Unit of Analysis in Digitally-Enabled Electronic Procurement Research

2020 ◽  
pp. 83-103
Author(s):  
Md Mahbubur Rahim ◽  
Maryam Jabberzadeh ◽  
Nergiz Ilhan
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Critical Issue ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
Unit Of Analysis ◽  
Electronic Procurement ◽  
Future Research Directions ◽  
Methodological Concern

E-procurement systems that have been in place for over a decade have begun incorporating digital tools like big data, cloud computing, internet of things, and data mining. Hence, there exists a rich literature on earlier e-procurement systems and advanced digitally-enabled e-procurement systems. Existing literature on these systems addresses many research issues (e.g., adoption) associated with e-procurement. However, one critical issue that has so far received no rigorous attention is about “unit of analysis,” a methodological concern of importance, for e-procurement research context. Hence, the aim of this chapter is twofold: 1) to discuss how the notion of “unit of analysis” has been conceptualised in the e-procurement literature and 2) to discuss how its use has been justified by e-procurement scholars to address the research issues under investigation. Finally, the chapter provides several interesting findings and outlines future research directions.

scholarly journals Data Placement in Era of Cloud Computing: a Survey, Taxonomy and Open Research Issues

2019 ◽  
Vol 20 (2) ◽  
pp. 377-398 ◽  
Author(s):  
Avinash Kaur ◽  
Pooja Gupta ◽  
Manpreet Singh ◽  
Anand Nayyar
Keyword(s):  
Cloud Computing ◽  
Workflow Management ◽  
Data Placement ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
Cloud Computing Environment ◽  
Factors Affecting ◽  
Complete Survey ◽  
Future Research Directions

In cloud computing, data placement is a critical operation performed as part of workflow management and aims to find the best physical machine to place the data. It has direct impact on performance, cost and execution time of workflows. Number of data placement algorithms is designed in cloud computing environment that aimed to improve various factors affecting the workflows and their execution including the movement of data among data centers. This paper provides a complete survey and analyses of existing data placement schemes proposed in literature for cloud computing. Further, it classifies data placement schemes based on their assess capabilities and objectives. Further objectives and properties of data placement schemes are compared. Finally future research directions are provided with concluding remarks.

Defining and Evaluating A Novel Penalty Model for Managing Violations in the Cloud Computing

2017 ◽  
Vol 9 (2) ◽  
pp. 36-52
Author(s):  
Adil Maarouf ◽  
Bouchra El Qacimy ◽  
Abderrahim Marzouk ◽  
Abdelkrim Haqiq
Keyword(s):  
Cloud Computing ◽  
Critical Issue ◽  
Service Level ◽  
Future Research ◽  
Calculation Methods ◽  
Research Directions ◽  
Penalty Cost ◽  
Proposed Model ◽  
Cloud Providers ◽  
Future Research Directions

Managing and applying penalties has become a critical issue for Cloud Computing. In this paper, the authors investigate this issue and present the most frequently used definitions of service level agreements (SLA) penalty functions. They identify the characteristics of these functions by highlighting their strengths and weaknesses. They survey and analyze various penalty calculation and availability calculation methods of cloud providers. Then, they propose a Novel Penalty Model for computing the penalty cost of the violations and present formalization for the penalty concerned. They also propose a business model for cloud providers to manage their profit. An example application will be presented to demonstrate the effectiveness of the proposed model. Finally, the paper notes some challenges and future research directions.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

Cloud and Mobile

2016 ◽  
pp. 1-20 ◽  
Author(s):  
Antonio Miguel Rosado da Cruz ◽  
Sara Paiva
Keyword(s):  
Cloud Computing ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Apps ◽  
Mobile Technologies ◽  
Future Research ◽  
Mobile Cloud ◽  
Research Projects ◽  
Research Directions ◽  
Future Research Directions

Mobile computing and Cloud computing are two of the most growing technologies in number of users, practitioners and research projects. This chapter surveys mobile technologies and applications, along with cloud computing technologies and applications, presenting their evolution and characteristics. Then, building on mobile devices limitations and mobile apps increasing need of resources, and on the cloud computing ability to overcome those limitations, the chapter presents mobile cloud computing, and characterizes it by addressing approaches to augment mobile devices capabilities. The chapter is settled after some views about future research directions and some concluding remarks.

Augmented Reality Research for Architecture and Design

2012 ◽  
pp. 225-237 ◽  
Author(s):  
Mi Jeong Kim ◽  
Xiangyu Wang ◽  
Xingquan Zhu ◽  
Shih-Chung Kang
Keyword(s):  
Augmented Reality ◽  
Emerging Technology ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
The Past ◽  
Growing Body ◽  
Future Research Directions ◽  
Architecture And Design

A growing body of research has shown that Augmented Reality (AR) has the potential to contribute to interaction and visualization for architecture and design. While this emerging technology has only been developed for the past decade, numerous journals and conferences in architecture and design have published articles related to AR. This chapter reviews 44 articles on AR especially related to the architecture and design area that were published from 2005 to 2011. Further, this chapter discusses the representative AR research works in terms of four aspects: AR concept, AR implementation, AR evaluation, and AR industry adoption. The chapter draws conclusions about major findings, research issues, and future research directions through the review results. This chapter will be a basis for future research of AR in architecture and design areas.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2016 ◽  
pp. 212-244
Author(s):  
Boutheina Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2020 ◽  
pp. 700-725
Author(s):  
Boutheina A. Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

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