@article {Sabri2021888,
	title = {Cloud Computing in Remote Sensing: Big Data Remote Sensing Knowledge Discovery and Information Analysis},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {5},
	year = {2021},
	note = {cited By 1},
	pages = {888-895},
	abstract = {With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data. This paper proposes a feature supporting, salable, and efficient data cube for time-series analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change.The spatial-feature remote sensing data cube (SRSDC) is described in this paper. It is a data cube whose goal is to provide a spatial-feature-supported, efficient, and scalable multidimensional data analysis system to handle largescale RS data. It provides a high-level architectural overview of the SRSDC.The SRSDC offers spatial feature repositories for storing and managing vector feature data, as well as feature translation for converting spatial feature information to query operations.The paper describes the design and implementation of a feature data cube and distributed execution engine in the SRSDC. It uses the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine. Big data has become a strategic highland in the knowledge economy as a new strategic resource for humans. The core knowledge discovery methods include supervised learning methods data analysis supervised learning, unsupervised learning methods data analysis unsupervised learning, and their combinations and variants. {\textcopyright} 2021. All Rights Reserved.},
	keywords = {Big data, Cloud computing, Cloud-computing, Data cube, Data distributed, Data integration, Data mining, Engines, Execution engine, Feature data, Geometry, Methods. Data analysis, Remote sensing, Remote sensing data, Remote sensing technology, Remote-sensing, Spatial features, Supervised learning, Time series analysis, Unsupervised learning},
	doi = {10.14569/IJACSA.2021.01205104},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107802131\&doi=10.14569\%2fIJACSA.2021.01205104\&partnerID=40\&md5=4251cd5707097684639ead5c44f44bec},
	author = {Sabri, Y. and Bahja, F. and Siham, A. and Maizate, A.}
}
@conference {Choukri2021,
	title = {Context aware Hidden Markov Model for Intention process mining},
	booktitle = {5th International Conference on Intelligent Computing in Data Sciences, ICDS 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Nowadays, the omnipresence of digital devices and solutions in daily life made the digital footprints of individuals and the traces of their activities widely available. Smart devices generate a tremendous amount of data that and enables tracking their users{\textquoteright} activity. Extensive research has been conducted to produce generic process models based on the analysis of user{\textquoteright}s activity recorded during the enactment of operational processes. Unfortunately, these approaches considered only the relation between the observed activities and their sequences to infer the underlying process. Thus, ignoring the goal conditioning the user{\textquoteright}s behavior when triggering the actual process. Nevertheless, the same activity traces could serve to unhide the intention behind each process. This article will focus on presenting our approach to Contextual intention mining using Hidden Markov Model (HMM). This approach explores the close relationships between intention and context to construct the process model while ensuring consistency between observed context and actual intentions. {\textcopyright} 2021 IEEE.},
	keywords = {Behavioral research, Context, Data mining, Digital devices, Event logs, Goal mining, Goal models, Hidden Markov models, Information systems, Information use, Intention-oriented process modeling, Intentional process modeling, Machine learning, Markov modeling, Oriented process, Process Discovery, Process engineering, Process mining, Process recommendation, Process-aware information systems, Process-models, Supervised learning, Unsupervised learning, User profile},
	doi = {10.1109/ICDS53782.2021.9626765},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123483088\&doi=10.1109\%2fICDS53782.2021.9626765\&partnerID=40\&md5=d61fab7bfc96ede4658a8984da68ef3f},
	author = {Choukri, I. and Guermah, H. and Daosabah, A. and Nassar, M.}
}
@article {Talei2021,
	title = {Smart building energy inefficiencies detection through time series analysis and unsupervised machine learning},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	note = {cited By 6},
	abstract = {The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building{\textquoteright}s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6\% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Clusterings, Conditioning systems, energy efficiency, Energy management systems, Energy savings, Energy utilization, Energy-savings, Harmonic analysis, Heating ventilation and air conditioning, Houston, HVAC, Internet of things, K-means clustering, Leadership in energy and environment design building, Leadership in energy and environment designs, Learning algorithms, Office buildings, Potential energy, Time series analysis, Time-series analysis, Times series, Tropics, Unsupervised learning},
	doi = {10.3390/en14196042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115608885\&doi=10.3390\%2fen14196042\&partnerID=40\&md5=e01e465020f683b8a7ccebf61bf08077},
	author = {Talei, H. and Benhaddou, D. and Gamarra, C. and Benbrahim, H. and Essaaidi, M.}
}
@article {OUDOUAR2020102064,
	title = {A novel approach based on heuristics and a neural network to solve a capacitated location routing problem},
	journal = {Simulation Modelling Practice and Theory},
	volume = {100},
	year = {2020},
	pages = {102064},
	abstract = {In this work, we describe a method to solve the capacitated location-routing problem (CLRP) to minimize the delivery distance for a vehicle. The CLRP consists of locating depots, assigning each customer to one depot, and determining routes. The objective is to minimize the cost (distance). In the locating problem, we use a self-organizing map (SOM) to determine the depots and assign customers to depots. The SOM is an unsupervised learning method with two layers and has proven effective in several research areas, such as clustering. In the routing problem, we use the Clarke and Wright technique to determine routes. In the present work, we propose an improvement of the capacitated self-organizing map (CSOM) to optimize the location of depots and the Or-Opt algorithm to ameliorate the routes obtained by Clarke and Write (CSOM\&CW). The numerical results show that the proposed method can meet many benchmarks of small and medium instances. Computational results assess the higher performance of our approach and demonstrate its efficiency in solving large-size instances.},
	keywords = {Clustering, Location routing problem, Neural network, Optimization, Self-organizing map, Unsupervised learning},
	issn = {1569-190X},
	doi = {https://doi.org/10.1016/j.simpat.2019.102064},
	url = {https://www.sciencedirect.com/science/article/pii/S1569190X19301959},
	author = {Fadoua Oudouar and Mohamed Lazaar and Zaoui El Miloud}
}
@article {Oudouar2020,
	title = {A novel approach based on heuristics and a neural network to solve a capacitated location routing problem},
	journal = {Simulation Modelling Practice and Theory},
	volume = {100},
	year = {2020},
	note = {cited By 12},
	abstract = {In this work, we describe a method to solve the capacitated location-routing problem (CLRP) to minimize the delivery distance for a vehicle. The CLRP consists of locating depots, assigning each customer to one depot, and determining routes. The objective is to minimize the cost (distance). In the locating problem, we use a self-organizing map (SOM) to determine the depots and assign customers to depots. The SOM is an unsupervised learning method with two layers and has proven effective in several research areas, such as clustering. In the routing problem, we use the Clarke and Wright technique to determine routes. In the present work, we propose an improvement of the capacitated self-organizing map (CSOM) to optimize the location of depots and the Or-Opt algorithm to ameliorate the routes obtained by Clarke and Write (CSOM\&CW). The numerical results show that the proposed method can meet many benchmarks of small and medium instances. Computational results assess the higher performance of our approach and demonstrate its efficiency in solving large-size instances. {\textcopyright} 2019 Elsevier B.V.},
	keywords = {Capacitated location, Clustering, Computational results, Conformal mapping, Its efficiencies, Location, Location routing problem, Machine learning, Network routing, Neural networks, Numerical methods, Numerical results, Optimization, Routing problems, Self organizing maps, Unsupervised learning, Unsupervised learning method},
	doi = {10.1016/j.simpat.2019.102064},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077516665\&doi=10.1016\%2fj.simpat.2019.102064\&partnerID=40\&md5=a650efb5ef7f1f150f0e4f6f9af68e91},
	author = {Oudouar, F. and Lazaar, M. and Miloud, Z.E.}
}
