technologie qui permet d’analyser les données et de présenter des informations exploitables pour aider les cadres, les responsables et les autres utilisateurs finaux à prendre des décisions d’affaires éclairées. La BI est faite de processus standardisés ETL, permettant de passer des données brutes à un modèle centralisé, uniformisé, et plus facilement exploitable par les analystes. Cependant, la BI a été impactée aussi par l'arrivée des challenges du Big Data, avec des données toujours plus volumineuses, variées et véloces. Bien que la BI traditionnelle soit encore bien ancrée, notamment dû à des questions de sécurité et de robustesse, les lignes semblent se déplacer aujourd'hui pour tirer parti des outils Big Data au bénéfice de la BI. Dans ce papier, nous étudions l'impact du Big Data sur la BI, et nous présentons différents types de solutions permettant d'allier la puissance du Big Data aux contraintes de la BI. Enfin, nous proposerons un exemple d'architecture complète, permettant d'effectuer de la BI temps réel en utilisant exclusivement des outils Big Data open source.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
L'Informatique Décisionnelle (ou Business Intelligence BI) a toujours été au coeur de l'entreprise, et ce depuis les débuts de l'informatique. L'Informatique Décisionnelle peut être décrite comme un processus axé sur la technologie qui permet d’analyser les données et de présenter des informations exploitables pour aider les cadres, les responsables et les autres utilisateurs finaux à prendre des décisions d’affaires éclairées. La BI est faite de processus standardisés ETL, permettant de passer des données brutes à un modèle centralisé, uniformisé, et plus facilement exploitable par les analystes. Cependant, la BI a été impactée aussi par l'arrivée des challenges du Big Data, avec des données toujours plus volumineuses, variées et véloces. Bien que la BI traditionnelle soit encore bien ancrée, notamment dû à des questions de sécurité et de robustesse, les lignes semblent se déplacer aujourd'hui pour tirer parti des outils Big Data au bénéfice de la BI. Dans ce papier, nous étudions l'impact du Big Data sur la BI, et nous présentons différents types de solutions permettant d'allier la puissance du Big Data aux contraintes de la BI. Enfin, nous proposerons un exemple d'architecture complète, permettant d'effectuer de la BI temps réel en utilisant exclusivement des outils Big Data open source. |
Ladjel Bellatreche; Sharma Chakravarthy A special issue in extending data warehouses to big data analytics Article de journal Distributed Parallel Databases, 37 (3), p. 323–327, 2019. Liens | BibTeX | Étiquettes: @article{DBLP:journals/dpd/BellatrecheC19,
title = {A special issue in extending data warehouses to big data analytics},
author = {Ladjel Bellatreche and Sharma Chakravarthy},
url = {https://doi.org/10.1007/s10619-019-07262-1},
doi = {10.1007/s10619-019-07262-1},
year = {2019},
date = {2019-01-01},
journal = {Distributed Parallel Databases},
volume = {37},
number = {3},
pages = {323--327},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Selma Khouri; Nabila Berkani; Ladjel Bellatreche; Dihia Lanasri Data Cube Is Dead, Long Life to Data Cube in the Age of Web Data Inproceedings Madria, Sanjay; -, Philippe Fournier; Chaudhary, Sanjay; Reddy, Krishna P (Ed.): Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad,
India, December 17-20, 2019, Proceedings, p. 44–64, Springer, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/bigda/KhouriBBL19,
title = {Data Cube Is Dead, Long Life to Data Cube in the Age of Web Data},
author = {Selma Khouri and Nabila Berkani and Ladjel Bellatreche and Dihia Lanasri},
editor = {Sanjay Madria and Philippe Fournier - and Sanjay Chaudhary and Krishna P Reddy},
url = {https://doi.org/10.1007/978-3-030-37188-3_4},
doi = {10.1007/978-3-030-37188-3_4},
year = {2019},
date = {2019-01-01},
booktitle = {Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad,
India, December 17-20, 2019, Proceedings},
volume = {11932},
pages = {44--64},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jorge Galicia; Amin Mesmoudi; Ladjel Bellatreche RDFPartSuite: Bridging Physical and Logical RDF Partitioning Inproceedings Ordonez, Carlos; -, Il; -, Gabriele Anderst; Tjoa, Min A; Khalil, Ismail (Ed.): Big Data Analytics and Knowledge Discovery - 21st International Conference,
DaWaK 2019, Linz, Austria, August 26-29, 2019, Proceedings, p. 136–150, Springer, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/dawak/GaliciaMB19,
title = {RDFPartSuite: Bridging Physical and Logical RDF Partitioning},
author = {Jorge Galicia and Amin Mesmoudi and Ladjel Bellatreche},
editor = {Carlos Ordonez and Il - and Gabriele Anderst - and Min A Tjoa and Ismail Khalil},
url = {https://doi.org/10.1007/978-3-030-27520-4_10},
doi = {10.1007/978-3-030-27520-4_10},
year = {2019},
date = {2019-01-01},
booktitle = {Big Data Analytics and Knowledge Discovery - 21st International Conference,
DaWaK 2019, Linz, Austria, August 26-29, 2019, Proceedings},
volume = {11708},
pages = {136--150},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jorge Galicia; Amin Mesmoudi; Ladjel Bellatreche; Carlos Ordonez Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis Article de journal DEXA (2), p. 174–183, 2019. BibTeX | Étiquettes: @article{galicia2019reverse,
title = {Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis},
author = {Jorge Galicia and Amin Mesmoudi and Ladjel Bellatreche and Carlos Ordonez},
year = {2019},
date = {2019-01-01},
journal = {DEXA (2)},
pages = {174--183},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Selma Khouri; Dihia Lanasri; Roaya Saidoune; Kamila Boudoukha; Ladjel Bellatreche LogLInc: LoG Queries of Linked Open Data Investigator for Cube Design Inproceedings Hartmann, Sven; ü, Josef K; Chakravarthy, Sharma; -, Gabriele Anderst; Tjoa, Min A; Khalil, Ismail (Ed.): Database and Expert Systems Applications - 30th International Conference,
DEXA 2019, Linz, Austria, August 26-29, 2019, Proceedings, Part
I, p. 352–367, Springer, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/dexa/KhouriLSBB19,
title = {LogLInc: LoG Queries of Linked Open Data Investigator for Cube Design},
author = {Selma Khouri and Dihia Lanasri and Roaya Saidoune and Kamila Boudoukha and Ladjel Bellatreche},
editor = {Sven Hartmann and Josef K ü and Sharma Chakravarthy and Gabriele Anderst - and Min A Tjoa and Ismail Khalil},
url = {https://doi.org/10.1007/978-3-030-27615-7_27},
doi = {10.1007/978-3-030-27615-7_27},
year = {2019},
date = {2019-01-01},
booktitle = {Database and Expert Systems Applications - 30th International Conference,
DEXA 2019, Linz, Austria, August 26-29, 2019, Proceedings, Part
I},
volume = {11706},
pages = {352--367},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Carlos Ordonez; Ladjel Bellatreche Enhancing ER Diagrams to View Data Transformations Computed with
Queries Inproceedings -, Il; Romero, Oscar; Wrembel, Robert (Ed.): Proceedings of the 21st International Workshop on Design, Optimization,
Languages and Analytical Processing of Big Data, co-located with EDBT/ICDT Joint Conference, DOLAP@EDBT/ICDT 2019, Lisbon, Portugal, March 26,
2019, CEUR-WS.org, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/dolap/0001B19,
title = {Enhancing ER Diagrams to View Data Transformations Computed with
Queries},
author = {Carlos Ordonez and Ladjel Bellatreche},
editor = {Il - and Oscar Romero and Robert Wrembel},
url = {http://ceur-ws.org/Vol-2324/Paper27-COrdonez.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 21st International Workshop on Design, Optimization,
Languages and Analytical Processing of Big Data, co-located with EDBT/ICDT Joint Conference, DOLAP@EDBT/ICDT 2019, Lisbon, Portugal, March 26,
2019},
volume = {2324},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Nabila Berkani; Ladjel Bellatreche; Selma Khouri; Carlos Ordonez Value-driven Approach for Designing Extended Data Warehouses Inproceedings -, Il; Romero, Oscar; Wrembel, Robert (Ed.): Proceedings of the 21st International Workshop on Design, Optimization,
Languages and Analytical Processing of Big Data, co-located with EDBT/ICDT Joint Conference, DOLAP@EDBT/ICDT 2019, Lisbon, Portugal, March 26,
2019, CEUR-WS.org, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/dolap/BerkaniBK019,
title = {Value-driven Approach for Designing Extended Data Warehouses},
author = {Nabila Berkani and Ladjel Bellatreche and Selma Khouri and Carlos Ordonez},
editor = {Il - and Oscar Romero and Robert Wrembel},
url = {http://ceur-ws.org/Vol-2324/Paper25-NBerkani.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 21st International Workshop on Design, Optimization,
Languages and Analytical Processing of Big Data, co-located with EDBT/ICDT Joint Conference, DOLAP@EDBT/ICDT 2019, Lisbon, Portugal, March 26,
2019},
volume = {2324},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Selma Khouri; Ladjel Bellatreche; Abdessamed R é; Yasmine Aouimer Intégrer les LOD dans un cube de données :
Transformons une action technique en valeur organisationnelle Inproceedings Lemire, Daniel; Sautot, Lucile (Ed.): Business Intelligence & Big Data, 15ème Edition de la
conférence EDA, Montpellier, France, 3-4 octobre 2019, p. 61–76, Éditions RNTI, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/eda/KhouriBGA19,
title = {Intégrer les LOD dans un cube de données :
Transformons une action technique en valeur organisationnelle},
author = {Selma Khouri and Ladjel Bellatreche and Abdessamed R é and Yasmine Aouimer},
editor = {Daniel Lemire and Lucile Sautot},
url = {http://editions-rnti.fr/?inprocid=1002551},
year = {2019},
date = {2019-01-01},
booktitle = {Business Intelligence & Big Data, 15ème Edition de la
conférence EDA, Montpellier, France, 3-4 octobre 2019},
volume = {B-15},
pages = {61--76},
publisher = {Éditions RNTI},
series = {RNTI},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Soumia Benkrid; Ladjel Bellatreche Vers une Conception des Entrepôts de Données Parallèles
Autonomes Inproceedings Lemire, Daniel; Sautot, Lucile (Ed.): Business Intelligence & Big Data, 15ème Edition de la
conférence EDA, Montpellier, France, 3-4 octobre 2019, p. 109–124, Éditions RNTI, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/eda/BenkridB19,
title = {Vers une Conception des Entrepôts de Données Parallèles
Autonomes},
author = {Soumia Benkrid and Ladjel Bellatreche},
editor = {Daniel Lemire and Lucile Sautot},
url = {http://editions-rnti.fr/?inprocid=1002548},
year = {2019},
date = {2019-01-01},
booktitle = {Business Intelligence & Big Data, 15ème Edition de la
conférence EDA, Montpellier, France, 3-4 octobre 2019},
volume = {B-15},
pages = {109--124},
publisher = {Éditions RNTI},
series = {RNTI},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Dihia Lanasri; Carlos Ordonez; Ladjel Bellatreche; Selma Khouri ER4ML: An ER Modeling Tool to Represent Data Transformations in
Data Science Inproceedings é, Jos; Guizzardi, Renata S S; Claro, Daniela Barreiro (Ed.): Proceedings of the ER Forum and Poster & Demos Session 2019
on Publishing Papers with CEUR-WS co-located with 38th International
Conference on Conceptual Modeling (ER 2019), Salvador, Brazil, November
4, 2019, p. 123–127, CEUR-WS.org, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/er/Lanasri0BK19,
title = {ER4ML: An ER Modeling Tool to Represent Data Transformations in
Data Science},
author = {Dihia Lanasri and Carlos Ordonez and Ladjel Bellatreche and Selma Khouri},
editor = {Jos é and Renata S S Guizzardi and Daniela Barreiro Claro},
url = {http://ceur-ws.org/Vol-2469/ERDemo04.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the ER Forum and Poster & Demos Session 2019
on Publishing Papers with CEUR-WS co-located with 38th International
Conference on Conceptual Modeling (ER 2019), Salvador, Brazil, November
4, 2019},
volume = {2469},
pages = {123--127},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Dihia Lanasri; Selma Khouri; Roaya Saidoune; Kamila Boudoukha; Ladjel Bellatreche Crumbs4Cube: Turning Breadcrumbs into Smart Enriched Data Cubes Inproceedings é, Jos; Guizzardi, Renata S S; Claro, Daniela Barreiro (Ed.): Proceedings of the ER Forum and Poster & Demos Session 2019
on Publishing Papers with CEUR-WS co-located with 38th International
Conference on Conceptual Modeling (ER 2019), Salvador, Brazil, November
4, 2019, p. 128–132, CEUR-WS.org, 2019. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/er/LanasriKSBB19,
title = {Crumbs4Cube: Turning Breadcrumbs into Smart Enriched Data Cubes},
author = {Dihia Lanasri and Selma Khouri and Roaya Saidoune and Kamila Boudoukha and Ladjel Bellatreche},
editor = {Jos é and Renata S S Guizzardi and Daniela Barreiro Claro},
url = {http://ceur-ws.org/Vol-2469/ERDemo05.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the ER Forum and Poster & Demos Session 2019
on Publishing Papers with CEUR-WS co-located with 38th International
Conference on Conceptual Modeling (ER 2019), Salvador, Brazil, November
4, 2019},
volume = {2469},
pages = {128--132},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2018
|
Mahfoud Djedaini; Jamal Malki; Alain Bouju Architecture Big Data open source pour l'Informatique Décisionnelle Technical Report Lab. L3i et aYaline - Projet FEDER PLAIBDE 2018. Résumé | Liens | BibTeX | Étiquettes: @techreport{mahfoudDjedaini2018-1,
title = {Architecture Big Data open source pour l'Informatique Décisionnelle},
author = {Mahfoud Djedaini and Jamal Malki and Alain Bouju},
url = {https://plaibde.ayaline.com/wp-content/uploads/2020/08/PLAIBDE_L3i_Mahfoud_DJEDAINI_technical_report1_2018.pdf},
year = {2018},
date = {2018-07-09},
institution = {Lab. L3i et aYaline - Projet FEDER PLAIBDE},
abstract = {Depuis quelques années, avec l'utilisation de plus en plus massive des données provenant des réseaux sociaux et l'open data, aYaline se retrouve confrontée à des problématiques de volumétrie des données. En effet, les données deviennent tellement volumineuses pour certains clients, que les outils traditionnels utilisés pour exploiter ces données ne sont plus tout à fait adaptés. Dans certains cas, les outils ne sont tout simplement pas utilisables. Dans d'autre cas, les outils fonctionnent mais sont tellement lents qu'au final, leur utilisation n'est pas agréable ni même profitable.Afin d'illustrer ce propos, nous pouvons citer le cas des clients d'aYaline pour lesquels l'entreprise fournit des solutions décisionnelles.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Depuis quelques années, avec l'utilisation de plus en plus massive des données provenant des réseaux sociaux et l'open data, aYaline se retrouve confrontée à des problématiques de volumétrie des données. En effet, les données deviennent tellement volumineuses pour certains clients, que les outils traditionnels utilisés pour exploiter ces données ne sont plus tout à fait adaptés. Dans certains cas, les outils ne sont tout simplement pas utilisables. Dans d'autre cas, les outils fonctionnent mais sont tellement lents qu'au final, leur utilisation n'est pas agréable ni même profitable.Afin d'illustrer ce propos, nous pouvons citer le cas des clients d'aYaline pour lesquels l'entreprise fournit des solutions décisionnelles. |
Sirine Knaz; Jamal Malki Architecture dórchestration et de communication des microservices à base de conteneurs pour le développement dápplications cloud-natives Masters Thesis Lab. L3i et aYaline - Projet FEDER PLAIBDE, 2018. Résumé | Liens | BibTeX | Étiquettes: @mastersthesis{sirineKnaz2018,
title = {Architecture dórchestration et de communication des microservices à base de conteneurs pour le développement dápplications cloud-natives},
author = {Sirine Knaz and Jamal Malki},
url = {https://plaibde.ayaline.com/wp-content/uploads/2020/08/PLAIBDE_L3i_Rapport_Stage_M2_Sirine_knaz_2018.pdf},
year = {2018},
date = {2018-06-25},
institution = {Lab. L3i et aYaline - Projet FEDER PLAIBDE},
school = {Lab. L3i et aYaline - Projet FEDER PLAIBDE},
abstract = {Le déploiement des applications métiers ou entreprises basé sur la technologie des conteneurs est entrain de changer les plateformes cloud. Ce changement touche à la fois la façon de conception et de gestion de ces plateformes. Pour comprendre cet impact, on considère un conteneur comme un environnement logiciel capable de gérer entièrement une application ou un service et possédant sa propre configuration d’exécution. En comparaison avec un déploiement dans une plateforme cloud traditionnelle, gérée par exemple sous OpenStack, donc basée sur des machines virtuelles, la technologie des conteneurs a au moins le mérite d’avoir résolu les problèmes liés à la gestion automatique des approvisionnements nécessaires pour la régulation des performances. La nouvelle technologie de déploiement basée sur les conteneurs a aussi contribué à la résolution des verrous liés à la portabilité des applications et des services. Cependant, cette technologie nouvellement naissante doit affronter les problèmes de maturité qui offrent alors des sujets de recherche et de développement importants. Parmi ces challenges, on s’intéresse au problème d’orchestration des conteneurs dans un contexte microservices. On aborde aussi les modèles de passerelle de communication des conteneurs.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Le déploiement des applications métiers ou entreprises basé sur la technologie des conteneurs est entrain de changer les plateformes cloud. Ce changement touche à la fois la façon de conception et de gestion de ces plateformes. Pour comprendre cet impact, on considère un conteneur comme un environnement logiciel capable de gérer entièrement une application ou un service et possédant sa propre configuration d’exécution. En comparaison avec un déploiement dans une plateforme cloud traditionnelle, gérée par exemple sous OpenStack, donc basée sur des machines virtuelles, la technologie des conteneurs a au moins le mérite d’avoir résolu les problèmes liés à la gestion automatique des approvisionnements nécessaires pour la régulation des performances. La nouvelle technologie de déploiement basée sur les conteneurs a aussi contribué à la résolution des verrous liés à la portabilité des applications et des services. Cependant, cette technologie nouvellement naissante doit affronter les problèmes de maturité qui offrent alors des sujets de recherche et de développement importants. Parmi ces challenges, on s’intéresse au problème d’orchestration des conteneurs dans un contexte microservices. On aborde aussi les modèles de passerelle de communication des conteneurs. |
Anas El Majdoubi; Jamal Malki Mise en place d’un OLAP entreprise basé sur l’écosystème Hadoop Masters Thesis Lab. L3i et aYaline - Projet FEDER PLAIBDE, 2018. Résumé | Liens | BibTeX | Étiquettes: @mastersthesis{anasElMajdoubi2018,
title = {Mise en place d’un OLAP entreprise basé sur l’écosystème Hadoop},
author = {Anas El Majdoubi and Jamal Malki},
url = {https://plaibde.ayaline.com/wp-content/uploads/2020/08/PLAIBDE_L3i_Rapport_Stage_Anass_El_Majdoubi_2018.pdf},
year = {2018},
date = {2018-01-01},
institution = {Lab. L3i et aYaline},
school = {Lab. L3i et aYaline - Projet FEDER PLAIBDE},
abstract = {Dans le cadre de mon projet de fin d’études à la faculté des sciences Ain-Chock Casablanca et afin d’obtenir le diplôme de Master « Big Data and Cloud Computing » , j’ai effectué mon stage au sein de Laboratoire Informatique, Image et Interaction - (L3i) de l'université de La Rochelle en collaboration avec la société aYaline. Les projets analytiques actuels de l'entreprise aYaline sont mis en difficultés devant les grandes masses de données avec un temps de chargement et de réponse très longs ce qui ne satisfait pas les besoins des utilisateurs finaux. Le travail réalisé a comme objectif de mettre en place une solution Big data pour gérer les données massives et intégrer les projets OLAP de l'entreprise aYaline dans l'écosystème Hadoop. Concernant les données utilisées, on a travaillé avec des Data Warehouse du projet BI-Charente-Maritime-Tourisme qui porte sur un écosystème analytique complet et indépendant pour les données provenant de l'écosystème e-Tourisme du département Charente-Maritime (Région Nouvelle Aquitaine, France). L’élaboration de ce projet m'a permis de minimiser le temps de chargement des données, ainsi que le temps de l’exécution.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Dans le cadre de mon projet de fin d’études à la faculté des sciences Ain-Chock Casablanca et afin d’obtenir le diplôme de Master « Big Data and Cloud Computing » , j’ai effectué mon stage au sein de Laboratoire Informatique, Image et Interaction - (L3i) de l'université de La Rochelle en collaboration avec la société aYaline. Les projets analytiques actuels de l'entreprise aYaline sont mis en difficultés devant les grandes masses de données avec un temps de chargement et de réponse très longs ce qui ne satisfait pas les besoins des utilisateurs finaux. Le travail réalisé a comme objectif de mettre en place une solution Big data pour gérer les données massives et intégrer les projets OLAP de l'entreprise aYaline dans l'écosystème Hadoop. Concernant les données utilisées, on a travaillé avec des Data Warehouse du projet BI-Charente-Maritime-Tourisme qui porte sur un écosystème analytique complet et indépendant pour les données provenant de l'écosystème e-Tourisme du département Charente-Maritime (Région Nouvelle Aquitaine, France). L’élaboration de ce projet m'a permis de minimiser le temps de chargement des données, ainsi que le temps de l’exécution. |
Ladjel Bellatreche; Carson Leung; Yinglong Xia; Didier El Baz Advances in cloud and big data computing - Foreward to the special issue Article de journal Concurrency and Computation: Practice and Experience, 31 (2), p. e5053, 2018. Liens | BibTeX | Étiquettes: @article{bellatreche:hal-02091765,
title = {Advances in cloud and big data computing - Foreward to the special issue},
author = {Ladjel Bellatreche and Carson Leung and Yinglong Xia and Didier El Baz},
url = {https://hal.laas.fr/hal-02091765},
doi = {10.1002/cpe.5053},
year = {2018},
date = {2018-01-01},
journal = {Concurrency and Computation: Practice and Experience},
volume = {31},
number = {2},
pages = {e5053},
publisher = {Wiley},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Selma Khouri; Ladjel Bellatreche LOD for Data Warehouses: Managing the Ecosystem Co-Evolution Article de journal Inf., 9 (7), p. 174, 2018. Liens | BibTeX | Étiquettes: @article{DBLP:journals/information/KhouriB18,
title = {LOD for Data Warehouses: Managing the Ecosystem Co-Evolution},
author = {Selma Khouri and Ladjel Bellatreche},
url = {https://doi.org/10.3390/info9070174},
doi = {10.3390/info9070174},
year = {2018},
date = {2018-01-01},
journal = {Inf.},
volume = {9},
number = {7},
pages = {174},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Nabila Berkani; Ladjel Bellatreche; Laurent Guittet ETL Processes in the Era of Variety Article de journal Trans. Large Scale Data Knowl. Centered Syst., 39 , p. 98–129, 2018. Liens | BibTeX | Étiquettes: @article{DBLP:journals/tlsdkcs/BerkaniBG18,
title = {ETL Processes in the Era of Variety},
author = {Nabila Berkani and Ladjel Bellatreche and Laurent Guittet},
url = {https://doi.org/10.1007/978-3-662-58415-6_4},
doi = {10.1007/978-3-662-58415-6_4},
year = {2018},
date = {2018-01-01},
journal = {Trans. Large Scale Data Knowl. Centered Syst.},
volume = {39},
pages = {98--129},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Carlos Ordonez; Ladjel Bellatreche A Survey on Parallel Database Systems from a Storage Perspective:
Rows Versus Columns Inproceedings Elloumi, Mourad; Granitzer, Michael; Hameurlain, Abdelkader; Seifert, Christin; Stein, Benno; Tjoa, Min A; Wagner, Roland R (Ed.): Database and Expert Systems Applications - DEXA 2018 International
Workshops, BDMICS, BIOKDD, and TIR, Regensburg, Germany, September
3-6, 2018, Proceedings, p. 5–20, Springer, 2018. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/dexaw/0001B18,
title = {A Survey on Parallel Database Systems from a Storage Perspective:
Rows Versus Columns},
author = {Carlos Ordonez and Ladjel Bellatreche},
editor = {Mourad Elloumi and Michael Granitzer and Abdelkader Hameurlain and Christin Seifert and Benno Stein and Min A Tjoa and Roland R Wagner},
url = {https://doi.org/10.1007/978-3-319-99133-7_1},
doi = {10.1007/978-3-319-99133-7_1},
year = {2018},
date = {2018-01-01},
booktitle = {Database and Expert Systems Applications - DEXA 2018 International
Workshops, BDMICS, BIOKDD, and TIR, Regensburg, Germany, September
3-6, 2018, Proceedings},
volume = {903},
pages = {5--20},
publisher = {Springer},
series = {Communications in Computer and Information Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Nabila Berkani; Selma Khouri; Ladjel Bellatreche Linked Open Data pour les Entrepôts de Données: Opportunité
et Défis Inproceedings Badir, Hassan; Bentayeb, Fadila; ï, Omar Boussa (Ed.): Business Intelligence & Big Data, 14ème Edition de la
conference EDA, Tanger, Maroc, 4-6 octobre 2018, p. 303–312, Éditions RNTI, 2018. Liens | BibTeX | Étiquettes: @inproceedings{DBLP:conf/eda/BerkaniKB18,
title = {Linked Open Data pour les Entrepôts de Données: Opportunité
et Défis},
author = {Nabila Berkani and Selma Khouri and Ladjel Bellatreche},
editor = {Hassan Badir and Fadila Bentayeb and Omar Boussa ï},
url = {http://editions-rnti.fr/?inprocid=1002453},
year = {2018},
date = {2018-01-01},
booktitle = {Business Intelligence & Big Data, 14ème Edition de la
conference EDA, Tanger, Maroc, 4-6 octobre 2018},
volume = {B-14},
pages = {303--312},
publisher = {Éditions RNTI},
series = {RNTI},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
