Prof. Ahmed Elragal
Luleå tekniska universitet
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Academic Career Length
16Years (2011-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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1 January 2016 in Procedia Computer Science
Journal Article
NSNada Sanad
Nada SanadAhmed ElragalInformation is a key success factor influencing the performance of decision makers, specifically the quality of their decisions. Nowadays, sheer amounts of data are available for organizations to analyze. Data is considered the raw material of the 21st century, and abundance is assumed with today's 15 billion devices [aka Things!] already connected to the Internet. Accordingly, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such rapidly changing data of high volume, velocity, variety, veracity, and value by using big data analytics. This paper aims to research how big data analytics can be integrated into the decision making process. Accordingly, using a design science methodology, the “Big – Data, Analytics, and Decisions” (B-DAD) framework was developed in order to map big data tools, architectures, and analytics to the different decision making phases. The ultimate objective and contribution of the framework is using big data analytics to enhance and support decision making in organizations, by integrating big data analytics into the decision making process. Consequently, an experiment in the retail industry was administered to test the framework. Accordingly, results showed added value when integrating big data analytics into the decision making process
4 March 2021 in Journal of Decision Systems
Journal Article
Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions.
Journal Article
Alexander BakumenkoAhmed ElragalBookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts.
1 January 2015 in Procedia Computer Science
Journal Article
Moutaz HaddaraAhmed ElragalIn 2011, at the Hanover Fair, the term Industry 4.0 was first coined. In October 2012, the Working Group on Industry 4.0, presented a set of implementation recommendations to the German government. The term Industry 4.0 initiates from a project in the high-tech strategy of the German government. Such project advocates the computerization of the manufacturing industry. It is also known as the 4th industrial revolution. Precisely speaking, industry 4.0 is based on the technological concepts of cyber-physical systems, Internet of Things (IoT), which enables the Factory of the Future (FoF). Within the modular structured smart factories of Industry 4.0, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, Cyber-physical systems communicate and cooperate with each other and with humans in real time. Enterprise resource planning (ERP) systems are considered the backbone for the Industry 4.0. Thus, this paper attempts to answer the research question: “Are today's ERP systems ready for the FoF?”. We have conducted interviews with manufacturers, ERP vendors, and partners in order to seek their feedback on the readiness of ERP systems for the FoF. Our results show that ERP systems are ready for the FoF
23 June 2017 in Journal of Big Data
Journal Article
Ahmed ElragalRKRalf Klischewski
Ralf Klischewski
Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions based on executing a sequence of data processing while seemingly abstaining from being theoretically informed about the subject matter. This paper discusses how to deal with the shift from theory-driven to process-driven prediction through analyzing the BDA steps and identifying the epistemological challenges and various needs of theoretically informing BDA throughout data acquisition, preprocessing, analysis, and interpretation. We suggest a theory-driven guidance for the BDA process including acquisition, pre-processing, analytics and interpretation. That is, we propose—in association with these BDA process steps—a lightweight theory-driven approach in order to safeguard the analytics process from epistemological pitfalls. This study may serve as a guideline for researchers and practitioners to consider while conducting future big data analytics.
About Me
Prof. Ahmed Elragal (PhD, MBA, BSc) has been a Professor of Information Systems at Luleå University of Technology in Sweden since January 2014. He is also an Adjunct Professor of Information Systems at the American University in Cairo (AUC).
Prof. Elragal used to work at the Department of Business Informatics and Operations Management, German University in Cairo (GUC), 2007–2018, and the Arab Academy for Science, Technology, and Maritime Transport (AASTMT), 1992–2007.
Prof. Elragal has over sixty research papers and articles published at international conferences and journals. He is a member of the editorial board of I & M and IJBIR journals. Prof. Elragal is the Associate Editor of the International Journal of Information Systems and Project Management (IJISPM). He is the coauthor of Pearson’s AWE of the MIS textbook (Laudon, Laudon, and Elragal).
He is the winner of the 2010’s international case study competition on “Business Intelligence”, a prestigious international award.
He has over 15 years of consulting experience, focused mainly on enterprise systems and business intelligence. He has helped different regional as well as multinationals organizations (including SAP, Teradata, Hyperone & Egypt Census Bureau) in the areas of enterprise systems, business intelligence, data mining, big data, analytics, etc.
He is also a certified SAP Solution Architect since 2008. He is also expert working with Tableau, RapidMiner and R & Python Programming languages.