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  • 1.
    Al-Serafi, Ayman
    et al.
    Teradata Corporation.
    Elragal, Ahmed
    Department of Business Informatics and Operations, German University in Cairo (GUC), Cairo.
    Visual trajectory pattern mining: An exploratory study in baggage handling systems2014In: Advances in data mining: Advances in data mining : applications and theoretical aspects : 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings / [ed] Petra Perner, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 159-173Conference paper (Refereed)
    Abstract [en]

    There is currently a huge amount of data being collected about movements of objects. Such data is called spatiotemporal data and paths left by moving-objects are called trajectories. Recently, researchers have been targeting those trajectories for extracting interesting and useful knowledge by means of pattern analysis and data mining. But, it is difficult to analyse huge datasets of trajectories without summarizing them and visualizing them for the knowledge seeker and for the decision makers. Therefore, this research paper focuses on utilizing visual techniques and data mining analysis of trajectory patterns in order to help extract patterns and knowledge in an interactive approach. The research study proposes a research framework which integrates multiple data analysis and visualization techniques in a coherent architecture in support of interactive trajectory pattern visualization for the decision makers. An application case-study of the techniques is conducted on an airport's baggage movement data within the Baggage Handling System (BHS). The results indicate the feasibility of the approach and its methods in visually analysing trajectory patterns in an interactive approach which can support the decision maker. © 2014 Springer International Publishing Switzerland.

  • 2. Amin, Marian Hany
    et al.
    Mohamed, Ehab Kamel
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Corporate Social Responsibility disclosure via Twitter by top listed UK companies: A Data Science Approach2018Conference paper (Refereed)
  • 3.
    Amin, Marian Hany
    et al.
    The German University in Cairo.
    Mohamed, Ehab Kamel
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Financial Disclosure on Twitter by Top Listed UK Companies: A Data Science Approach2018Conference paper (Refereed)
    Abstract [en]

    Ongoing advancements in technology have changed dramatically the disclosure media that companies adopt. Such disclosure media have evolved from the traditional paper-based ones, to the internet as the new platform to disclose information via companies’ designated websites. However, currently the new media for disclosures are the social media. The aim of this paper is to investigate corporate social media accounts for financial disclosure, as well as, identify its determinants. The sample of the study is comprised of the tweets posted on the Twitter accounts belonging to the FTSE 350 constituents. Topic modeling is applied to identify financial disclosure tweets and logistic regression is run to identify the determinants of financial disclosure on Twitter. Results show that companies use Twitter to make corporate disclosures and some board characteristics are found to have a significant relationship with financial disclosure.

  • 4. Amin, Marian Hany
    et al.
    Mohamed, Ehab Kamel
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Twitter: An emerging media for corporate disclosure2018Conference paper (Refereed)
  • 5.
    Badawy, Shaimaa Ali
    et al.
    University of Western Ontario.
    Elragal, Ahmed
    German University in Cairo (GUC).
    Gabr, Mahmoud Mohamed Hassan
    Faculty of Science, Alexandria University.
    Multivariate similarity-based conformity measure (MSCM: An outlier detection measure for data mining applications2008In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications Machine Learning: as part of the 26th IASTED International Multi-Conference on Applied Informatics ; February 11 - 13, 2008, Innsbruck, Austria / [ed] Alexander Gammermann, Anaheim, Calif: ACTA Press, 2008, p. 314-320Conference paper (Refereed)
    Abstract [en]

    Outliers, the odd objects in the dataset, can be viewed from two different perspectives; the outliers as undesirable objects that should be treated or deleted in the data preparation step of the data mining process, and the outliers as interesting objects that are identified for their own interest in the data mining step of the mining process. In the latter case, outliers shouldn't be removed, that's why one of the main categories of tasks performed by data mining techniques is outlier detection. Applications that make use of such detection include credit card fraud detection and network intrusion detection. Most of the available outlier detection techniques rely in a distance measure to compare the objects in the dataset which imposed the restriction of dealing with numeric data. In this paper a new multivariate similarity-based conformity measure (MSCM) is suggested to be used to detect outliers in datasets that contain attributes of different data types. The MSCM satisfies two other desirable features; being a multivariate measure and giving ranking instead of a binary judgment of the object. The measure has been applied on three different datasets in order to be evaluated; the measure has shown good results in these experiments.

  • 6.
    Elgendy, Nada
    et al.
    Department of Business Informatics and Operations, German University in Cairo (GUC), Cairo.
    Elragal, Ahmed
    Department of Business Informatics and Operations, German University in Cairo (GUC), Cairo.
    Big data analytics: A literature review paper2014In: Advances in data mining: applications and theoretical aspects : 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings / [ed] Petra Perner, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 214-227Conference paper (Refereed)
    Abstract [en]

    In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, 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 varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. © 2014 Springer International Publishing Switzerland.

  • 7.
    Elgendy, Nada
    et al.
    Department of Business Informatics & Operations Management, German University in Cairo (GuC).
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Analytics in Support of the Decision Making Process2016In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 100, p. 1071-1084Article in journal (Refereed)
    Abstract [en]

    Information 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.

  • 8.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    ERP and Big Data: the inept couple2014In: Procedia Technology - Elsevier, ISSN 2212-0173, E-ISSN 2212-0173, Vol. 16, p. 242-249Article in journal (Refereed)
    Abstract [en]

    The world is witnessing an unprecedented interest in big data. Big data is data that is big in size (volume), big in variety (structured; semi-structured; unstructured), and big in speed of change (velocity). It was reported that almost 90% of the data worldwide was just created in the past 2 years. Therefore, this paper is an attempt to align ERP systems with big data. The objective is to suggest a future research agenda to bring together big data and ERP. While almost everyone is talking about big data at the product or tool level, relationship with social media, relationship with Internet of things, etc. no one has tried to integrate big data and ERP. A research agenda is discussed and introduced in this paper.

  • 9.
    Elragal, Ahmed
    et al.
    German University in Cairo (GUC).
    Abouseif, Heba George
    German University in Cairo (GUC).
    Classification of enterprise portals: A data mining approach2010In: Knowledge Management and Innovation: A Business Competitive Edge Perspective - Proceedings of the 15th International Business Information Management Association Conference, IBIMA 2010, International Business Information Management Association (IBIMA), 2010, Vol. 2, p. 1287-1295Conference paper (Refereed)
    Abstract [en]

    Classification of enterprise portal systems (EP) based on their features is the topic of this paper. We propose a classification based on cluster analysis which depends on features collected from the internet. Results showed that systems were found to belong to different classes based on their features

  • 10.
    Elragal, Ahmed
    et al.
    German University in Cairo (GUC).
    Al-Serafi, Ayman M.
    German University in Cairo (GUC).
    The effect of implementing ERP systems on business performance: An exploratory case-study2010In: Knowledge Management and Innovation: A Business Competitive Edge Perspective - Proceedings of the 15th International Business Information Management Association Conference, IBIMA 2010, International Business Information Management Association (IBIMA), 2010, Vol. 2, p. 1296-1314Conference paper (Refereed)
    Abstract [en]

    There is currently plenty of research concerning the effect of Enterprise Resource Planning (ERP) on business performance. Previous research has shown a mixed relationship between ERP and business performance where some suggested that ERP improves performance while others found that it does not. This paper investigates this topic by analyzing a detailed case-study consisting of an Egyptian SME, as a branch of a multinational company. The results indicate in general many benefits in business performance were achieved after implementing the ERP as reported by many users, but have also shown that few benefits previously linked to ERP were not fully achieved.

  • 11.
    Elragal, Ahmed
    et al.
    German University in Cairo (GUC).
    El-Gendy, Nada
    German University in Cairo.
    Trajectory data mining: Integrating semantics2013In: Journal of Enterprise Information Management, ISSN 1741-0398, E-ISSN 1758-7409, Vol. 26, no 5, p. 516-535Article in journal (Refereed)
    Abstract [en]

    Purpose: Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be understood. Therefore, the purpose of this paper is to enrich trajectories with semantic annotations, such as the name of the location where the trajectory has stopped, so that the paper is able to attain quality decisions. Design/methodology/approach: An experiment was conducted to explain that the use of raw trajectories alone is not enough for the decision-making process and detailed pattern extraction. Findings: The findings of the paper indicates that some fundamental patterns and knowledge discovery is only obtainable by understanding the semantics underlying the position of each point. Research limitations/implications: The unavailability of data are a limitation of the paper, which would limit its generalizability. Additionally, the lack of availability of tools for automatically adding semantics to clusters posed as a limitation of the paper. Practical implications: The paper encourages governments as well as businesses to analyze movement data using data mining techniques, in light of the surrounding semantics. This will allow, for example, solving traffic congestions, since by understanding the movement patterns, the traffic authority could make decisions in order to avoid such congestions. Moreover, it could also help tourism authorities, at national levels, to know tourist movement patterns and support these patterns with the required logistical support. Additionally, for businesses, mobile operators could dynamically enhance their services, voice and data, by knowing the semantically enriched patterns of movement. Originality/value: The paper contributes to the already rare literature on trajectory mining, enhanced with semantics. Mainstream literature focusses on either trajectory mining or semantics, therefore the paper claims that the approach is novel and is needed as well. By integrating mining outcomes with semantic annotation, the paper contributes to the body of knowledge and introduces, with lab evidence, the new approach. © Emerald Group Publishing Limited.

  • 12.
    Elragal, Ahmed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Haddara, Moutaz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Analytics: A Text Mining-Based Literature Analysis2014In: NOKOBIT - Norsk konferanse for organisasjoners bruk av informasjonsteknologi, ISSN 1892-0748, E-ISSN 1894-7719, Vol. 22, no 1Article in journal (Refereed)
    Abstract [en]

    This literature review paper summarizes the state-of-the-art research on big data analytics. Due to massive amount of data exchanged everyday and the increased need for better data-based decision, businesses nowadays are looking for ways to efficiently manage, and optimize these huge datasets. Moreover, because of globalization, partnerships, value networks, emergence of social networks, and the huge information flow across and within enterprises, more and more businesses are interested in utilizing big data analytics. The main focus of this paper is to elucidate knowledge on the characteristics of big data analytics literature as well as explore the areas that lack sufficient research within the big data analytics domain, suggest future research avenues, as well as, present the current research findings that could aid practitioners, researchers, and vendors when embarking on big data analytics projects. Towards that end, we have reviewed 24 publications between 2010 and 2014. Results of text mining the papers revealed that they belong to three clusters with both common as well as distinct characteristics. The reviewed papers were clustered into three main themes, 1) technical algorithsms; 2) processing, cloud computing, opportunities & challenges; and 3) performance, prediction, and distributed systems.

  • 13.
    Elragal, Ahmed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Haddara, Moutaz
    Department of Technology, Kristiania University College, Oslo, Norway.
    Design Science Research: Evaluation in the Lens of Big Data Analytics2019In: Systems, ISSN 2079-8954, Vol. 7, no 2, article id 27Article in journal (Refereed)
    Abstract [en]

    Abstract: Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

  • 14.
    Elragal, Ahmed
    et al.
    German University in Cairo (GUC).
    Haddara, Moutaz
    University of Agder.
    The Future of ERP Systems: look backward before moving forward2012In: Procedia Technology - Elsevier, ISSN 2212-0173, E-ISSN 2212-0173, Vol. 5, p. 21-30Article in journal (Refereed)
    Abstract [en]

    This paper explores the enterprise resource planning (ERP) systems literature in an attempt to elucidate knowledge to help us see the future of ERP systems' research. The main purpose of this research is to study the development of ERP systems and other related areas in order to reach the constructs of mainstream literature. The analysis of literature has helped us to reach the key constructs of an as-is scenario, those are: history and development of ERP systems, the implementation life cycle, critical success factors and project management, and benefits and costs. However, the to-be scenario calls for more up-to-date research constructs of ERP systems integrating the following constructs: social networks, cloud computing, enterprise 2.0, and decision 2.0. In the end, the conclusion section will establish the link between the as-is and to-be scenarios opening the door for more novel ERP research areas

  • 15.
    Elragal, Ahmed
    et al.
    German University in Cairo (GUC).
    Haddara, Moutaz
    University of Agder.
    The use of experts panels in ERP cost estimation research2010In: ENTERprise Information Systems: International Conference, CENTERIS 2010, Viana do Castelo, Portugal, October 20-22, 2010, Proceedings / [ed] João Eduardo Quintela Varajão ; Maria Manuela Cruz-Cunha ; Goran D. Punik; António Trigo, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2010, p. 97-108Conference paper (Refereed)
    Abstract [en]

    This paper is an effort towards illustrating the use of expert panel (EP) as a mean of eliciting knowledge from a group of enterprise resource planning (ERP) experts as an exploratory research. The development of a cost estimation model (CEM) for ERP adoptions is very crucial for research and practice, and that was the main reason behind the willingness of experts to participate in this research. The use of EP was very beneficial as it involved various data collection and visualisation techniques, as well as data validation and confirmation. Beside its advantages, one of the main motives for using a group technique is that it is difficult to find a representative sample for a casual survey method, as ERP experts and consultants are rare to find, especially in the scope of SMEs' ERP implementations. It is worth noting that the panel reached consensus regarding the results of the EP. The experts modified and enhanced the initial cost drivers (CD) list largely, as they added, modified, merged and split different costs drivers. In addition, the experts added CF (sub-factors) that could influence or affect each cost driver. Moreover, they ranked the CD according to their weight on total costs. All of this helped the authors to better understand relationships among various CF

  • 16.
    Elragal, Ahmed
    et al.
    German University in Cairo.
    Haddara, Moutaz
    Centre of Enterprise Systems, Department of Information Systems, University of Agder.
    The use of experts panels in ERP cost estimation research2012In: Organizational integration of enterprise systems and resources: advancements and applications ; [... 22 papers selected from the international conference CENTERIS - Conference on ENTERprise Information Systems held in Viana do Castelo, Portugal, in October 2010], Hershey, Pa.: Business Science Reference , 2012, p. 117-130Chapter in book (Refereed)
    Abstract [en]

    This chapter is an effort towards illustrating the use of expert panels (EP) as a means of eliciting knowledge from a group of enterprise resource planning (ERP) experts in an exploratory research. The development of a cost estimation model for ERP adoptions is very crucial for research and practice, and that was the main reason behind the willingness of experts to participate in this research. The use of EP was very beneficial as it involved various data collection and visualization techniques, as well as data validation and confirmation. Arguments for using EP over other group techniques are presented in this chapter. Experts modified and enhanced the initial cost drivers list and their sub-factors significantly, as they added, modified, merged and split different costs. Moreover, they ranked the cost drivers according to their weight on total costs. All of this helped the authors to better understand relationships among various cost factors

  • 17.
    Elragal, Ahmed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Hassanien, Hossam El-Din
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Augmenting Advanced Analytics into Enterprise Systems: A Focus on Post-Implementation Activities2019In: Systems, E-ISSN 2079-8954, Vol. 7, no 2, article id 31Article in journal (Refereed)
    Abstract [en]

    An analytics-empowered enterprise system looks to many organizations to be a far-fetched target, owing to the vast amounts of factors that need to be controlled across the implementation lifecycle activities, especially during usage and maintenance phases. On the other hand, advanced analytics techniques such as machine learning and data mining have been strongly present in academic as well as industrial arenas through robust classification and prediction. Correspondingly, this paper is set out to address a methodological approach that works on tackling post-live implementation activities, focusing on employing advanced analytics techniques to detect (business process) problems, find and recommend a solution to them, and confirm the solution. The objective is to make enterprise systems self-moderated by reducing the reliance on vendor support. The paper will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach. Employing an advanced analytics engine has the potential to support post-implementation activities. Our research is innovative in two ways: (1) it enables enterprise systems to become self-moderated and increase their availability; and (2) the IT artifact i.e., the analytics engine, has the potential to solve other problems and be used by other systems, e.g., HRIS. This paper is beneficial to businesses implementing enterprise systems. It highlights how enterprise systems could be safeguarded from retirement caused by post-implementation problems.

  • 18.
    Elragal, Ahmed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Klischewski, Ralf
    German University in Cairo (GUC).
    Theory-driven or Process-driven Prediction?: Epistemological Challenges of Big Data Analytics2017In: Journal of Big Data, E-ISSN 2196-1115, Vol. 4, no 1, article id 19Article in journal (Refereed)
    Abstract [en]

    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.

  • 19.
    Elragal, Ahmed
    et al.
    German University in Cairo (GUC).
    Kommos, Malak El
    German University in Cairo (GUC).
    In-house versus In-cloud ERP systems: A comparative study2012In: Innovation Vision 2020: Sustainable growth, Entrepreneurship, and Economic Development - Proceedings of the 19th International Business Information Management Association Conference, International Business Information Management Association (IBIMA), 2012, Vol. 2, p. 605-616Conference paper (Refereed)
    Abstract [en]

    This paper provides a framework for comparison between the implementation of ERP systems in-house versus the in-cloud implementations. The paper first establishes a framework for the comparison based on three factors: pre-live i.e., the implementation methodologies of both options, post-live i.e., cost, time, and the user-friendliness of the systems, and third is other factors i.e., security and scalability. Results show that in-cloud systems are faster to implement, less costly and easier to use, and they are scalable. Inhouse system, compare to in-cloud, give organizations more control and hence they seems to be more secure to many organizations

  • 20.
    Elragal, Ahmed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Päivärinta, Tero
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Opening Digital Archives and Collections With Emerging Data Analytics Technology: A Research Agenda2017In: Tidsskriftet Arkiv, ISSN 1891-8107, E-ISSN 1891-8107, Vol. 8, no 1Article in journal (Refereed)
    Abstract [en]

    In the public sector, the EU legislation requires preservation and opening of increasing amounts of heterogeneous digital information that should be utilized by citizens and businesses. While technologies such as big data analytics (BDA) have emerged, opening of digital archives and collections at a large scale is in its infancy. Opening archives and collections involve also particular requirements for recognizing and managing issues of privacy and digital rights. As well, ensuring the sustainability of the opened materials and economical appraisal of digital materials for preservation require robust digital preservation practices. We need to proceed beyond the state-of-the-art in opening digital archives and collections through the means of emerging big data analytics and validating a novel concept for analytics which then enables delivering of knowledge for citizens and the society. We set out an agenda for using BDA as our strategy for research and enquiry and for demonstrating the benefit of BDA for opening digital archives by civil servants and for citizens. That will –eventually -transform the preservation practices, and delivery and use opportunities of public digital archives. Our research agenda suggests a framework integrating four domains of inquiry, analytics-enhanced appraisal, analytics-prepared preservation, analytics-enhanced opening, and analytics-enhanced use, for utilizing the BDA technologies in the domain of digital archives and collections. The suggested framework and research agenda identifies initially particular BDA technologies to be utilized in each of the four domains, and contributes by highlighting a need for an integrated “public understanding of big data” in the domain of digital preservation.

  • 21.
    Elragal, Ahmed
    et al.
    Department of Business Informatics and Operations, German University in Cairo (GUC), Cairo.
    Raslan, Hisham
    Teradata Egypt.
    Analysis of trajectory data in support of traffic management: A data mining approach2014In: Advances in data mining: applications and theoretical aspects : 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings / [ed] Petra Perner, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 174-188Conference paper (Refereed)
    Abstract [en]

    Huge amount of location and tracking data is gathered by location and tracking technologies, such as global positioning system (GPS) and global system for mobile communication (GSM) devices; leading to the collection of large spatiotemporal datasets and to the opportunity of discovering usable knowledge about movement behavior. Movement behavior can be extremely useful in many ways when applied, for example, in the domain of traffic management, planning metropolitan areas, mobile marketing, tourism, etc. In this research, we move towards this direction and propose a framework for finding trajectory patterns of frequent behaviors using GSM data. The research question is «how to use trajectory data analysis in support of solving traffic management problems utilizing data mining techniques?» Our framework is illustrated to explain how GSM data can provide accurate information about population movement behavior, and hence support traffic decisions. © 2014 Springer International Publishing Switzerland.

  • 22.
    El-Telbany, Ola
    et al.
    German University in Cairo (GUC).
    Elragal, Ahmed
    German University in Cairo (GUC).
    Decision 2.0: An exploratory case study2012In: 2012 45th Hawaii International Conference on System Sciences: (HICSS 2012); Maui, Hawaii, USA, 4 - 7 January 2012 / [ed] Ralph H. Sprague, Piscataway, NJ: IEEE Communications Society, 2012, p. 432-443, article id 6148659Conference paper (Refereed)
    Abstract [en]

    The emergence of the Enterprise 2.0 technologies indicates that they can provide value to different types of users and potentially different types of value. Many published research explored what these E2.0 tools and applications can offer to organizations, such as collaboration platforms, social networking and user-created content, enhancing their productivity and management among employees. However, little research was devoted to study the effect these tools and applications have on the decision making process. Decision 2.0 has received little attention in literature, especially from the standpoint of making use of the "crowd". Therefore, this paper focuses on this research gap with a case study in an attempt to elucidate and extract knowledge to answer this question "How does decision 2.0 make use of the crowd to support the traditional decision making process and hence add value to organizations through collaboration and collective intelligence?"

  • 23.
    El-Telbany, Ola
    et al.
    German University in Cairo, Al-Tagamoa Al-Khames New Cairo City, Cairo .
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Gamification of Enterprise Systems: A Lifecycle Approach2017In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 121, p. 106-114Article in journal (Refereed)
    Abstract [en]

    Introducing an Enterprise Resource Planning (ERP) system within an organization can bring many benefits and paybacks, yet an effective implementation of a fully functioning ERP system is still a challenge, the odds are high the costly investment might turn into an implementation failure or even lead to bankruptcy. To prevent such situations, organizations need to go through several changes, and carefully manage the critical success factors affecting each stage of the ERP implementation lifecycle, respectively. Previous studies have observed that the majority of the challenges faced during the implementation of ERP systems arise from social and organizational aspects rather than technical ones. This is where gamification comes to the rescue. This research adopts a design science paradigm in an attempt to develop a gamified process for the ERP lifecycle, to ease and enhance the ERP implementation process. The objective of this research is threefold. Firstly, to explore the benefits ERP systems can render via the gamification of the ERP lifecycle. Secondly, to pinpoint the ERP lifecycle phases that are most likely to benefit from gamification. Thirdly, to gamify these formerly identified phases; that were found to be mostly affected by gamification, and test for the impact of gamification on them.

  • 24.
    Haddara, Moutaz
    et al.
    Department of Information Systems, University of Agder.
    Elragal, Ahmed
    German University in Cairo (GUC).
    Cost Estimation for ERP Adoption in SMEs: A Conceptual Framework2009Conference paper (Refereed)
  • 25.
    Haddara, Moutaz
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    German University in Cairo (GUC).
    ERP adoption cost factors identification and classification: a study in SMEs2013In: International journal of information systems and project management, ISSN 2182-7796, E-ISSN 2182-7788, Vol. 1, no 2, p. 5-21Article in journal (Refereed)
    Abstract [en]

    Enterprise resource planning (ERP) systems adoptions require substantial resources and investments. The majority of businesses around the globe can be considered to be small and medium sized enterprises (SMEs). Thus, SMEs are seen to be typical companies that are the cornerstone of most economies. Compared with large enterprises, an SME-context contains several characteristics, and scarcity of resources is among the top of them. For SMEs, unplanned costs escalation could pose a serious threat to their stability and survival in the market. Frequently, ERP projects have crossed their estimated budgets and schedules. Researchers and practitioners state that a prevailing number of ERP adoption projects fail due to inaccurate or to too optimistic budgets/schedules. In addition, many organizations face difficulties in identifying the potential cost factors that could occur during their ERP adoption lifecycle. While focusing on the SME-context, this research attempts to identify potential costs that could occur in ERP adoptions. The research method employed in this study targeted diverse stakeholders and experts involved in ERP projects in Egypt. This research provides a list of cost factors and their classifications that could aid adopting organizations to better estimate their needed ERP project budgets. In particular, this research explores the direct and indirect cost factors that occur in ERP adoptions in Egyptian SMEs. Also, this study investigates the influence of some SME-specific contextual factors on costs. Moreover, the paper provides a ranking of cost factors according to their impact on total adoption costs.

  • 26.
    Haddara, Moutaz
    et al.
    Department of Information Systems, University of Agder.
    Elragal, Ahmed
    Department of Information Systems, German University in Cairo (GUC).
    ERP lifecycle: A retirement case study2013In: Information Resources Management Journal, ISSN 1040-1628, E-ISSN 1533-7979, Vol. 26, no 1, p. 1-11Article in journal (Refereed)
    Abstract [en]

    A lot of research has been undertaken focusing on ERP systems lifecycles, but very little paid attention to retirement. ERP retirement means the replacement of an ERP with another. The aim of this research paper is to investigate why and when should organizations retire their ERP systems. A convenience case study of a SME has been selected from Egypt. The case study under investigation has retired their local ERP system and replaced it with SAP ERP. Results of the analysis indicate that reasons of retirement were: wrong selection, users were not involved in the selection process, and lack of an official implementation methodology. This is considered a new finding since main stream literature was mainly focused on retirement after maturity. Copyright © 2013, IGI Global.

  • 27.
    Haddara, Moutaz
    et al.
    University of Agder.
    Elragal, Ahmed
    German University in Cairo (GUC).
    ERP lifecycle: When to retire your ERP system?2011In: ENTERprise Information Systems: International Conference, CENTERIS 2011, Vilamoura, Algarve, Portugal, October 5-7, 2011, Proceedings / [ed] Maria Manuela Cruz-Cunha ; João Varajão ; Philip Powell; Ricardo Martinho, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2011, p. 168-177Conference paper (Refereed)
    Abstract [en]

    A lot of research has been undertaken focusing on ERP systems lifecycles, but very little paid attention to retirement. ERP retirement means the replacement of an ERP with another. The aim of this research paper is to investigate why and when should organizations retire their ERP systems. A convenience case study of an SME has been selected from Egypt. The case study under investigation has retired their local ERP system and replaced it with SAP ERP. Results of our analysis indicated that reasons of retirement were: wrong selection, users were not involved in the selection process, and lack of an official implementation methodology. This is considered a new finding since main stream literature was mainly focused on retirement after maturity

  • 28.
    Haddara, Moutaz
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    The Readiness of ERP Systems for the Factory of the Future2015In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 64, p. 721-728Article in journal (Refereed)
    Abstract [en]

    In 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 check on the readiness of ERP systems for the FoF. Our results show that ERP systems are ready for the FoF.

  • 29.
    Hassan, Ayaallah
    et al.
    German University in Cairo.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Visualization Tool: a Best-Practice Selection Model2017In: IADIS Information Systems Conference (IS 2017) / [ed] Powell P.,Rodrigues L.,Nunes M.B.,Isaias P., Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 59-68Conference paper (Refereed)
    Abstract [en]

    Big data visualization tools are analytical tools used by organizations for the purpose of discovering knowledge. With the support of interactive visual interfaces, methods and techniques for analyzing big data are applied to facilitate the knowledge discovery process and provide domain relevant insights. Many studies, both academic and industrial, have been conducted in order to investigate visualization tools. However, little research has been conducted in order to study how big data visualization tools could be selected. Consequently, this research is setout to fill-in this gap. Accordingly, a three phases (literature review, Delphi method, and exploratory case study) research process is conducted to propose a big data visualization tool best-practice selection model. The use of this model would help organizations in selecting and obtaining the appropriate visualization tool. The results of this research revealed a number of criteria elements, which formulate the best-practice model for big data visualization tool selection. A total number of 36 criteria have been agreed upon by a number of 14 big data experts. Such criteria belong to six main types: technical, visualization, collaboration & mobility, operational, data governance and managerial requirements. An exploratory case study was conducted on a multi-national telecommunication company in order to test the usability of the model, which attained positive feedback results.

  • 30.
    Klischewski, Ralf
    et al.
    German University in Cairo (GUC).
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Business-IT Alignment in The Arab World: Is There a Fast Track to Maturity?2015In: 23rd European Conference on Information Systems, ECIS 2015, 2015Conference paper (Refereed)
    Abstract [en]

    This research investigates how companies operating in emerging markets and investing in latest IT solutions can be supported in developing maturity in business-IT alignment faster than their predecessors in developed countries. It follows up the consultant perspective through documenting ex-post the participant’s observations, reflected through participant objectivation, to analyze the activities con-ducted, the obstacles faced and the success reached while trying to mature business-IT alignment in selected companies operating in the Arab World. From the five cases included in the sample, evidence was found that specific consultant’s interceptions can indeed “fast-track” the needed improvement in business-IT alignment, especially when the sense of crisis in the company creates a readiness to em-brace the change and the subsequent benefits are obvious enough to those involved to sustain the newly reached maturity level. However, the personality and behavior of the CEO, the attitude of middle management, and the corporate culture as a whole may obstruct all efforts of “fast-tracking” any time, especially when the actors involved adhere to traits deeply rooted in the region’s hierarchical traditions.

  • 31.
    Mekawie, Shereen
    et al.
    German University in Cairo (GUC).
    Elragal, Ahmed
    German University in Cairo (GUC).
    ERP and SCM integration: The impact on measuring business performance2013In: International Journal of Enterprise Information Systems, ISSN 1548-1115, E-ISSN 1548-1123, Vol. 9, no 2, p. 106-124Article in journal (Refereed)
    Abstract [en]

    Organizations rely on various types of information systems (IS) to manage day-to-day business and make decisions such as enterprise resource planning (ERP) and supply chain management (SCM) systems. Organizations rely on ERP systems to replace their legacy systems, integrate core business processes and to help adding value and increasing visibility. Additionally, SCM systems help organizations to enhance relationships with supply chain members. It is essential for organizations to measure their business performance by taking into consideration intra-organizational and inter-organizational indicators. Therefore, the integration between ERP and SCM systems is a key to enable more business performance; that were otherwise hidden. Accordingly, the motive for this paper is to study the influence of ERP-SCM integration on enabling more business performance measures. For this reason, a business performance measures framework was constructed and then tested on two organizations using multi-case study qualitative research approach. Analysis results indicated that integrating ERP and SCM systems would render more performance measures and hence enable better and wider-scope evaluation. Consequently, managers are more informed and accordingly are able to make high quality decisions. Copyright © 2013, IGI Global.

  • 32.
    Mourady, Ahned
    et al.
    Sheffield Hallam University.
    Elragal, Ahmed
    German University in Cairo (GUC).
    Business intelligence in support of EGov healthcare decisions2011In: Proceedings of the European, Mediterranean and Middle Eastern Conference on Information Systems - Informing Responsible Management: Sustainability in Emerging Economies, EMCIS 2011 / [ed] Ahmad Ghuneim; Marinos Themistocleous; Dimitrios N Koufopoulos; Muhammad Kamal, Information Systems Evaluation and Integration Group, 2011, p. 285-293Conference paper (Refereed)
    Abstract [en]

    Integrating business intelligence (BI) as a framework to support EGov decisions is vital. Data mining, a major BI component, is a group of techniques used to find hidden patterns and unknown facts in data sets. In this paper, we implemented the Pan America Health Organization (PAHO) and World Health Organization (WHO) PAHO/WHO hospital safety index to a data set containing six educational hospitals from Alexandria, Egypt. The index results show that five hospitals fall in one category and a remaining hospital falls in another category. Based on the results decisions were about to be taken to allocate resources to enhance safety of hospitals. To validate the index results, we used cluster analysis, a data mining technique. Results show that hospitals fall in two classes; class one has three hospitals whereby class two has the remaining three. That is, by introducing one of the data mining techniques to one of the EGov decision areas we were able to gain more knowledge about the problem domain and hence make more informative decisions. The mining results call for more investigative actions to be taken by EGov projects in order to enhance decision quality to help achieving better safety of hospitals.

  • 33.
    Osman, Ahmed M. Shahat
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Bergvall-Kåreborn, Birgitta
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Analytics and Smart Cities: A Loose or Tight Couple?2017In: Proceedings of the International Conference on ICT, Society and Human Beings 2017: Part of the Multi Conference on Computer Science and Information Systems 2017 / [ed] Kommers P.,Rodrigues L., IADIS , 2017, p. 157-168Conference paper (Refereed)
    Abstract [en]

    Smart City (SC) is an emerging concept aiming at mitigating the challenges raised due to the continuous urbanization development. To face these challenges, government decision makers sponsor SC projects targeting sustainable economic growth and better quality of life for inhabitants and visitors. Information and Communication Technologies (ICT) is the enabling technology for smartening. These technologies yield massive volumes of data known as Big Data (BD). If spawned BD are integrated and analyzed, both city decision makers and citizens can benefit from valuable insights and information services. The process of extracting information and insights from BD is known as Big Data Analytics (BDA). Although BDA involves non-trivial challenges, it attracted academician and industrialist. Surveying the literature reveals the novelty and increasing interest in addressing BD applications in SCs. Although literature is replete with abundant number of articles about SCs applications harnessing BD, comprehensive discussion on BDA frameworks fitting SCs requirements is still needed. This paper attempts to fill this gap. It is a systematic literature review on BDA frameworks in SCs. In this review, we will try to answer the following research questions: what are the big data analytics frameworks applied in smart cities? what are the functional gaps in the current available frameworks? what are the conceptual guidelines of designing integrated scalable big data analytics frameworks for smart cities purposes? The paper concludes with a proposal for a novel conceptual analytics framework to serve SCs requirements. Additionally, open issues and further research directions are presented.

  • 34.
    Rigaki, Maria
    et al.
    Luleå University of Technology.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Adversarial deep learning against intrusion detection classifiers2017In: CEUR Workshop Proceedings / [ed] Kott A.,Pechoucek M., CEUR-WS , 2017, Vol. 2057, p. 35-48Conference paper (Refereed)
    Abstract [en]

    Traditional approaches in network intrusion detection follow a signature-based approach, however the use of anomaly detection approaches and machine learning techniques have been studied heavily for the past twenty years. The continuous change in the way attacks are appearing, the volume of attacks, as well as the improvements in the big data analytics space, make machine learning approaches more alluringthan ever. The intention of this paper is to show that using machine learning in the intrusion detection domain should be accompanied with an evaluation of its robustness against adversaries. Several adversarial techniques have emerged lately from the deep learning research, largely in the area of image classification. These techniques are based on the idea of introducing small changes in the original input data in order to make a machine learning model to misclassify it. This paper follows a big data analytics methodology and explores adversarial machine learning techniques that have emerged from the deep learning domain, against machine learning classifiers used for network intrusion detection. We look at several well-known classifiers and study their performance under attack over several metrics, such as accuracy, F1-score and receiver operating characteristic. The approach used assumes no knowledge of the original classifier and examines both general and targeted misclassification. The results showthat using relatively simple methods for generating adversarial samples it is possible to lower the detection accuracy of intrusion detection classifiers as much as 27%. Performance degradation is achieved using a methodology that is simpler than previous approaches and it requires only 6.14% change between the original and the adversarial sample, making it a candidate for a practical adversarial approach. 

  • 35.
    Rizk, Aya
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Bergvall-Kåreborn, Birgitta
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Digital Service Innovation Enabled by Big Data Analytics: A Review and the Way Forward2017In: Proceedings of the 50th Hawaii International Conference on System Sciences 2017, University of Hawai'i at Manoa , 2017, p. 1247-1256Conference paper (Refereed)
    Abstract [en]

    Service innovation is attracting attention with the expanding service industries and economies. Accompanied by major developments in ICT and sensory and digital technologies, the interest in digital service innovation (DSI), both from academia and industry, is increasing. Digitization and the accompanying technological advancements are leading to phenomena that call for extensive research in relation to service innovation; one of which is big data analytics (BDA). In this paper, we review the DSI literature and explore how BDA can contribute along the different dimensions of DSI. The ex post literature suffers from the lack of such studies. Accordingly, we suggest a research agenda for BDA-enabled DSI, motivated by emerging research gaps, as well as opportunities and guiding research questions. It is expected that such research agenda will contribute to shape an ex ante research efforts in an attempt to advance the state-of-the-art in BDA-enabled DSI.

  • 36.
    Rizk, Aya
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Bergvall-Kåreborn, Birgitta
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Towards A Taxonomy of Data-driven Digital Services2018Conference paper (Refereed)
    Abstract [en]

    Digitization is transforming every domain nowadays, leading to a growing body of knowledge on digital service innovation. Coupled with the generation and collection of big data, data-driven digital services are becoming of great importance to business, economy and society. This paper aims to classify the different types of data-driven digital services, as a first step to understand their characteristics and dynamics. A taxonomy is developed and the emerging characteristics include data acquisition mechanisms, data exploitation, insights utilization, and service interaction characteristics. The examined services fall into 15 distinct types and are further clustered into 3 classes of types: distributed analytics intermediaries, visual data-driven services, and analytics-embedded services. Such contribution enables service designers and providers to understand the key aspects in utilizing data and analytics in the design and delivery of their services. The taxonomy is set out to shape the direction and scope of scholarly discourse around digital service innovation research and practice.

  • 37.
    Rizk, Aya
    et al.
    Teradata Egypt.
    Elragal, Ahmed
    German University in Cairo.
    Trajectory data analysis in support of understanding movement patterns: A data mining approach2012In: 18th Americas conference on information systems 2012: Seattle, Washington, USA, 9-12 August 2012., Curran Associates, Inc., 2012, p. 1695-1702Conference paper (Refereed)
    Abstract [en]

    Recent developments in wireless technology, mobility and networking infrastructures increased the amounts of data being captured every second. Data captured from the digital traces of moving objects and devices is called trajectory data. With the increasing volume of spatiotemporal trajectories, constructive and meaningful knowledge needs to be extracted. In this paper, a conceptual framework is proposed to apply data mining techniques on trajectories and semantically enrich the extracted patterns. A design science research approach is followed, where the framework is tested and evaluated using a prototypical instantiation, built to support decisions in the context of the Egyptian tourism industry. By applying association rule mining, the revealed time-stamped frequently visited regions of interest (ROI) patterns show that specific semantic annotations are required at early stages in the process and on lower levels of detail, refuting the presumption of cross-application usable patterns

  • 38.
    Schelén, Olov
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Haddara, Moutaz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A roadmap for big-data research and education2015Report (Other academic)
    Abstract [en]

    The research area known as big data is characterized by the 3 V’s, which are vol- ume; variety; and velocity. Recently, also veracity and value have been associated with big data and that adds up to the 5 V’s. Big data related information systems (IS) are typically highly distributed and scalable in order to handle the huge datasets in organizations. Data processing in such systems includes creation, retrieval, storage, analysis, presentation, visualization, and any other activity that is typical for IS sys- tems. Big data is often associated with business analytics, cloud services, or industrial systems.This document presents a brief overview of the state of the art in selected topics of big data research, with the purpose of providing input to a roadmap for research and education at Lule ̊a University of Technology (LTU). The selection of topics is based on assessments of where LTU can make an impact based on current and anticipated research strengths and position with industry (e.g., process industry, data centers and cloud application providers). Topics include distributed systems, mobility, Internet of Things, and advanced analytics.

1 - 38 of 38
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