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Fwd: [AISWorld] Special Issue Call for Papers from Journal of Data, Information, and Management: Big Data Analytics and Artificial Intelligence to Combat a Pandemic
by Gustaf Neumann 23 Aug '20

23 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] Special Issue Call for Papers from Journal of Data, Information, and Management: Big Data Analytics and Artificial Intelligence to Combat a Pandemic Date: Fri, 21 Aug 2020 01:43:04 +0000 From: Zadeh, Amir <amir.zadeh(a)okstate.edu> To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> Journal of Data, Information, and Management Call for Paper for Special Issue on Big Data Analytics and Artificial Intelligence to Combat a Pandemic The pandemic, COVID-19 has become a global public health pandemics, causing hundreds of thousands of deaths and trillions of dollars of economic damage. Scientists, researchers, doctors and healthcare experts around the world are studying this pandemic and its impacts on individuals, organizations and societies. While there is no cure yet for the virus, contact tracing has become an important part of the response to the covid-19 pandemic. This has led to calls for new technologies such as machine learning and artificial intelligence, capable of detecting, tracking, and isolating Covid-19 cases, to slow the spread of the coronavirus. This also includes performing predictive analytics on relational, temporal and/or spatial data (medical encounter data, hospital admission data, social media data, mobile apps data and others) and use the insights from it to understand and monitor the epidemic. Further, the impact of Covid-19 is not limited just to the healthcare sector as all other sect ors of any economy of the world are affected as well. Therefore, due to the Covid-19 pandemic, the need to understand different things in a new way has also created pressure to develop analytical tools. In particular, research has been done on how the virus spreads among the population and what health problems are caused by viral infections, as well as how different treatment measures affect the disease. Recently, however, other problems caused by the Covid-19 virus, such as economic and social problems, have also arisen. These various problems are constantly accumulating a considerable amount of data, which can be either a well-defined or fragmented news stream. In this special issue of the Journal of Data, Information and Management on the theme of "Big Data Analytics and Artificial Intelligence (including machine learning) to combat a Pandemic " like Covid-19 is intended to obtain insights and viewpoints from scholars and practitioners on the use of new technologies such as artificial intelligence and machine learning for the prevention or reduction of the negative impact of a pandemic. We seek for articles that apply artificial intelligence, machine learning and data science concepts and techniques to study the pandemic and its effects on individuals, organizations or the supply chain at large. Topics of interests include, but are not limited to: * The behavioral, temporal, societal, and organizational aspects of the pandemic * Predictive modeling of a pandemic's spread based on hospital data * Location-based human mobility data (e.g. social media, mobile) to track a pandemic like COVID-19 * Use of AI and machine learning enabled devices to detect a pandemic like COVID-19 * Analysis of disease transmissions and interventions * Data analysis of the impact of various (non)government intervention policies (e.g. stay-at-home, mask mandate) on the pandemic transmission * How enterprise systems (i.e. ERP, SCM, CRM) complement, the ever increasing need to work from home. * Urban logistics * Sustainability issues * Supply Chain Flexibility * Food Supply Chains * Distant work (productivity and social impact) * Innovation in the supply chain * Globalization of supply chains * Supply Chain Risk * Innovative strategies to limit risk of epidemic disease propagation * Simulation of outbreak events * Big data-driven health system response to epidemic outbreaks on operations risk identification * AI-based epidemic network analysis * MCDM models in field of healthcare risk management * IoT application in healthcare * Humanitarian logistics dealing with uncertainties * Other topics related this special issue Important Dates: September 15, 2020: Submission deadline for paper submission October 21, 2020: First round of reviews to the authors November 22, 2020: Submission of the revised papers December 15, 2020: Notification of the acceptance decision March 1, 2021: Publication of the special issue Guest Editor-in-Chief: Jatinder N. D. Gupta, University of Alabama in Huntsville, guptaj(a)uah.edu<mailto:guptaj@uah.edu> Guest Editors: Markku Kuula, markku.kuula(a)aalto.fi<mailto:markku.kuula@aalto.fi> Yanfei Lan, Tianjin University, lanyf(a)tju.edu.cn<mailto:lanyf@tju.edu.cn> Amir Zadeh, Wright State University, amir.zadeh(a)wright.edu<mailto:amir.zadeh@wright.edu> For guidelines and instructions for submitting a paper, click here https://www.springer.com/journal/42488. For questions about this special issue, please contact the guest editors. _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [WI] CfP WI2021-Track „Datenmanagement und Datenökosysteme“
by Gustaf Neumann 23 Aug '20

23 Aug '20

-------- Forwarded Message -------- Subject: [WI] CfP WI2021-Track „Datenmanagement und Datenökosysteme“ Date: Fri, 21 Aug 2020 07:00:43 +0000 From: Christine Legner <christine.legner(a)unil.ch> Reply-To: Christine Legner <christine.legner(a)unil.ch> To: wi(a)lists.kit.edu <wi(a)lists.kit.edu> CC: Otto, Boris <Boris.Otto(a)isst.fraunhofer.de>, Stelzer Dirk Univ.-Prof. Dr. TU Ilmenau <dirk.stelzer(a)tu-ilmenau.de> /Bitte entschuldigen Sie eventuelle Mehrfachzustellungen/ ** *Call for Papers für denWI2021-Track„**Datenmanagement und Datenökosysteme“* 16. Internationale Tagung Wirtschaftsinformatik (WI 2021), 09.-11. März 2021, Universität Duisburg-Essen https://wi2021.de/datenmanagement-und-datenoekosysteme/ ** *Einreichungsfrist : 28.8.2020 (verlängert)* ** *Track Chairs* Christine Legner, HEC - Universität Lausanne Boris Otto, Fraunhofer-Institut für Software- und Systemtechnik ISST, TU Dortmund Dirk Stelzer, TU Ilmenau ** *Beschreibung* Die Wertschöpfung von Unternehmen ist schon heute entscheidend von der Qualität der Daten und der Effizienz ihrer Planung, Verarbeitung, Steuerung und Kontrolle abhängig. Ausgelöst durch die Digitalisierung und das Internet der Dinge werden Daten darüber hinaus zum Kernbestandteil von Geschäftsmodellen oder von neuen Dienstleistungen („Data-as-a-Service“). Daten werden zunehmend direkt monetarisiert, und es entstehen auf diese Weise neuartige Datenökosysteme. Die kürzlich aufgelegten Datenstrategien der deutschen Bundesregierung und der Europäischen Kommission unterstreichen die Bedeutung, die Daten für die zukünftige Wettbewerbsfähigkeit von Unternehmen sowie gesellschaftliche Prosperität beigemessen wird. Datenmanagement umfasst sämtliche Aktivitäten, die zur Bewirtschaftung von Daten in Unternehmen und Wertschöpfungsnetzwerken notwendig sind. Dazu gehören insbesondere organisatorische Aufgaben und Daten-Governance sowie Daten-/Informationsmodellierung und -architekturen. Durch die Digitalisierung und das Internet der Dinge ergeben sich vielfältige Forschungsbedarfe: Die Ansätze des Datenmanagements sind auf neue Datenklassen, insbesondere unstrukturierte Daten, und Datenspeicher für heterogene Daten zu erweitern. Darüber hinaus erfordern Datenökosysteme das Teilen von Daten bei gleichzeitiger Gewährleistung der Datensouveränität des Datengebers sowie der Vertrauensschutz des Datennehmers. Forschungsbedarf besteht zudem bei der institutionellen, organisatorischen und technischen Gestaltung von Dateninfrastrukturen, die es erlauben interne mit externen und offenen Datenbestände („Open Data“) zu verknüpfen. *Themenfelder * Wir ermutigen Autorinnen und Autoren, sowohl theoriebasierte als auch anwendungsorientierte Beiträge, die sich mit dem Datenmanagement und Datenökosystemen beschäftigen, in diesem Track einzureichen. Mögliche Themen umfassen: * Management von Daten und Datenqualität * Verfahren zur Definition, Bewertung und Verbesserung der Datenqualität * Data Sharing und multilaterale Formen des Datenmanagements * Governance von Datenplattformen * Datenmonetarisierung, Datenmarktplätze und Datenwert * Theorien zur Wertschöpfung von Daten * Datenschutz, Datensicherheit und Datensouveränität * Nutzung und Bewirtschaftung von externen Daten * Wiederverwenden und Teilen von Daten in Ökosystemen * Rollen und Konfiguration von Data Supply Chains / Data Value Chains * Architekturen und -standards für Datenökosysteme * Interoperabilität und Portabilität von Daten in der Cloud *Associate Editors* Sören Auer, Leibniz Universität Hannover Markus Eurich, Universität Lausanne Jörg Leukel, Universität Hohenheim Matthias Jarke, RWTH Allan Hanbury, TU Wien Matthias Klier, Universität Ulm Wolfgang Maaß, DFKI, Universität des Saarlandes Thomas Schäffer, Hochschule Heilbronn Alexander Schiller, Universität Regensburg Kristin Weber, Hochschule Würzburg-Schweinfurt Anette Weisbecker, Fraunhofer IAO, Universität Stuttgart Manuel Wiesche, TU Dortmund Axel Winkelmann, Universität Würzburg Informationen zum Track: https://wi2021.de/datenmanagement-und-datenoekosysteme/ -- Mailing-Liste: wi(a)lists.kit.edu Administrator: wi-request(a)lists.kit.edu Konfiguration: https://www.lists.kit.edu/wws/info/wi

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Fwd: [AISWorld] Call for Chapters: Leveraging Artificial Intelligence in Global Epidemics
by Gustaf Neumann 20 Aug '20

20 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] Call for Chapters: Leveraging Artificial Intelligence in Global Epidemics Date: Thu, 20 Aug 2020 15:52:06 +0200 From: Sven Groppe <groppe(a)ifis.uni-luebeck.de> To: aisworld(a)lists.aisnet.org Leveraging Artificial Intelligence in Global Epidemics Overview of the Book: In the fight against epidemics, medical staff is on the front line; but behind the lines the battle is fought by the government officials, researchers, and data scientists. Artificial Intelligence (AI) has been helping nations with computer modeling and simulation for predictions about e.g. the overall economic situation, tax incomes and population development. In the same manner, AI can prepare the government for emergency situations by backing the medical science. AI plays a key and cutting-edge role in the preparedness for and dealing with the outbreak situation of global epidemics. It can help researchers analyze global data about known viruses to predict the knows-hows of the next pandemic and the impact it will have. Not only for prediction, but also AI plays an increasingly important role in assessing a country’s readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations. While there exist books about AI in various application domains, what is currently lacking is a book that provides a comprehensive coverage of disease outbreak and specifically how AI can help fight with epidemics. To fill this gap, this book contains chapters describing the role AI plays in various stages of a disease outbreak, with COVID-19 as a case study. Invitation to Contribute a Chapter: The individual chapters should provide a comprehensive overview of the chosen topic covering advances in the area and should be tutorial in nature and presentation, to appeal to a broad range of readers who may NOT be researchers on the topic. Hence, they shouldn’t be like a research paper. You are hereby invited to submit first, a 2-3 page extended summary including (a) an abstract, (b) an outline / TOC / list of headings and sub-headings along with a summary of each heading, (c) short biographies of the authors, and (d) a statement why you are experts for the proposed chapter. Following a review of your summary, we’ll offer our feedback and suggestions for improvement and better synergy with rest of the chapters and cohesive coverage. Chapter Submission Link: https://easychair.orgconferences/?conf=aige2021 Structure of the Book: The book is planned to have 11-12 chapters divided into two sections. Part I consists of 3 chapters providing a holistic overview of global epidemics, a motivation for the book and challenges. Part II consists of emerging technologies that help fight against epidemics. Part II has 6 chapters as listed below. This makes 9 chapters. The rest 2-3 chapters are to be added dynamically based on the availability of authors (These chapters may be on The Use of Social Media for Tracking Public Behavior, Does AI help in Genome Sequencing, AI-assisted Testing, Computational Drug Repurposing, or any other trending and suitable topic in line with the title and the theme of the book). Part I: Controlling Epidemics: A Perspective This part provides an overview and study of Global Epidemic situations. It starts with assessing the countries' readiness for coping with epidemics, goes through the challenges in battling with epidemic, and then closes with a survey of existing approaches, techniques and tools available or developed by academicians or industry professionals for controlling the epidemics. 1 Setting the Scene (A Chapter by the Editors) This chapter provides an overview and summary of the complete book as a whole It acts as glue between the chapters written by different authors and brings in synergy to all the chapters in both the parts of the book. In effect, it sets the scene for the reader and acts as an introductory and a must read before proceeding for the further chapters. 2 An Overview of Global Epidemics and the Challenges Faced (a) Assessing Countries’ Readiness for Coping with Epidemics: There is a need of retrospective evaluation of each country’s readiness for coping with epidemics. An automated data collection followed by evaluation will help in knowing if the human kind is well-equipped to deal with it. (b) A Study related to Ebola, Corona viruses, Zika, influenza, Dengue, Chikungunya, Malaria like infectious diseases will be presented in this chapter. A discussion on the most fatal pandemics recorded in the history (like the Plague, Spanish Flu, HIV/AIDS, COVID-19) and their economic consequences. What is common in these? All these have jumped to humans after being originated in animals somehow. (c) Challenges in Battling with Epidemics (d) … … 3 A Survey of Existing Approaches and Tools to fight against epidemic and the lessons learnt (a) Survey of tools/techniques/methodologies/software already available at the time of writing of this book to predict/detect/recommend actions during an epidemic situation. (b) Lessons learnt by every epidemic that has struck this world (including COVID-19) Part II: Emerging Technologies Fight Against Epidemics Having gone through the background of controlling global epidemic situation, this part of the book covers the emerging techniques to fight against epidemic. A number of digital technologies……………This book focuses on …”Artificial Intelligence”…. At the time of writing this book, COVID-19 pandemic was caused by coronavirus and was widespread with deadly results. The case study of COVID-19 has been presented to better exploit the study presented in each chapter. … 4 AI Technologies specialized to the need in the fight against epidemic All current technologies under the umbrella of AI or surrounding AI should be covered like Data Science, Big Data, Machine Learning, Semantic Technologies, Data Analytics, cyber security. 5 Does AI help in Forecasting? This chapter will speak about WHO statistics on pathogens. AI can help predicting everything about spillovers, hence allowing the governments to plan ahead. AI can predict what, when, why, and where of the epidemic. We define AI and discuss the various machine learning models to predict the same. Case Study of Covid-19 for Predictions will be presented. 6 AI and Detection / Improved Diagnosis In case of outbreak of a disease; after detection, we need to publicize the threat. AI makes possible quick detection in order to enable possible vaccination and treatment; and alerts for the public. Diagnosis and Monitoring of cases is of paramount importance. Case Study of Covid-19 for Early Detection will be presented. 7 Generating Recommendations A global, AI-enabled data system can provide advices and issue warnings in real time. Help manage socio-economic impacts. 8 Role of AI in Contact Tracing The architecture of so called “Corona apps” and their backends is described in this chapter. The role of AI is clarified for these approaches and also other aspects like privacy and security. Furthermore, other approaches to contact tracing are introduced in this chapter. 9 Situation Awareness In any disaster, it is essential that the citizens get the correct information and organizations get the correct data. This chapter describes AI approaches to detect fake news or scams. …. 10-12 Open Topics dynamically picked by the authors Any other trending and suitable topic in line with the title and the theme of the book can be incorporated. Editors: - Le Gruenwald, The University of Oklahoma, USA, ggruenwald(a)ou.edu - Sarika Jain, National Institute of Technology, Kurukshetra, Haryana, India, jasarika(a)nitkkr.ac.in. - Sven Groppe, University of Lübeck, Germany, groppe(a)ifis.uni-luebeck.de Deadlines: Extended Summary: Sep 30, 2020 Notification of Proposal Approval: Oct 20, 2020 Full Chapter Submission: Jan 10, 2020 _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] Issue 24, Complex Systems Inf. and Mod. Q. , CFP
by Gustaf Neumann 20 Aug '20

20 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] Issue 24, Complex Systems Inf. and Mod. Q. , CFP Date: Thu, 20 Aug 2020 14:10:07 +0000 From: Mārīte Kirikova <Marite.Kirikova(a)rtu.lv> To: ISWorld <aisworld(a)lists.aisnet.org> Complex Systems Informatics and Modeling Quarterly (CSIMQ) Issue 24 Call for Papers Submission deadline: September 15, 2020 Submission site: https://www.easychair.org/conferences/?conf=csimqfrom2014 CSIMQ is an Open Access journal that publishes peer-reviewed original research articles reporting on studies of the structure, algorithms, behavior, and interactions of natural, social, socio-technical, and artificial systems that store, process, access, and communicate information. It concerns data, information, and knowledge fundamentals, models, and management from the points of view of their discovery, engineering, and application. The articles shall address the topics of the following areas: Social, Socio-technical, Organization and Business Informatics, which includes but is not limited to · Social Systems Informatics · Socio-technical Systems Informatics · Organization Informatics · Business Informatics · Natural Systems Informatics · Artificial Systems Informatics High Complexity Information Systems Engineering, which includes but is not limited to · Intelligent Systems Engineering · Complex Systems Modeling and Software Development · Adaptive and Adaptable Information Systems Engineering · Networked Systems Engineering · Emergent and Virtual Systems Engineering Data and Knowledge Analytics, which includes but is not limited to · Data Intensive Computing · Semantic Data Analysis and Management · Big Data Analytics · Knowledge Analytics · Business Analytics · Social Networks Analytics Supercomputing, which includes but is not limited to · High Performance Computing · High Throughput Computing · Grid Computing · Cloud Computing · Supercomputing for Engineering Applications · Mathematical and Analytical Methods for Scientific Computing · Supercomputing for Social Systems The main principles of the journal are the following: · Usefulness and relevance of papers · Comfort for authors and readers · Flexibility in publishing The journal is free of charge, i.e. no fees are charged for manuscript processing and publishing and article download. Each submitted article (before the start of the peer review process) is first processed by plagiarism control system, and goes through the initial quality checking. ISSN: 2255-9922; indexed in DOAJ, DBLP, EBSCO, NSD; website: https://csimq-journals.rtu.lv/

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Fwd: [AISWorld] CFP: Prototypes trach @Pre-ICIS SIGDSA
by Gustaf Neumann 20 Aug '20

20 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] CFP: Prototypes trach @Pre-ICIS SIGDSA Date: Thu, 20 Aug 2020 14:34:51 +0000 From: Matta, Vic <matta(a)ohio.edu> To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> Special Interest Group for Decision Sciences and Analytics (SIGDSA) CALL FOR PROTOTYPE DEMONSTRATIONS & IDEA PAPERS Proposals are invited to a Prototypes track for submission to the Pre-ICIS 2020 SIGDSA Symposium. Submissions can be of one of the types listed below. 1. Prototype demonstrations proposals: These are showcasing innovative methodological developments or applications of artificial intelligence, machine learning, deep learning, text mining, and other data analytics/decision support techniques are encouraged. Prototypes should highlight aspects such as novelty, architecture, functioning, and ongoing/future work. This high-visibility category is different from paper presentation category and provides an opportunity to display a working artifact to an audience in an interactive manner. Prototype demonstrations will involve a poster display (printed or electronic) and a live demonstration of a prototype to the attendees. 2. Design science-oriented idea papers: Idea papers are supposed to identify innovative future lines of design-oriented BIA research related to the topics discussed above. The idea papers should include a convincing motivation, a brief overview of the relevant state of the art, the core idea, possible contributions to research and practice, and an outline of a possible course of the research project. Submission Topics These can be related to (but not limited to) business intelligence, big data analytics, data federation, data management and analytics in the cloud (incl. containerized analytics and function-as-a-service for analytics), data catalogs, process mining, ETL and business intelligence automation and security, streaming data management, social media analytics, machine and deep learning, embedded analytics (e.g., Internet of Things, or IoT), visual analytics, cognitive computing and artificial intelligence, model sharing, deployment and administration systems, analytics-oriented data generation for teaching, development, or testing, or platforms for community-based model development / analytics competitions or other emerging data management and analytic technologies. Submissions may also highlight novel applications in one or more business sectors, e.g., financial services, manufacturing, logistics, retail, pharmaceutical, healthcare, government, agribusiness, hospitality, IT services, education and entertainment, supply chain, etc. The contributions should ignite a joint brainstorming session on future trajectories for business intelligence and analytics (BIA) research. JOURNAL PUBLICATION OPPORTUNITY Selected papers from the symposium will be eligible for fast-track review for publication consideration in the https://www.tandfonline.com/loi/tjba20. It should be noted that the papers will go through a rigorous review process with no guarantee of acceptance. The papers will need to be extended to full papers and then submitted to the journal to be considered for publication. SUBMISSION GUIDELINES Prototype proposals and idea papers can be submitted in one of two formats 1. extended abstract: two to three single-spaced pages in length, or 2. complete papers: six to seven pages in length. In addition to the Title, Abstract, and Keywords, a prototype proposal should contain only one or two sections describing the prototype or core research idea to be showcased, highlighting the key features and novelty. Submission type should be mentioned in the submission as Prototype Demonstration or Design Research Idea Paper respectively. • Submission Format All submissions must conform to the template linked https://preicissigdsa2020.files.wordpress.com/2020/08/sigdsa2020_submission…. • Submission System All papers need to be submitted through the Easy Chair system linked https://easychair.org/conferences/?conf=sigdsa2020symposium. The submitting author will need to create an account to submit the paper. • Important Dates - Submission Deadline: September 10, 2020 - Other relevant dates: https://preicissigdsa2020.wordpress.com/important-dates. AWARDS • Best Prototype Award Prototypes/demonstrations (not the idea papers) will be evaluated at the conference by a panel of judges. One prototype will be selected for the “Best Prototype” award recognition at the symposium. • Best Reviewer Award One reviewer from the prototype track will be selected for the “Best Reviewer” award recognition at the symposium. PROTOTYPE TRACK CO-CHAIRS For questions regarding the submissions to the prototypes track, please contact us. Please use the subject line: “Pre-ICIS SIGDSA 2020 Question” • Vic Matta, (matta(a)ohio.edu<mailto:matta@ohio.edu>), Ohio University, USA. • Aleš Popovič, (ales.popovic(a)neoma-bs.fr<mailto:ales.popovic@neoma-bs.fr>), NEOMA Business School, France Best, Vic Matta (PhD) Associate Professor, Analytics & Information Systems, College of Business, Ohio University 71 S. Court Street, Copeland Hall #228, Athens, OH 45701 O: +1(740)593-0656, W: http://cob.ohio.edu/matta _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] Special Issue CfP: Business Analytics for the Management of Information Systems Development
by Gustaf Neumann 20 Aug '20

20 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] Special Issue CfP: Business Analytics for the Management of Information Systems Development Date: Thu, 20 Aug 2020 15:18:12 +0000 From: Dennehy, Denis <denis.dennehy(a)nuigalway.ie> To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> Special issue CfP from Information Technology & People Submission deadline: 30th September 2020 Special issue editors: Denis Dennehy, National University of Ireland Galway, Ireland Email: denis.dennehy(a)nuigalway.ie<mailto:denis.dennehy@nuigalway.ie> Ilias Pappas, University of Agder, Norway Email: ilias.pappas(a)uia.no<mailto:%20ilias.pappas@uia.no> Samuel Fossa Wamba, Toulouse Business School, France Email: s.fosso-wamba(a)tbs-education.fr<mailto:s.fosso-wamba@tbs-education.fr> Katina Michael, Arizona State University, USA Email: katina.michael(a)asu.edu <mailto:katina.michael@asu.edu> <mailto:katina.michael@asu.edu>Overview of Special Issue Information Systems Development (ISD) has been part of the intellectual core of information systems for over 40 years (Obrand et al., 2018; Sidorova et al., 2008). Its chequered history of successes and failures has, however, been an ongoing concern of the IS research community (Dwivedi et al., 2015; Hassan & Mathiassen, 2017). Despite efforts to improve the management of ISD projects, these efforts have not had the desired effect (Lim et al., 2011). There is anecdotal evidence that business analytics can help project managers to (i) understand the dynamics and collective state of complex projects, (ii) detect and forecast trends, (iii) improve the effectiveness of risk models, (iv) evaluate the effectiveness of a change to the development process, and (v) distinguish questions of ‘information’ from questions of ‘insight’ (Davenport et al., 2010). Business analytics are frequently referred to as ‘the techniques, technologies, systems, practices, methodologies, and applications that analyse critical business data to help an enterprise better understand its business and market and make timely decisions’ (Chen et al., 2012, p.1166). Yet, much of the research conducted to date has focused on the technologies of business analytics and not enough on the people and their organisational context in which such technologies are intended to be used (Abbasi et al, 2017; Conboy et al., 2018; Mikalef et al., 2019). This is a significant limitation given that the ISD environment is a highly metric oriented, complex, and socially embedded activity that is continuously changing (Conboy, 2009; Kudaravalli et al., 2017; Windeler et al., 2017). This special issue seeks to collect contemporary research on the latest developments and challenges of how organisations exploit business analytics to support project/portfolio managers, project teams, and other project stakeholders. Indicative List of Anticipated Themes This special issue seeks a wide range of articles that draw on diverse project settings, theories, and approaches to understand the different aspects of business analytics as applied to the context of ISD. The following questions are of interest for the special issue: • How can business analytics be used to better understand and manage ISD projects? • What value does business analytics provide to ISD management and development teams? • What tensions arise from the integration of business analytics with traditional and agile methods and practices? • What are the emerging best practices that enable business analytics to be embedded within the ISD process and the wider organisation? • Does the deployment of business analytics make existing agile methods and practices less valuable or even obsolete? • What new metrics and standards can business analytics provide to manage and control ISD projects more effectively? • How can business analytics enable organisational learning and innovation in the context of ISD? • How are business analytics being applied in various types (distributed, large scale) of ISD projects? • How can business analytics support the scaling of ISD projects? • What are the change management and organisational cultural issues that need to be considered when developing analytical capabilities? • How can business analytics provide new ways of working in ISD projects? • What ethical issues stem from the use of business analytics in ISD projects? • How can business analytics be used to support more effective decision making? • What are the new theories and theoretical developments to explain the implementation and use of business analytics in ISD projects? These questions are not intended to be exhaustive. Rather they are intended to stimulate thinking about the role of business analytics in the management of ISD projects across various levels of analysis - from participants in individual projects through projects, programs, portfolios, organisations, and the wider society. We welcome submissions that address questions pertaining to all aspects of the intersection of business analytics and ISD project management. Important Dates: • Initial paper submission deadline: 30th September, 2020 • First round authors notification: 30th November, 2020 • Invited revisions deadline: 31st January, 2021 • Second round authors notification: 31st March, 2021 • Final revision deadline: 31st May, 2021 • Final authors notification: 30th June, 2021 • Projected publication date: Winter 2021 Submission Details To view the author guidelines for this journal, please visit: https://www.emeraldgrouppublishing.com/products/journals/author_guidelines.… Please submit your manuscript via our review website: http://mc.manuscriptcentral.com/itp References Abbasi, A., Sarker, S. and Chiang, R.H., 2016. Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), p.I. Conboy, K., Dennehy, D., & O'Connor, M. (2018). ‘Big time’: An examination of temporal complexity and business value in analytics. Information & Management. Conboy, K., 2009. Agility from first principles: Reconstructing the concept of agility in information systems development. Information Systems Research, 20(3), pp.329-354. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4). Davenport, T.H., Harris, J. and Shapiro, J., 2010. Competing on talent analytics. Harvard Business Review, 88(10), pp.52-58. Dwivedi, Y.K., Wastell, D., Laumer, S., Henriksen, H.Z., Myers, M.D., Bunker, D., Elbanna, A., Ravishankar, M.N. and Srivastava, S.C., 2015. Research on information systems failures and successes: Status update and future directions. Information Systems Frontiers, 17(1), pp.143-157. Hassan, N.R. and Mathiassen, L., 2018. Distilling a body of knowledge for information systems development. Information Systems Journal, 28(1), pp.175-226. Kudaravalli, S., Faraj, S. and Johnson, S.L., 2017. A Configural Approach to Coordinating Expertise in Software Development Teams. MIS Quarterly, 41(1). Lim, E.P., Chen, H. and Chen, G., 2013. Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 3(4), p.17. Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272-298. Sidorova, A., Evangelopoulos, N., Valacich, J.S. and Ramakrishnan, T., 2008. Uncovering the intellectual core of the information systems discipline. MIS Quarterly, pp.467-482. Öbrand, L., Augustsson, N.P., Mathiassen, L. and Holmström, J., 2019. The interstitiality of IT risk: An inquiry into information systems development practices. Information Systems Journal, 29(1), pp.97-118. Windeler, J.B., Maruping, L. and Venkatesh, V., 2017. Technical systems development risk factors: The role of empowering leadership in lowering developers’ stress. Information Systems Research, 28(4), pp.775-796. _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] CFP - The Bright Side and the Dark Side of Digital Health, Internet Research (SSCI, ABDC 'A' JCR Q1)
by Gustaf Neumann 20 Aug '20

20 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] CFP - The Bright Side and the Dark Side of Digital Health, Internet Research (SSCI, ABDC 'A' JCR Q1) Date: Fri, 21 Aug 2020 01:21:47 +0900 From: Young Hoon Chang <younghoonchang(a)gmail.com> To: AISWorld(a)lists.aisnet.org Internet Research https://www.emeraldgrouppublishing.com/journal/intr/bright-side-and-dark-si… [Call for Papers] - The Bright Side and the Dark Side of Digital Health Submission due: September 1, 2020 Guest Editors Zhijun Yan - Beijing Institute of Technology, China, yanzhijun(a)bit.edu.cn Roberta Bernardi - University of Bristol, UK, roberta.bernardi(a)bristol.ac.uk Nina (Ni) Huang - University of Houston, US, nihuang(a)bauer.uh.edu Younghoon Chang - Beijing Institute of Technology, China, younghoonchang(a)bit.edu.cn Overview of Special Issue Digital technology has been transforming how individuals, organizations, and societies use information to improve their decision making on their daily lives and daily operations. In recent years, the healthcare industry has also actively engaged in the adoption of digital technology and enabled the formation of digital health. The digital health covers lots of advanced technologies, such as mobile health (mHealth), health information technology (HIT), wearable devices, telehealth and telemedicine, health data analytics and personalized medicine (Lupton 2018). These technologies offer new exciting opportunities to improve medical outcomes, enhance efficiency and balance health resources. In particular, digital health can better collect, process and analyze health-related information, and provide decision support for patients, doctors, healthcare organizations, public health management and medical research (Guha and Kumar 2018). There are many positive and negative issues associated with the use of digital health by these stakeholders. On the one hand, digital health can empower patients to make better decisions about their own health and provide new options for facilitating prevention, early diagnosis, surveillance, management and prediction of chronic conditions outside traditional healthcare settings (Lin et al. 2017). Doctors can also get a more holistic view of patient health through access to data and improve quality of care (Lin et al. 2019). Pharmaceutical companies and digital health companies can also benefit from patient-generated knowledge for the advancement of medical research (Kallinikos and Tempini 2014) and the design of personalized healthcare interventions (Bernardi 2019). On the other hand, the integration of digital technology in the healthcare industry presents risks such as the spread of misinformation (e.g. anti-vax communities, Doty 2015), the disclosure of patients' privacy that could be used by health insurance companies to make discriminatory pricing (McFall and Moor 2018), increased doctors' technical anxiety and slow acceptance of digital health innovation (Bernardi and Exworthy 2019), and health inequalities due to the digital exclusion of patients (Latulippe et al. 2017; Halford and Savage 2010). The healthcare industry is one of the largest and also one of the most important industries for citizens’ wellbeing. Addressing the complexities of today’s various negative and positive healthcare issues requires more than one perspective and needs more interdisciplinary collaboration and research. The rapid development of advanced technologies and methodologies such as social media, Internet of things (IoT) data analytics, machine learning, artificial intelligence (AI) brings lots of opportunities to handle the complicated problems in the healthcare industry. It makes it possible to improve people’s health conditions smartly and comfortably. However, the adoption of digital technology in health care usually lags behind other industries, as some major technological and managerial obstacles still remain (Bunduchi et al. 2015). Obstacles include the lack of health data integration, data overload issues, data privacy and security, and limited or inefficient data visualization (Agarwal et al. 2010). At the same time, academics need to address the issues related to the dark side and potential risks of digital health. This special issue aims to serve as a forum in which healthcare, computer science, management and social science scholars can come together to discuss new emerging issues related to the bright side and the dark side of digital health. It invites submissions from a variety of methodological, theoretical, and multidisciplinary perspectives. Theoretical work that engages critically with the debate about the bright and dark sides of digital health is also welcome. In bringing technical, behavioral, clinical, and managerial perspectives together, this special issue hopes to generate new insights into the design, adoption, utilization, and management of digital health as well as an understanding of its risks and adverse consequences for individuals, organizations, and societies. Topics of interest include, but are not limited to: • Participating behavior of digital health • Knowledge sharing and knowledge seeking of online health communities • Knowledge discovery and decision support based on online health communities and clinical decision-making systems • Social and economic return of digital health • Data privacy, trust and security in digital health • Fake information and information fraud in online health communities • Online-offline data integration and analytics • Organizational, operational, clinical and financial implications of digital health • Health, social and economic impact of digital health • Big data analytics and artificial intelligence application • Theories, models and classification frameworks that shed light on the bright side and dark side of digital health • Methods for studying the bright side and dark side of digital health and its impact on individuals, communities (societies) and organizations • Understanding how individuals, communities and organizations can minimize, prevent or respond to the dark side of digital health • Understanding what motivates individuals, communities and organizations to deliberately engage in digital health • Examining the dark side (outcomes, behaviors and practices) that accidently or unintentionally emerge in digital health • The ethics of the dark sides of digital health (especially with recent AI developments and uses in digital health) • Region, sector and industry-focused studies on the bright side and dark side of digital health • Economic impact of digital health on the healthcare industry • The effects of digital health on epidemic or pandemic outbreaks Important dates • Submission due: September 1, 2020 • 1st round review decision: November 30, 2020 • Revised submission due: December 31, 2020 • 2nd round final review decision: January 31, 2021 • Publication: 2021 Submission Details Internet Research is an international, refereed journal and listed by Social Science Citation Index (SSCI) and Science Citation Index Expanded (SCIE) (IF 4.109 in 2018). To view the author guidelines for this journal, please visit: https://www.emeraldgrouppublishing.com/products/journals/author_guidelines.… Please submit your manuscript via our review website: https://mc.manuscriptcentral.com/intr Editorial Review Board Spyros Angelopoulos - Tilburg University, Netherlands Petros Chamakiotis - ESCP Business School (Madrid), Spain Ben Choi - Nanyang Technological University, Singapore Qianzhou Du - Nanjing University, China Juyeon Ham - Beijing Institute of Technology, China Kevin Yili Hong - Arizona State University, USA Liqiang Huang - Zhejiang University, China Yi-cheng Ku - Fu Jen Catholic University, Taiwan One-Ki Daniel Lee - University of Massachusetts Boston, USA Weizi Li - University of Reading, UK Christian Libaque-Saenz - Universidad del Pacífico, Peru Benjamin Marent - University of Sussex, UK Yang Pan - Louisiana State University, USA Jae Hyun Park - Kyoto Institute of Technology, Japan. Dimitra Petrakaki - University of Sussex, UK Niccolò Tempini - University of Exeter, UK Yichuan Wang - University of Sheffield, UK Siew Fan Wong - Sunway University, Malaysia Jiayin Zhang - Tsinghua University, China Minhao Zhang - University of Bristol, UK Xiaofei Zhang - Nankai University, China Kang Zhao - University of Iowa, USA Yuxiang Zhao - Nanjing University of Science & Technology, China _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: Full Paper Due: Sep. 21, 2020
by Gustaf Neumann 19 Aug '20

19 Aug '20

-------- Forwarded Message -------- Subject: Full Paper Due: Sep. 21, 2020 Date: Wed, 19 Aug 2020 13:32:32 +0800 (CST) From: cise <cise(a)muchawait.com> Reply-To: conf_contact(a)163.com To: gustaf.neumann(a)wu.ac.at Dear Colleagues, The 13th Int'l Conference on Computational Intelligence and Software Engineering (CISE 2021) will be held in Sanya, China during January 5-7, 2021. CiSE is an annual regular conference since 2008 and has been successfully held in Shanghai, China and Bangkok, Thailand. CiSE 2021 will be a valuable and important platform for inspiring Int’l and interdisciplinary exchange at the forefront of Computational Intelligence and Software Engineering. *Time*: January 5-7, 2021 *Venue*: International Asia-Pacific Convention Center Sanya, Sanya, China *Themes*: Computational Intelligence and Software Engineering Sanya, China Sanya is superior in geography climate and natural resources. It is known as “the oriental Hawaii" and “Sunshine City” because of its special tropical scenes as well as its attractive nature. Sanya's tourist resources, richly endowed by nature, are not only special in China, but also rare in the world. All the accepted papers will be published by peer-reviewed open access journal and submitted to CNKI and Google Scholar for indexing. Note: Kindly submit your papers/abstracts to: /conf_contact(a)163.com/ Looking forward to your participation. Best regards, Organizing Committee PS: If you wish to serve the conference as a Keynote Speaker, please send email to us with your CV for evaluation.

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Fwd: [AISWorld] [AJIS] New Post Publication Review: A post-publication review of "Research directions in information systems field, current status and future trends: A literature analysis of AIS basket of top journals"
by Gustaf Neumann 18 Aug '20

18 Aug '20

-------- Forwarded Message -------- Subject: [AISWorld] [AJIS] New Post Publication Review: A post-publication review of "Research directions in information systems field, current status and future trends: A literature analysis of AIS basket of top journals" Date: Tue, 18 Aug 2020 12:25:33 +1000 From: Ajis Editor <ajis.eic(a)gmail.com> To: ISHoDs <IS-hods(a)list.utas.edu.au>, ISAus <IS-Aus(a)list.utas.edu.au>, ISWorld <aisworld(a)lists.aisnet.org> Hi, The *Australasian Journal of Information Systems* has just published its latest article. *A post-publication review of "Research directions in information systems field, current status and future trends: A literature analysis of AIS basket of top journals"W M Limhttps://doi.org/10.3127/ajis.v24i0.2921 <https://doi.org/10.3127/ajis.v24i0.2921>* *Abstract* This post-publication review highlights the strengths and shortcomings of Mazaheri, Lagzian, and Hemmat (2020) and the key considerations for future systematic reviews in information systems. -=-=-=- *Call for Papers* AJIS publishes high quality contributions to the global Information Systems (IS) discipline with an emphasis on theory and practice on the Australasian context. Topics cover core IS theory development and application (the nature of data, information and knowledge; formal representations of the world, the interaction of people, organisations and information technologies; the analysis, design and deployment of information systems; the impacts of information systems on individuals, organisations and society), IS domains (e-business, e-government, e-learning, e-law, etc) and IS research approaches. Research and conceptual development based in a very wide range of epistemological methods are welcomed. All manuscripts undergo double blind reviewing by at least 2 well qualified reviewers. Their task is to provide constructive, fair, and timely advice to authors and editor. AJIS welcomes research and conceptual development of the IS discipline based in a very wide range of epistemologies. Different types of research paper need to be judged by different criteria. Here are some assessment criteria that may be applied: • Relevance - topic or focus is part of the IS discipline. • Effectiveness - paper makes a significant contribution to the IS body of knowledge. • Impact - paper will be used for further research and/or practice. • Uniqueness - paper is innovative, original & unique. • Conceptual soundness - theory, model or framework made explicit. • Argument - design of the research or investigation is sound; methods appropriate. • Clarity - Topic is clearly stated; illustrations, charts & examples support content. • Reliability - data available; replication possible. • References - sound, used appropriately, and sufficient – appropriate AJIS articles referenced • Style - appropriate language, manuscript flows. This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. AJIS has been published since 1993 and appears in the Index of Information Systems Journals, is ranked "A" by both the Australian Council of Professors and Heads of Information Systems and the Australian Business Deans' Council. In addition to web distribution, AJIS is distributed by EBSCO, it is listed in Cabell's International Directory and is indexed by EBSCO, Elsevier, Scopus and the Directory of Open Access Journals. Thanks for the continuing interest in our work, Cheers Associate Professor John Lamp Editor-in-Chief, *Australasian Journal of Information Systems* http://journal.acs.org.au/index.php/ajis/ _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [computational.science] ScalA@SC20 - Papers due September 1 | SC20 Conference Going Virtual
by Gustaf Neumann 18 Aug '20

18 Aug '20

-------- Forwarded Message -------- Subject: [computational.science] ScalA@SC20 - Papers due September 1 | SC20 Conference Going Virtual Date: Mon, 17 Aug 2020 21:37:33 +0000 From: Engelmann, Christian via computational.science <computational.science(a)lists.iccsa.org> Reply-To: Engelmann, Christian <engelmannc(a)ornl.gov> To: sdmas(a)googlegroups.com <sdmas(a)googlegroups.com>, TCSC(a)cse.stfx.ca <TCSC(a)cse.stfx.ca>, hpc-announce(a)mcs.anl.gov <hpc-announce(a)mcs.anl.gov>, Computational Science List <computational.science(a)lists.iccsa.org>, sc-workshop-attendee-cfp(a)group.supercomputing.org <sc-workshop-attendee-cfp(a)group.supercomputing.org>, siam-sc(a)siam.org <siam-sc(a)siam.org> We apologize if you receive multiple copies of this notice. ----------------------------------------------------------------------------- ScalA20: 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems held in conjunction with the SC20: The International Conference on High Performance Computing, Networking, Storage and Analysis in cooperation with the IEEE Computer Society Technical Consortium on High Performance Computing (TCHPC) November 12, 2020 --- Virtual Location <http://www.csm.ornl.gov/srt/conferences/Scala/2020> Novel scalable scientific algorithms are needed in order to enable key science applications to exploit the computational power of large-scale systems. This is especially true for the current tier of leading petascale machines and the road to exascale computing as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, and have no synchronization points. With the advent of Big Data and AI in the past few years the need of such scalable mathematical methods and algorithms able to handle data and compute intensive applications at scale becomes even more important. Scientific algorithms for multi-petaflop and exa-flop systems also need to be fault tolerant and fault resilient, since the probability of faults increases with scale. Resilience at the system software and at the algorithmic level is needed as a crosscutting effort. Finally, with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs, scientific algorithms need to match these architectures to extract the most performance. This includes different system-specific levels of parallelism as well as co-scheduling of computation. Key science applications require novel mathematical models and system software that address the scalability and resilience challenges of current- and future-generation extreme-scale HPC systems. Submission Guidelines --------------------- Authors are invited to submit manuscripts in English structured as technical papers at a length of at least 6 letter size (8.5in x 11in) pages and not exceeding 8 pages, including figures, tables, and references using the IEEE format for conference proceedings. Reference style files are available at <http://www.ieee.org/conferences_events/conferences/publishing/templates.html>. Submitted papers must represent original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines will be rejected without review and further action may be taken, including (but not limited to) notifications sent to the heads of the institutions of the authors and sponsors of the conference. Submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. Papers should be submitted electronically at <https://submissions.supercomputing.org>. All manuscripts will be peer-reviewed and judged on correctness, originality, technical strength, and significance, quality of presentation, and interest and relevance to the workshop attendees. Accepted papers will be published with the IEEE Computer Society as part of the SC20 workshop proceedings in the IEEE Xplore Digital Library. At least one author of an accepted paper must register for and present the paper at the workshop. Authors may contact the workshop program chair, Christian Engelmann at engelmannc(a)ornl.gov, for more information. Transparency and Reproducibility Initiative ------------------------------------------- As part of a major initiative that aims to increase the level of reproducibility and replicability of results, ScalA20 invites authors of technical papers to submit optional appendix information that can promote better reproducibility of computational results. Authors are highly encouraged to provide a 2-page Artifact Description Appendix, which will not count toward the page limit of the submission. Notes: - A paper cannot be disqualified based on information provided or not provided in this appendix, nor if the appendix is not available. - The availability and quality of an appendix can be used in ranking a paper. In particular, if two papers are of similar quality, the existence and quality of the appendices can be part of the evaluation process. - Appendices should not be used to circumvent the page limit. Further information about the SC Transparency and Reproducibility Initiative can be found at <https://sc20.supercomputing.org/submit/transparency-reproducibility-initiat…>. Important Web Sites ------------------- - ScalA20 Website: <https://www.csm.ornl.gov/srt/conferences/Scala/2020> - ScalA20 Submissions: <https://submissions.supercomputing.org> - SC20 website: <http://sc20.supercomputing.org/> SC20 Going Virtual ------------------ The Planning Committee has announced that SC20 will be a fully virtual conference. More details can be found here: <https://sc20.supercomputing.org/2020/07/27/sc20-virtual-event-announced-by-…>. Important Dates --------------- - Full paper submission: September 1, 2020 - Notification of acceptance: October 1, 2020 - Final paper submission (firm): October 11, 2020 - Workshop/conference early registration: TBD - Workshop: November 12, 2020 (virtual location, 10AM-6:30PM EST, Track 8) Topics of interest include, but are not limited to: --------------------------------------------------- - Novel scientific algorithms that improve performance, scalability, resilience, and power efficiency - Porting scientific algorithms and applications to many-core and heterogeneous architectures - Performance and resilience limitations of scientific algorithms and applications at scale, including Data Science approaches in dealing with Big Data - Crosscutting approaches (system software and applications) in addressing scalability challenges - Scientific algorithms that can exploit extreme concurrency (e.g. 1 billion for exascale by 2023) - Naturally fault tolerant, self-healing, or fault oblivious scientific algorithms - Programming model and system software support for algorithm scalability and resilience (including ones enabling Big Data processing) Workshop Chairs --------------- - Vassil Alexandrov, Hartree Centre, Science and Technology Facilities Council, UK - Al Geist, Oak Ridge National Laboratory, USA - Jack Dongarra, University of Tennessee, Knoxville, USA Workshop Program Chair ---------------------- - Christian Engelmann, Oak Ridge National Laboratory, USA Contact at engelmannc(a)ornl.gov Program Committee ----------------- - Hartwig Anzt, University of Tennessee, Knoxville, USA - Rick Archibald, Oak Ridge National Laboratory, USA - Marco Berghoff, Karlsruhe Institute of Technology, Germany - Hans-Joachim Bungartz, Technical University of Munich, Germany - Florina M. Ciorba, University of Basel, Switzerland - James Elliott, Sandia National Laboratories, USA - Nahid Emad, University of Versailles SQ, France - Wilfried Gansterer, University of Vienna, Austria - Yasuhiro Idomura, Japan Atomic Energy Agency, Japan - Kirk E. Jordan, IBM T.J. Watson Research, USA - Dieter Kranzlmueller, Ludwig-Maximilians-University Munich, Germany - Sriram Krishnamoorthy, Pacific Northwest National Laboratory, USA - Paul Lin, Lawrence Berkeley National Laboratory, USA - Kengo Nakajima, RIKEN, Japan - Yves Robert, ENS Lyon, France - Stuart Slattery, Oak Ridge National Laboratory, USA - Valerie Taylor, Argonne National Laboratory, USA -- Christian Engelmann, Ph.D. Senior R&D Staff Scientist Computer Science Research Group Computer Science and Mathematics Division Oak Ridge National Laboratory Mail: P.O. Box 2008, Oak Ridge, TN 37831-6173, USA Phone: +1 (865) 574-3132 / Fax: +1 (865) 576-5491 e-Mail: engelmannc(a)ornl.gov / Home: www.christian-engelmann.info

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