Imen Ouled Dlala

@inproceedings{martin-prin_1890,

title = {A Distributed SAT-based Framework for Closed Frequent Itemset Mining},

author = {Julien Martin-Prin and Imen Ouled Dlala and Nicolas Travers and Said Jabbour},

url = {https://adma2022.uqcloud.net/important_date.html},

year  = {2022},

date = {2022-11-01},

booktitle = {International Conference on Advanced Data Mining and Applications},

publisher = {Springer},

address = {Brisbane, Australia},

abstract = {Frequent Itemset Mining is an essential part of data mining. SAT-based approaches that extract frequent itemsets in big data encounter significant challenges when computing power and storage capacity are limited. This paper proposes an efficient distributed SAT-based framework for the Closed Frequent Itemset Mining problem (CFIM) which minimizes communications throughout the distributed architecture and reduces bottlenecks due to shared memory. Moreover, it enhances scalability and fault tolerance. This approach makes use of a Computation-Distributed Paradigm to enumerate efficiently the set of all closed itemsets, by reducing the processing time. To the best of our knowledge, this paper presents the first attempt towards a distributed SAT-based approach for CFIM. An extensive empirical evaluation on various real-word datasets shows the efficiency of the approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hidouri_1766,

title = {On Minimal and Maximal High Utility Itemsets Mining using Propositional Satisfiability},

author = {amel Hidouri and Said Jabbour and Imen Ouled Dlala and Badran Raddaoui},

url = {https://doi.org/10.1109/BigData52589.2021.9671422},

issn = {978-1-6654-4599-3},

year  = {2021},

date = {2021-12-01},

booktitle = {IEEE International Conference on Big Data},

address = {Orlando, FL, USA},

abstract = {Mining high utility itemsets is a keystone data mining method for discovering useful itemsets yielding high utility values. Minimal and maximal high utility itemsets are

two examples of compact representations used to reduce the

output size due to not only the large but incomprehensible

number of patterns. Despite the variety of proposed solutions to

tackle this problem, it still provides methods with less flexibility.

In this paper, we present a novel method for translating the

problem of mining minimal and maximal high utility itemsets

into a propositional satisfiability problem. In order to find these

patterns of interest, we demonstrate that the task of minimal and maximal patterns is equivalent to enumerating the X-

minimal models of a given CNF formula. Then, to improve the

scalability issue, we harness a decomposition paradigm that splits

the transaction database into smaller and independent sub-bases, allowing an efficient enumeration of minimal and maximal high

utility itemsets. Finally, through extensive evaluation studies on

various real-world datasets, we demonstrate that our approach is

very competitive w.r.t. to the state-of-the-art specialized solutions.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ouled_dlala_1756,

title = {A Parallel SAT-Based Framework for Closed Frequent Itemsets Mining},

author = {Imen Ouled Dlala and Said Jabbour and Badran Raddaoui and Lakhdar Sais},

url = {https://doi.org/10.1007/978-3-319-98334-9_37},

issn = {978-3-030-78229-0},

year  = {2018},

date = {2018-08-01},

booktitle = {Principles and Practice of Constraint Programming -24th International Conference},

address = {Lille, France},

abstract = {Constraint programming (CP) and propositional satisfiabil- ity (SAT) based framework for modeling and solving pattern mining tasks has gained a considerable audience in recent years. However, this nice declarative and generic framework encounters a scaling problem. The huge size of constraints networks/propositional formulas encoding large datasets is identified as the main bottleneck of most existing ap- proaches. In this paper, we propose a parallel SAT based framework for itemset mining problem to push forward the solving efficiency. The pro- posed approach is based on a divide-and-conquer paradigm, where the transaction database is partitioned using item-based guiding paths. Such decomposition allows us to derive smaller and independent Boolean for- mulas that can be solved in parallel. The performance and scalability of the proposed algorithm are evaluated through extensive experiments on several datasets. We demonstrate that our partition-based parallel SAT approach outperforms other CP approaches even in the sequential case, while significantly reducing the performances gap with specialized approaches},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jabbour_1757,

title = {On Maximal Frequent Itemsets Mining with Constraints},

author = {Said Jabbour and Fatima Ezzahra Mana and Imen Ouled Dlala and Badran Raddaoui and Lakhdar Sais},

url = {https://doi.org/10.1007/978-3-319-98334-9_36},

issn = {978-3-030-78229-0},

year  = {2018},

date = {2018-08-01},

booktitle = {Principles and Practice of Constraint Programming - 24th International Conference},

address = {Lille, France},

abstract = {Recently, a new declarative mining framework based on constraint programming (CP) and propositional satisfiability (SAT) has been designed to deal with several pattern mining tasks. The itemset mining problem has been modeled using constraints whose models cor- respond to the patterns to be mined. In this paper, we propose a new propositional satisfiability based approach for mining maximal frequent itemsets that extends the one proposed in [20]. We show that instead of adding constraints to the initial SAT based itemset mining encoding, the maximal itemsets can be obtained by performing clause learning during search. A major strength of our approach rises in the compactness of the proposed encoding and the efficiency of the SAT-based maximal itemsets enumeration derived using blocked clauses. Experimental results on several datasets, show the feasibility and the efficiency of our approach},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ouled_dlala_1754,

title = {A Comparative Study of SAT-Based Itemsets Mining},

author = {Imen Ouled Dlala and Said Jabbour and Lakhdar Sais and Boutheina Ben Yaghlane},

url = {https://doi.org/10.1007/978-3-319-47175-4_3},

issn = {978-3-319-47175-4},

year  = {2016},

date = {2016-12-01},

booktitle = {The Thirty-Sixth International Conference on Innovative Techniques and Applications of Artificial Intelligence},

address = {Cambridge, UK},

abstract = {Mining frequent itemsets from transactional datasets is a well known problem. Thus, various methods have been studied to deal with this issue. Recently, original proposals have emerged from the cross-fertilization between data mining and artificial intelligence. In these declarative approaches, the itemset mining problem is modeled either as a constraint network or a propositional formula whose models correspond to the patterns of interest. In this paper, we focus on the propositional satisfiability based itemset mining framework. Our main goal is to enhance the efficiency of SAT model enumeration algorithms. This issue is particularly crucial for the scalability and competitiveness of such declarative itemset mining approaches. In this context, we deeply analyse the effect of the different SAT solver components on the efficiency of the model enumeration problem. Our analysis includes the main components of modern SAT solvers such as restarts, activity based variable ordering heuristics and clauses learning mechanism. Through extensive experiments, we show that these classical components play an essential role in such procedure to improve the performance by pushing forward the efficiency of SAT solvers. More precisely, our experimental evaluation includes a comparative study in enumerating all the models corresponding to the closed frequent itemsets. Additionally, our experimental analysis is extended to include the Top-k itemset mining problem.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ouled_dlala_1753,

title = {Parallel SAT based closed frequent itemsets enumeration},

author = {Imen Ouled Dlala and Said Jabbour and Lakhdar Sais and Yakoub Salhi and Boutheina Ben Yaghlane},

url = {https://doi.org/10.1109/AICCSA.2015.7507151},

issn = {978-1-5090-0479-9},

year  = {2015},

date = {2015-11-01},

booktitle = {2015 IEEE/ACS 12th International Conference of Computer Systems and Applications},

address = {Marrakech, Morocco},

abstract = {Frequent itemset mining (FIM) is a useful task for discovering frequent co-occurring items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performances. Unfortunately, for huge dataset, scalability remains an important issue. In this work, we propose a new propositional satisfiability (SAT) parallel approach, called PSATCFIM, to deal with closed frequent itemsets mining problem. It is designed to run on multicore machines and uses a divide and conquer approach to partition the enumeration process. Such partitioning based on guiding paths eliminates computational overlap between cores. Through empirical study, we demonstrate that PSATCFIM can achieve significant performance improvements with respect to the sequential based version.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ouled_dlala_1752,

title = {Trolls Identification within an Uncertain Framework},

author = {Imen Ouled Dlala and Dorra Attiaoui and Arnaud Martin and Boutheina Ben Yaghlane},

url = {https://doi.org/10.1109/ICTAI.2014.153},

issn = {978-1-4799-6572-4},

year  = {2014},

date = {2014-11-01},

booktitle = {26th {IEEE} International Conference on Tools with Artificial Intelligence},

address = {Limassol, Cyprus},

abstract = {The web plays an important role in people's social lives since the emergence of Web 2.0. It facilitates the interaction between users, gives them the possibility to freely interact, share and collaborate through social networks, online communities forums, blogs, wikis and other online collaborative media. However, an other side of the web is negatively taken such as posting inflammatory messages. Thus, when dealing with the online communities forums, the managers seek to always enhance the performance of such platforms. In fact, to keep the serenity and prohibit the disturbance of the normal atmosphere, managers always try to novice users against these malicious persons by posting such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to reduce this phenomenon. In this context we propose a new approach for detecting malicious people also called 'Trolls' in order to allow community managers to take their ability to post online. To be more realistic, our proposal is defined within an uncertain framework. Based on the assumption consisting on the trolls' integration in the successful discussion threads, we try to detect the presence of such malicious users. Indeed, this method is based on a conflict measure of the belief function theory applied between the different messages of the thread. In order to show the feasibility and the result of our approach, we test it in different simulated data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}