Ontology-Based Education/Industry Collaboration System

Gasmi, Houssem; Bouras, Abdelaziz

doi:10.1109/access.2017.2778879

Cited by 27 publications

(9 citation statements)

References 20 publications

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“…This is why meeting the growing need of qualified specialists in BDSE has emerged as a challenge that software engineering must resolve in the near future [3]. This challenge, commonly defined as a big data skills gap, has been discussed by a number of scientific studies and industrial reports [3], [4], [8]- [11], [17], [21], [23]. Considering research based on the analysis of job ads, it is observed that a limited number of specific studies have been carried out to reveal the knowledge and skill requirements for the software engineering industry.…”

Section: A Big Data Software Engineeringmentioning

confidence: 99%

“…Given this background, BDSAs require a wide range of vocational knowledge, skills, and competencies, unlike traditional software applications [10], [11], [18], [20]. The software industry is a dynamic working environment based entirely on qualified human resources [3], [9], [11], [20], [21]. The quality of BDSE-based products and services is closely related to the competencies of BDSE specialists [3], [8], [22].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the number of BDSE-oriented job postings in online employment platforms are increasing significantly [3], [25]. When viewed from this perspective, it is envisaged that the identification of up-to-date knowledge and skills sought by the BDSE industry will provide significant contributions to meet the requirements for qualified labor in this area [21].…”

Section: Introductionmentioning

confidence: 99%

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Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling

Gürcan

Çağıltay

2019

IEEE Access

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Software engineering is a data-driven discipline and an integral part of data science. The introduction of big data systems has led to a great transformation in the architecture, methodologies, knowledge domains, and skills related to software engineering. Accordingly, education programs are now required to adapt themselves to up-to-date developments by first identifying the competencies concerning big data software engineering to meet the industrial needs and follow the latest trends. This paper aims to reveal the knowledge domains and skill sets required for big data software engineering and develop a taxonomy by mapping these competencies. A semi-automatic methodology is proposed for the semantic analysis of the textual contents of online job advertisements related to big data software engineering. This methodology uses the latent Dirichlet allocation (LDA), a probabilistic topic-modeling technique to discover the hidden semantic structures from a given textual corpus. The output of this paper is a systematic competency map comprising the essential knowledge domains, skills, and tools for big data software engineering. The findings of this paper are expected to help evaluate and improve IT professionals' vocational knowledge and skills, identify professional roles and competencies in personnel recruitment processes of companies, and meet the skill requirements of the industry through software engineering education programs. Additionally, the proposed model can be extended to blogs, social networks, forums, and other online communities to allow automatic identification of emerging trends and generate contextual tags. INDEX TERMS Big data software engineering, competency map, knowledge domains and skill sets, topic modeling, latent Dirichlet allocation. with the Department of Informatics, Karadeniz Technical University, from 2001 to 2014, where he has been an Instructor with the Center for Research and Application in Distance Education, since 2015. His research interests include trend analysis, sentiment analysis, statistical topic modeling, engineering education, data mining, machine learning, big data analytics, and text mining. NERGIZ ERCIL CAGILTAY received the degree in computer engineering and the Ph.D. degree in instructional technologies from Middle East Technical University, Turkey. She worked for commercial and government organizations as a Project Manager for more than eight years in Turkey. She was also with the Indiana University Digital Library Program as a System Analysis and a Programmer for four years. She has been with the Software Engineering Department, Atilim University, Turkey, since 2003, as an Associate Professor. Her main research interests include information systems, medical information systems, engineering education, instructional systems technologies, distance education, e-learning, and medical education.

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Section: A Big Data Software Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling

Gürcan

Çağıltay

2019

IEEE Access

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“…En este sentido se ha desarrollado una metodología para estudiar la efectividad de la utilización de estas aplicaciones interactivas para mejorar la educación en ingeniería de control (Vargas et al, 2020). En esta metodología los datos se organizan jerárquicamente en un modelo basado en competencias (Moon, 2002), (Gasmi and Bouras, 2018), (Vargas et al, 2020), para permitir a los instructores analizar la información en diferentes niveles de abstracción. Mientras que los niveles más altos proporcionan una visión panorámica de los resultados, los análisis detallados ayudan a identificar con precisión los efectos de la innovación.…”

Section: Una Herramienta Interactiva De Diseño De Sistemas De Control Linealesunclassified

Un enfoque interactivo para el análisis y diseño de sistemas de control utilizando el método del lugar de las raíces

Díaz

Costa‐Castelló

Dormido

2021

Rev. iberoam. autom. inform. ind.

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<p>El lugar de las raíces es un método clásico para el análisis y diseño de sistemas lineales de control que ha formado parte desde su irrupción en los años cincuenta de los programas de los cursos básicos de control automático. La aparición de herramientas software de diseño interactivo de sistemas de control, que dibujan fácilmente el lugar de las raíces, permite a los docentes centrarse en explicar cómo afecta al lugar de las raíces la introducción de nuevos polos/ceros en la función de transferencia en lazo abierto, en vez de en explicar sus reglas de construcción. En este trabajo se presenta una nueva metodología docente para introducir la técnica del lugar de las raíces en cursos básicos de control que fomenta la utilización de una herramienta interactiva, como por ejemplo la herramienta LCSD desarrollada por los autores, para reforzar su aprendizaje.</p>

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“…It can also reason about new or hidden knowledge based on known factual knowledge. Ontologies are widely used in many domains, such as information science [30,31], medical science [32,33], and education [34,8].…”

Section: The Ontology In Sarmentioning

confidence: 99%

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

Sun

Chen

2019

Future Internet

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The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot to understand how to make smart decisions. The ontology is divided into three parts, namely entity ontology, environment ontology, and task ontology. Web Ontology Language (OWL) is adopted to represent these three types of ontology. Through ontology and Semantic Web Rule Language (SWRL) rules, the robot infers the tasks to be performed according to the environment state and at the same time obtains the semantic information of the victims. Then, the paper proposes an ontology-based algorithm for task planning to get a sequence of atomic actions so as to complete the high-level inferred task. In addition, an indoor experiment was designed and built for the SAR scenario using a real robot platform—TurtleBot3. The correctness and usability of the ontology and the proposed methods are verified by experiments.

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Ontology-Based Education/Industry Collaboration System

Cited by 27 publications

References 20 publications

Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling

Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling

Un enfoque interactivo para el análisis y diseño de sistemas de control utilizando el método del lugar de las raíces

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

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