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Elragal, A. & Hassanien, H.-D. E. (2019). Augmenting Advanced Analytics into Enterprise Systems: A Focus on Post-Implementation Activities. Systems, 7(2), Article ID 31.
Open this publication in new window or tab >>Augmenting Advanced Analytics into Enterprise Systems: A Focus on Post-Implementation Activities
2019 (English)In: Systems, E-ISSN 2079-8954, Vol. 7, no 2, article id 31Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
enterprise system, advanced analytics, post-live activities, post-implementation
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-74850 (URN)10.3390/systems7020031 (DOI)000474932700013 ()
Note

Validerad;2019;Nivå 2;2019-06-24 (johcin)

Available from: 2019-06-21 Created: 2019-06-21 Last updated: 2019-08-16Bibliographically approved
Elragal, A. & Haddara, M. (2019). Design Science Research: Evaluation in the Lens of Big Data Analytics. Systems, 7(2), Article ID 27.
Open this publication in new window or tab >>Design Science Research: Evaluation in the Lens of Big Data Analytics
2019 (English)In: Systems, ISSN 2079-8954, Vol. 7, no 2, article id 27Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
DSR, big data analytics, evaluation
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-74123 (URN)10.3390/systems7020027 (DOI)000474932700009 ()
Note

Validerad;2019;Nivå 2;2019-06-10 (oliekm)

Available from: 2019-06-01 Created: 2019-06-01 Last updated: 2019-08-16Bibliographically approved
Rizk, A., Bergvall-Kåreborn, B. & Elragal, A. (2018). Towards A Taxonomy of Data-driven Digital Services. In: : . Paper presented at 51st Hawaii International Conference on System Sciences, (HICSS), Waikoloa, United States, 3–6 January 2018 (pp. 1076-1085). University of Hawai'i at Manoa
Open this publication in new window or tab >>Towards A Taxonomy of Data-driven Digital Services
2018 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
University of Hawai'i at Manoa, 2018
Keywords
Innovation, Digital services, Data-driven services, Big data, Taxonomy
National Category
Other Social Sciences not elsewhere specified Information Systems
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-66471 (URN)10.24251/HICSS.2018.135 (DOI)978-0-9981331-1-9 (ISBN)
Conference
51st Hawaii International Conference on System Sciences, (HICSS), Waikoloa, United States, 3–6 January 2018
Projects
OrganiCity
Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2019-01-18Bibliographically approved
Osman, A. M. S., Elragal, A. & Bergvall-Kåreborn, B. (2017). Big Data Analytics and Smart Cities: A Loose or Tight Couple?. In: Kommers P.,Rodrigues L. (Ed.), Proceedings of the International Conference on ICT, Society and Human Beings 2017: Part of the Multi Conference on Computer Science and Information Systems 2017. Paper presented at 10th International Conference on Connected Smart Cities 2017 (CSC 2017), Lisbon, 20-22 July 2017 (pp. 157-168). IADIS
Open this publication in new window or tab >>Big Data Analytics and Smart Cities: A Loose or Tight Couple?
2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
IADIS, 2017
Keywords
Big data, Big data analytics Frameworks, Smart cities
National Category
Computer Sciences Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-63718 (URN)2-s2.0-85040188916 (Scopus ID)9789898533678 (ISBN)
Conference
10th International Conference on Connected Smart Cities 2017 (CSC 2017), Lisbon, 20-22 July 2017
Available from: 2017-06-05 Created: 2017-06-05 Last updated: 2018-01-19Bibliographically approved
Hassan, A. & Elragal, A. (2017). Big Data Visualization Tool: a Best-Practice Selection Model. In: Powell P.,Rodrigues L.,Nunes M.B.,Isaias P. (Ed.), IADIS Information Systems Conference (IS 2017): . Paper presented at 10th IADIS International Conference on Information Systems 2017, Budapest, 10-12 April 2017 (pp. 59-68). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Big Data Visualization Tool: a Best-Practice Selection Model
2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Big data analytics, big data visualization tools, selection procedure, Delphi method
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-61861 (URN)2-s2.0-85032360067 (Scopus ID)
Conference
10th IADIS International Conference on Information Systems 2017, Budapest, 10-12 April 2017
Available from: 2017-02-07 Created: 2017-02-07 Last updated: 2017-11-24Bibliographically approved
Rizk, A., Bergvall-Kåreborn, B. & Elragal, A. (2017). Digital Service Innovation Enabled by Big Data Analytics: A Review and the Way Forward. In: Proceedings of the 50th Hawaii International Conference on System Sciences 2017: . Paper presented at Hawaii International Conference on System Sciences (HICSS), Jan 4-7 2017 (pp. 1247-1256). University of Hawai'i at Manoa
Open this publication in new window or tab >>Digital Service Innovation Enabled by Big Data Analytics: A Review and the Way Forward
2017 (English)In: Proceedings of the 50th Hawaii International Conference on System Sciences 2017, University of Hawai'i at Manoa , 2017, p. 1247-1256Conference paper, Published 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.

Place, publisher, year, edition, pages
University of Hawai'i at Manoa, 2017
Keywords
Big Data, Analytics, Service Innovation, Digital Services, Review
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-61095 (URN)10.24251/HICSS.2017.149 (DOI)978-0-9981331-0-2 (ISBN)
Conference
Hawaii International Conference on System Sciences (HICSS), Jan 4-7 2017
Available from: 2016-12-15 Created: 2016-12-15 Last updated: 2019-01-17Bibliographically approved
Elragal, A. & Päivärinta, T. (2017). Opening Digital Archives and Collections With Emerging Data Analytics Technology: A Research Agenda. Tidsskriftet Arkiv, 8(1)
Open this publication in new window or tab >>Opening Digital Archives and Collections With Emerging Data Analytics Technology: A Research Agenda
2017 (English)In: Tidsskriftet Arkiv, ISSN 1891-8107, E-ISSN 1891-8107, Vol. 8, no 1Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
ABM-media AS, 2017
Keywords
Digital Preservation; Digital Archives; Big Data Analytics; Text Mining; Research Agenda
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-61846 (URN)
Note

Validerad; 2017; Nivå 1; 2017-02-15 (andbra)

Available from: 2017-02-07 Created: 2017-02-07 Last updated: 2018-11-20Bibliographically approved
Elragal, A. & Klischewski, R. (2017). Theory-driven or Process-driven Prediction?: Epistemological Challenges of Big Data Analytics. Journal of Big Data, 4(1), Article ID 19.
Open this publication in new window or tab >>Theory-driven or Process-driven Prediction?: Epistemological Challenges of Big Data Analytics
2017 (English)In: Journal of Big Data, E-ISSN 2196-1115, Vol. 4, no 1, article id 19Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Big Data Analytics, epistemological challenges, information systems theories, predictive research
National Category
Computer Systems Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-63813 (URN)10.1186/s40537-017-0079-2 (DOI)2-s2.0-85021445287 (Scopus ID)
Note

Validerad;2017;Nivå 1;2017-12-12 (andbra)

Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-12-12Bibliographically approved
Elgendy, N. & Elragal, A. (2016). Big Data Analytics in Support of the Decision Making Process. Paper presented at International Conference on ENTERprise Information Systems/International Conference on Project MANagement/International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN / HCist 2016. Procedia Computer Science, 100, 1071-1084
Open this publication in new window or tab >>Big Data Analytics in Support of the Decision Making Process
2016 (English)In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 100, p. 1071-1084Article in journal (Refereed) Published
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.

National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-59520 (URN)10.1016/j.procs.2016.09.251 (DOI)000392695900136 ()2-s2.0-85006874575 (Scopus ID)
Conference
International Conference on ENTERprise Information Systems/International Conference on Project MANagement/International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN / HCist 2016
Note

Konferensartikel i tidskrift

Available from: 2016-10-05 Created: 2016-10-05 Last updated: 2018-07-10Bibliographically approved
Schelén, O., Elragal, A. & Haddara, M. (2015). A roadmap for big-data research and education (ed.). Paper presented at . Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>A roadmap for big-data research and education
2015 (English)Report (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.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2015. p. 10
Series
Technical report / Luleå University of Technology, ISSN 1402-1536
National Category
Media and Communication Technology Information Systems, Social aspects
Research subject
Mobile and Pervasive Computing; Information systems; Intelligent industrial processes (AERI); Enabling ICT (AERI)
Identifiers
urn:nbn:se:ltu:diva-24316 (URN)a77593be-a71c-4c7c-ae73-0cc30c31e50b (Local ID)978-91-7583-275-3 (ISBN)a77593be-a71c-4c7c-ae73-0cc30c31e50b (Archive number)a77593be-a71c-4c7c-ae73-0cc30c31e50b (OAI)
Note
Godkänd; 2015; 20150325 (olov)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-05-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4250-4752

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