International Journal Papers

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    Machine Learning for Smart Building Applications: Review and Taxonomy
    (ACM, 2019-03) Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko
    The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The paper ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.
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    Machine Learning for Smart Building Applications: Review and Taxonomy
    (ACM, 2019-03) Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko
    The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The paper ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.
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    Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach
    (IEEE, 2017-06) Djenouri, Youcef; Habbas, Zineb; Djenouri, Djamel
    A new approach decomposes a MAXSAT instance and then applies clustering via data mining decomposition techniques, with every cluster resulting from the decomposition separately solved to construct a partial solution. All partial solutions are then merged to build the global solution.
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    Reducing thread divergence in GPU-based bees swarm optimization applied to association rule mining
    (John Wiley & Sons, Ltd., 2016) Bendjoudi, Ahcène; Djenouri, Youcef; Habbas, Zineb; Mehdi, Malika; Djenouri, Djamel
    The association rules mining (ARM) problem is one of the most important problems in the area of data mining. It aims at finding all relevant association rules from transactional databases. It is CPU time intensive and requires a huge computing power when dealing with large transactional databases. To deal with this issue, Graphics Processing Units (GPUs) are a powerful tool to speed up the search process. However, their performance is closely subject to thread/branch divergence resulting from the single instruction multiple data parallel model of GPUs. In this paper, we propose three approaches based on database reorganization, aiming to reduce thread divergence in GPU-based bees swarm optimization metaheuristic for ARM, respectively, named block-based reordering, transactions-based reordering, and transactions-based reordering with median value. Theoretical and experimental studies have been carried out using well-known large ARM instances. The experiments have been performed on an Intel Xeon 64 bit quad-core processor E5520 coupled to Nvidia Tesla C2075 448 cores. The results show that the proposed approaches minimize considerably the number of thread divergence and improve the overall execution time. Indeed, the number of thread divergence occurrences has been reduced by up to eight times making the execution much faster.
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    GPU-based Bees Swarm Optimization for Association Rules Mining
    (Springer, 2014) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    Association Rules Mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large scale data sets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as GPUs that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (SE-GPU and ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our approaches, the two algorithms have been executed to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our approaches improve the execution time up to x100 over the sequential mono-core BSO-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1 to 8 cores). The results show that they are faster than the multi-core versions what ever the number of used cores.
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    Pruning Irrelevant Association Rules Using Knowledge Mining
    (2014) Djenouri, Youcef; Derias, Habiba; Bendjoudi, Ahcène
    The efficiency of existing association rules mining algorithms afford large number of delivered rules that the user can not exploit them easily. Consequently, thinking about another mining of these generated rules becomes essential task. For this, the present paper explores metarules extraction in order to prune the irrelevant rules. It first focuses on clustering association rules for large datasets. This allows the user better organising and interpreting the rules. To more go down in our mining, different dependencies between rules of the same cluster are extracted using meta-rules algorithm. Then, pruning algorithm uses these dependencies to delete the deductive rules and keep just the representative rules for each cluster. The proposed approach is tested on different experiments including clustering, meta-rules and pruning steps. The result is very promising in terms of the number of returned rules and their quality.