Big Data Analytics - Just More or Conceptually Different?
Claudia Loebbecke, University of Cologne, Germany
The Sensing Enterprise - Enterprise Information Systems in the Internet of Things
Sergio Gusmeroli, Independent Researcher, Italy
Making Process Mining Green - Using Event Data in a Responsible Way
Wil Van Der Aalst, Technology Management - Information Systems, Technische Universiteit Eindhoven, Netherlands
The Power of Text Mining - How to Leverage Naturally Occurring Text Data for Effective Enterprise Information Systems Design and Use
Jan Vom Brocke, Institute of Information Systems, University of Liechtenstein, Liechtenstein
Big Data Analytics - Just More or Conceptually Different?
Claudia Loebbecke
University of Cologne
Germany
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Brief Bio
Claudia Loebbecke is a professor of Business, Media and Technology Management and Director of the Department of Media and Technology Management at the University of Cologne, Germany. 2005–2006 she was elected president of the Association for Information Systems (AIS). She serves as Senior Editor of the Journal of Strategic Information Systems (JSIS), as Advisory Board Member of Information Systems Research (ISR) and of the Journal of Information Technology (JIT), and on the Editorial Board of the Information Systems Journal (ISJ) and Communications of the Association for Information Systems (CAIS). Claudia received a Masters (1990) and a Ph.D. (1995) in Business Administration, both from the University of Cologne, Germany, and an MBA from Indiana University, Bloomington, Indiana, U.S. (1991). In 2011, she co-authored the study “Assessing Cloud Readiness,” which won the Research Competition of the Society for Information Management. She has published over fifty internationally peer-reviewed journal articles.
Abstract
In the era of so-called big data, analytics for designing and delivering innovative services and actionable insights goes beyond dealing faster and smarter with more data. Done well, harnessing big data analytics will drive fundamentally transformed approaches to value creation – in business, industry sectors, society, higher education, and research.
This presentation will outline how big data analytics can empower organizations in the big data era and hopefully open the discussion on proactively shaping of new opportunities
The Sensing Enterprise - Enterprise Information Systems in the Internet of Things
Sergio Gusmeroli
Independent Researcher
Italy
Brief Bio
Sergio Gusmeroli is a Senior Advisor for the Research and Innovation Unit in Engineering Ingegneria Informatica SPA (www.eng.it). As a researcher, Sergio has recently and is coordinating large scale FP7 projects and H2020 R&I actions (e.g. Elliot MSEE FITMAN OSMOSE PSYMBIOSYS) in the field of IoT technologies especially applied to Manufacturing Industries. As an innovation manager, Sergio has been co-conducting with IDC the EC-commissioned study Definition of a research and innovation policy leveraging Cloud Computing and IoT Combination, SMART 2013/0037 and is coordinating a H2020 Innovation Action about full adoption of IoT-enabled Cyber Physical Production Systems in Industry.
Abstract
The keynote aims at describing how recent IT innovations in the field of IoT (e.g. cyber physical systems, smart networks, edge computing, smart objects, business intelligence, data analytics) are influencing the evolution of Enterprise Information Systems. Thanks to the advent of IOT, Enterprise PLM systems are abandoning the walled garden of Design and Engineering, while embracing the whole product lifecycle, including post-sales services and addressing circular economy challenges, becoming this way “Things Lifecycle Management Systems”. At the same time, MES (Manufacturing Execution Systems) need to consider Industry 4.0 evolution in production systems and the advent of Cyber Physical and Systems. What it is not fully clear up to now is the IOT-driven evolution of ERP and SCM systems and how decision making at the level of configuration, planning and scheduling of enterprises’ resources could be implemented by distributed edge-computing architectures. We call this new concept “The Sensing Proactive Enterprise”. The speech is inspired by several EC-funded R&I projects in the field of “IOT for Enterprise” under the FP7 and H2020 Framework Programmes in the Net Innovation unit E3 of the Future Internet (DG CNECT).
Making Process Mining Green - Using Event Data in a Responsible Way
Wil Van Der Aalst
Technology Management - Information Systems, Technische Universiteit Eindhoven
Netherlands
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Brief Bio
Prof.dr.ir. Wil van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). At TU/e he is the scientific director of the Data Science Center Eindhoven (DSC/e). Since 2003 he holds a part-time position at Queensland University of Technology (QUT). His personal research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Wil van der Aalst has published more than 180 journal papers, 18 books (as author or editor), 400 refereed conference/workshop publications, and 60 book chapters. Many of his papers are highly cited (he one of the most cited computer scientists in the world and has an H-index of 122 according to Google Scholar) and his ideas have influenced researchers, software developers, and standardization committees working on process support. He has been a co-chair of many conferences including the Business Process Management conference, the International Conference on Cooperative Information Systems, the International conference on the Application and Theory of Petri Nets, and the IEEE International Conference on Services Computing. He is also editor/member of the editorial board of several journals, including Computing, Distributed and Parallel Databases, Software and Systems Modeling, the International Journal of Business Process Integration and Management, the International Journal on Enterprise Modelling and Information Systems Architectures, Computers in Industry, Business & Information Systems Engineering, IEEE Transactions on Services Computing, Lecture Notes in Business Information Processing, and Transactions on Petri Nets and Other Models of Concurrency. In 2012, he received the degree of doctor honoris causa from Hasselt University in Belgium. He served as scientific director of the International Laboratory of Process-Aware Information Systems of the National Research University, Higher School of Economics in Moscow. In 2013, he was appointed as Distinguished University Professor of TU/e and was awarded an honorary guest professorship at Tsinghua University. In 2015, he was appointed as honorary professor at the National Research University, Higher School of Economics in Moscow. He is also a member of the Royal Netherlands Academy of Arts and Sciences (Koninklijke Nederlandse Akademie van Wetenschappen), Royal Holland Society of Sciences and Humanities (Koninklijke Hollandsche Maatschappij der Wetenschappen) and the Academy of Europe (Academia Europaea).
Abstract
Process mining provides new ways to utilize the abundance of event data in world surrounding us. These event data enable new forms of analysis facilitating process improvement. Process mining provides a novel set of tools to discover the real process, to detect deviations from some normative process, and to analyze bottlenecks and waste. Process mining will be an integral part of the data scientist's toolbox. Process mining is as generic as a spreadsheet. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Events (often hidden in Big Data) can be considered as the "new oil" and process mining aims to transform these into new forms of "energy": insights, diagnostics, models, predictions, and automated decisions. However, the process of transforming "new oil" (event data) into "new energy" (analytics) may negatively impact citizens, patients, customers, and employees. Systematic discrimination based on data, invasions of privacy, non-transparent life-changing decisions, and inaccurate conclusions illustrate that data science techniques may lead to new forms of "pollution". We use the term ``Green Data Science'' for technological solutions that enable individuals, organizations and society to reap the benefits from the widespread availability of data while ensuring fairness, confidentiality, accuracy, and transparency. To illustrate the scientific challenges related to "Green Data Science'', we focus on "Green Process Mining" as a concrete example. After introducing process mining, Wil van der Aalst will try to answer the question: How to benefit from process mining while avoiding "pollutions" related to unfairness, undesired disclosures, inaccuracies, and non-transparency?
The Power of Text Mining - How to Leverage Naturally Occurring Text Data for Effective Enterprise Information Systems Design and Use
Jan Vom Brocke
Institute of Information Systems, University of Liechtenstein
Liechtenstein
http://www.uni.li/is
Brief Bio
Jan vom Brocke is the Hilti Chair of Business Process Management and Director of the Institute of Information Systems at the University of Liechtenstein. He has been named a Fellow of the Association for Information Systems (AIS), and is a former Vice President of the University of Liechtenstein, the Association for Information Systems and the Association for Business Research. Jan`s work has been published, among others, in Management Science, MIS Quarterly, Journal of Management Information Systems, Journal of Information Technology, Journal of the Association for Information Systems, European Journal of Information Systems, Information Systems Journal, Communications of the ACM, and MIT Sloan Management Review. He has authored and edited over 30 books, including the International Handbook on Business Process Management, and the book BPM Cases - Digital Innovation and Business Transformation in Practice. Jan has teaching experience from 25 universities in 13 countries on Executive, PhD, Master and Bachelor-level, incl. many of the Financial Times Top 50 Business Schools, such as the University of St.Gallen in Switzerland, the Smurfit School of Business in Ireland, the Vlerick Business School in Belgium and the University of Warwick in the UK. His work has attracted more than €40 million in research funding and it has been recognized by press mentions, among others in the Financial Times, the Wall Street Journal, the Daily Mail. Jan has held many senor editorial roles and academic leadership positions throughout the world. He is an invited speaker at and trusted advisor to DAX 30 and Fortune 500 companies and governmental institutions as well as digital start-ups across Europe.
Abstract
This lecture shows how the design and use of Enterprise Information Systems can benefit from text mining techniques. It is estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video); and much of it is expressed in rich and ambiguous natural language. ERP systems store, for instance, piles of texts in the form of documents, E-mails, or service tickets. And comments on corporate social networks can be used to learn about work practices and preferences of users. We call this data naturally occurring data, since it is generated as a by-product of running IT-enabled business processes, and not provoked by analysts. Natural language processing has advanced over the past decades and today tools are available to analyze large amounts of unstructured texts in order to extract topics or sentiments buried in these. The talk gives examples on how successful companies leverage text mining to support important decisions as well as process, product and service innovations. It also shows how text mining can be applied as a new strategy of inquiry in EIS research, when e.g. online customer reviews are evaluated in order to learn about factors driving the ease of use or usefulness of EIS for specific user groups at specific times. The talk also presents a tool, which makes text mining accessible in the cloud both for practitioners and researcher, called MineMyText.com.