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Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems
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Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (20 September 2018) | Viewed by 25288

Special Issue Editors

Prof. Dr. Slimane Hammoudi

E-Mail Website
Guest Editor
Department of Computer Science, ESEO, School of Engineering, Angers, France
Interests: model driven engineering; service computing; personalization and recommandation; smart environment
Prof. Michal Smialek

E-Mail Website
Guest Editor
Warsaw University of Technology, Poland
Interests: software engineering; model-driven engineering; requirements engineering; software reuse; software development tools

Special Issue Information

Dear Colleagues,

We invite contributions that cover all aspects of advances and business applications of information systems, especially in the context of enterprises. We want to specially focus on real world applications; therefore, authors should highlight the benefits of Information Technology for industry and services. This prominently includes ideas on how to solve business problems using IT. Papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged. Papers describing original work are invited in any of the six areas listed below:

1. Databases and Information Systems Integration
2. Artificial Intelligence and Decision Support Systems
3. Information Systems Analysis and Specification
4. Software Agents and Internet Computing
5. Human–Computer Interactions
6. Enterprise Architecture

This Special Issue will contain extended versions of selected papers presented at the ICEIS 2018 Conference (http://www.iceis.org/?y=2018) held in Funchal, Madeira, 21–24 March 2018.

Prof. Dr. Slimane Hammoudi
Prof. Michal Smialek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Databases
  • Information Systems Integration
  • Artificial Intelligence
  • Decision Support Systems
  • Information Systems Analysis and Specification
  • Software Agents
  • Internet Computing
  • Human-Computer Interaction
  • Enterprise Architecture

Published Papers (5 papers)

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Research

36 pages, 1217 KiB  
Article
Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs
by Christian Sturm, Myriel Fichtner and Stefan Schönig
Information 2019, 10(1), 29; https://doi.org/10.3390/info10010029 - 15 Jan 2019
Cited by 3 | Viewed by 3672
Abstract
Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It [...] Read more.
Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It has been shown that the performance of process mining algorithms, particularly when considering the multi-perspective declarative process models, are not satisfactory. State-of-the-art mining tools do not support multi-perspective declarative models at this moment. We address this open research problem by proposing an efficient mining framework that leverages the latest big data analysis technology and builds upon the distributed processing method MapReduce. The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints. Additionally, we provide a novel implementation concept allowing an easy introduction and discovery of customised constraint templates. We evaluated the mining performance and effectiveness of the presented approach on several real-life event logs. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable time. Full article
(This article belongs to the Special Issue Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems)
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14 pages, 486 KiB  
Article
Identifying a Medical Department Based on Unstructured Data: A Big Data Application in Healthcare
by Veena Bansal, Abhishek Poddar and R. Ghosh-Roy
Information 2019, 10(1), 25; https://doi.org/10.3390/info10010025 - 11 Jan 2019
Cited by 3 | Viewed by 6116
Abstract
Health is an individual’s most precious asset and healthcare is one of the vehicles for preserving it. The Indian government’s spend on healthcare system is relatively low (1.2% of GDP). Consequently, Secondary and Tertiary government healthcare centers in India (that are presumed to [...] Read more.
Health is an individual’s most precious asset and healthcare is one of the vehicles for preserving it. The Indian government’s spend on healthcare system is relatively low (1.2% of GDP). Consequently, Secondary and Tertiary government healthcare centers in India (that are presumed to be of above average ratings) are always crowded. In Tertiary healthcare centers, like the All India Institute of Medical Science (AIIMS), patients are often unable to articulate their problems correctly to the healthcare center’s reception staff, so that these patients to be directed to the correct healthcare department. In this paper, we propose a system that will scan prescriptions, referral letters and medical diagnostic reports of a patient, process the input using OCR (Optical Character Recognition) engines, coupled with image processing tools, to direct the patient to the most relevant department. We have implemented and tested parts of this system wherein a patient enters his symptoms and/or provisional diagnosis; the system suggests a department based on this user input. Our system suggests the correct department 70.19% of the time. On further investigation, we found that one particular department of the hospital was over-represented. We eliminated the department from the data and performance of the system improved to 92.7%. Our system presently makes its suggestions using random forest algorithm that has been trained using two information repositories-symptoms and disease data, functional description of each medical department. It is our informed assumption that, once we have incorporated medicine information and diagnostics imaging data to train the system; and the complete medical history of the patient, performance of the system will improve further. Full article
(This article belongs to the Special Issue Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems)
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20 pages, 3221 KiB  
Article
Modeling and Visualizing Smart City Mobility Business Ecosystems: Insights from a Case Study
by Anne Faber, Sven-Volker Rehm, Adrian Hernandez-Mendez and Florian Matthes
Information 2018, 9(11), 270; https://doi.org/10.3390/info9110270 - 29 Oct 2018
Cited by 25 | Viewed by 5757
Abstract
Smart mobility is a central issue in the recent discourse about urban development policy towards smart cities. The design of innovative and sustainable mobility infrastructures as well as public policies require cooperation and innovations between various stakeholders—businesses as well as policy makers—of the [...] Read more.
Smart mobility is a central issue in the recent discourse about urban development policy towards smart cities. The design of innovative and sustainable mobility infrastructures as well as public policies require cooperation and innovations between various stakeholders—businesses as well as policy makers—of the business ecosystems that emerge around smart city initiatives. This poses a challenge for deploying instruments and approaches for the proactive management of such business ecosystems. In this article, we report on findings from a smart city initiative we have used as a case study to inform the development, implementation, and prototypical deployment of a visual analytic system (VAS). As results of our design science research we present an agile framework to collaboratively collect, aggregate and map data about the ecosystem. The VAS and the agile framework are intended to inform and stimulate knowledge flows between ecosystem stakeholders in order to reflect on viable business and policy strategies. Agile processes and roles to collaboratively manage and adapt business ecosystem models and visualizations are defined. We further introduce basic categories for identifying, assessing and selecting Internet data sources that provide the data for ecosystem models and we detail the ecosystem data and view models developed in our case study. Our model represents a first explication of categories for visualizing business ecosystem models in a smart city mobility context. Full article
(This article belongs to the Special Issue Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems)
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24 pages, 2071 KiB  
Article
Conceptualising and Modelling E-Recruitment Process for Enterprises through a Problem Oriented Approach
by Saleh Alamro, Huseyin Dogan, Deniz Cetinkaya, Nan Jiang and Keith Phalp
Information 2018, 9(11), 269; https://doi.org/10.3390/info9110269 - 29 Oct 2018
Cited by 5 | Viewed by 6615
Abstract
Internet-led labour market has become so competitive it is forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research was motivated by the results of a [...] Read more.
Internet-led labour market has become so competitive it is forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research was motivated by the results of a failed e-recruitment project conducted in military domain which was used as a case study. After reviewing the various challenges faced in that project through a number of related research domains, this research focused on two major problems: (1) the difficulty of scoping, representing, and systematically transforming recruitment problem knowledge towards e-recruitment solution specification; and (2) the difficulty of documenting e-recruitment best practices for reuse purposes in an enterprise recruitment environment. In this paper, a Problem-Oriented Conceptual Model (POCM) with a complementary Ontology for Recruitment Problem Definition (Onto-RPD) is proposed to contextualise the various recruitment problem viewpoints from an enterprise perspective, and to elaborate those problem viewpoints towards a comprehensive recruitment problem definition. POCM and Onto-RPD are developed incrementally using action-research conducted on three real case studies: (1) Secureland Army Enlistment; (2) British Army Regular Enlistment; and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). They are later evaluated in a focus group study against a set of criteria. The study shows that POCM and Onto-RPD provide a strong foundation for representing and understanding the e-recruitment problems from different perspectives. Full article
(This article belongs to the Special Issue Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems)
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18 pages, 1026 KiB  
Article
ImplicPBDD: A New Approach to Extract Proper Implications Set from High-Dimension Formal Contexts Using a Binary Decision Diagram †
by Phillip G. Santos, Pedro Henrique B. Ruas, Julio C. V. Neves, Paula R. Silva, Sérgio M. Dias, Luis E. Zárate and Mark A. J. Song
Information 2018, 9(11), 266; https://doi.org/10.3390/info9110266 - 25 Oct 2018
Cited by 2 | Viewed by 2621
Abstract
Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is [...] Read more.
Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the PropIm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance—up to 80% faster—than its original algorithm, regardless of the number of attributes, objects and densities. Full article
(This article belongs to the Special Issue Selected Papers from ICEIS 2018: Advances in Enterprise Information Systems)
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