@conference {Wissal2020557,
	title = {Adaptive Enterprise Architecture: Initiatives and Criteria},
	booktitle = {7th International Conference on Control, Decision and Information Technologies, CoDIT 2020},
	year = {2020},
	note = {cited By 3},
	pages = {557-562},
	abstract = {Due mainly to digital transformation, enterprises are immersed in dynamic environments. They are dealing with internal and external changes (gradual or abrupt, frequent or occasional, in the short or long term) that impact various parts of their Enterprise Architecture. To cope with the requirements of this new digital era, agility and adaptation are compulsory. Though Enterprise Architecture (EA) was designed as a catalyst for change management, traditional Enterprise Architecture approaches do not rely on well-defined concepts, lack agility and are constrained by layered architecture (business layer, functional layer, deployment layer, etc.). Thus, a new generation of EA, allowing continuous self-improvement and proactive adaptation to better match the new conditions, is required. In this paper, we explore and investigate some approaches of EA that deal with change management. Then, we, particularly, note the various definitions of the notion of adaptive EA, according to literature. Finally, we suggest some criteria, regarding the adaptation of EA, to highlight the limitations of the approaches analyzed. {\textcopyright} 2020 IEEE.},
	keywords = {Adaptive enterprise, Change management, Control engineering, Digital transformation, Dynamic environments, enterprise architecture, Functional layer, Layered architecture, Proactive adaptations},
	doi = {10.1109/CoDIT49905.2020.9263891},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098274425\&doi=10.1109\%2fCoDIT49905.2020.9263891\&partnerID=40\&md5=a1b2cd3899750d3829a4b6b9824d2970},
	author = {Wissal, D. and Karim, D. and Laila, K.}
}
@conference {Imgharene2020662,
	title = {Agility metrics model for Business IT Alignment},
	booktitle = {7th International Conference on Control, Decision and Information Technologies, CoDIT 2020},
	year = {2020},
	note = {cited By 1},
	pages = {662-667},
	abstract = {Enterprises are facing rapid and radical changes in a turbulent environment. This turbulence requires companies to be agile, i.e. to keep an overview of their environment and to be ready to react. Alignment Businesses IT is constantly being affected if levels of abstraction are affected by the change. The changes in companies must necessarily be accompanied by a (re)alignment of Business IT. In order to measure the impact of change on Business-IT alignment, this paper reviews the literature on agility metrics in different domains, by filtering them to focus on the context of Business-IT alignment, and then matching the metrics to the model that defines the impact of change on Business-IT alignment. {\textcopyright} 2020 IEEE.},
	keywords = {Alignment, Business - IT alignments, Business-IT, Control engineering, Different domains, Impact of changes, Levels of abstraction, Metrics models, Turbulent environments},
	doi = {10.1109/CoDIT49905.2020.9263839},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098240037\&doi=10.1109\%2fCoDIT49905.2020.9263839\&partnerID=40\&md5=821e8e127bf8d6c8fed3eb27359a6c73},
	author = {Imgharene, K. and Doumi, K. and Baina, S.}
}
@conference {Yousfi2020,
	title = {CSSM: A context-based semantic similarity measure},
	booktitle = {2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {Semantic similarity is very critical in applications that manipulate data across heterogeneous, autonomous and distributed data sources. It helps connect these data sources. Nevertheless, current semantic similarity approaches were proven to achieve a very moderate accuracy. Furthermore, they are merely applicable when we wish to determine the semantic similarity between words. In this paper, we present CSSM, a Context-based Semantic Similarity Measure. Experimental results on Miller Charles benchmark dataset show that CSSM performs semantic similarity comparisons properly, and obtains high accuracy. {\textcopyright} 2020 IEEE.},
	keywords = {Benchmark datasets, Computer science, Computers, Context-based semantics, Control engineering, Data-sources, Distributed data sources, Electronics engineering, Electronics industry, High-accuracy, Semantic similarity, Semantic similarity approaches, Semantics},
	doi = {10.1109/ICECOCS50124.2020.9314376},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100120972\&doi=10.1109\%2fICECOCS50124.2020.9314376\&partnerID=40\&md5=3191da8d69fa40844c69e41c268c5a17},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
