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The scale, dynamics, and robustness of the Web as a decentralized information system are unparalleled in history. Since the Web took off, Web engineering has tried to identify and reuse principles and patterns that were identified as the foundations for this success story. The latest development in this space are microservices, which take a refreshingly new approach by focusing on the organizational and cultural principles underlying the design and development of IT components. In this talk we take a closer look at how microservices compare to previous approaches of using the Web as inspiration for IT design and development. The result of this closer look is the identification of a few trends that are the drivers behind the current microservices adoption, and the identification of some areas where additional work is required to better fulfill the promise of Web-scale robustness and scalability.
Dr. Erik Wilde joined CA's API Academy in May 2016. He is Director of Technologies and focuses on technologies, standards, and design issues of open service landscapes. His areas of interest include Internet and Web of Things, semantic technologies, service description and documentation, and questions of how to design for openness and evolvability. Before joining the API Academy, Erik had a long career in academia, working mostly at ETH Zürich and UC Berkeley, and after that worked at EMC and Siemens before joining CA. Erik is a frequent speaker at industry and research events; you can follow his updates on Twitter at @dret.
03.11.2016 - Automatic Text Simplification
Speaker: Prof. Horacio Saggion, Ph.D.
Host: Prof. Dr. Martin Volk
Automatic text simplification arose from the necessity to make electronic textual content equally accessible to everyone. Automatic text simplification is a complex task which encompasses a number of operations applied to a text at different linguistic levels. The aim is to turn a complex text into a simplified variant, taking into consideration the specific needs of a particular target user. In this talk I will provide the audience with an overview of work in the area emphasizing also the relevant social function that content simplification can make to the information society. I will also present the techniques we have developed in the past years to produce simplification software for English and Spanish.
Prof. Peter Cramton, Ph.D., is Professor of Economics at the University of Maryland and European University Institute, and on the International Faculty at the University of Cologne. Since 1983, he has conducted research on auction theory and practice. This research appears in the leading economics journals. The main focus is the design of auctions for many related items. Applications include spectrum, energy, and financial auctions. On the practical side, he is an independent director on the board of the Electric Reliability Council of Texas, chief economist of Rivada, and chairman of Market Design Inc., an economics consultancy founded in 1995, focusing on the design of auction and matching markets. Since 1993, he has advised 12 governments and 40 bidders in spectrum auctions. He is a co-inventor of the spectrum auction design used in Canada, Australia, and many European countries to auction 4G spectrum. Since 2001, he has played a lead role in the design and implementation of electricity and gas auctions in North America, South America, and Europe. He has advised on the design of carbon auctions in Europe, Australia, and the United Sates, including conducting the world’s first greenhouse-gas auction held in the UK in 2002. He has led the development of innovative auctions in new applications, such as auctions for airport slots, wind rights, diamonds, medical equipment, and Internet top-level domains. He received his B.S. in Engineering from Cornell University in 1980 and his Ph.D. in Business from Stanford University in 1984.
For the longest time in human history, on a technical level, it was very hard to involve large numbers of participants in decision making processes. Essentially, participation on a large scale was limited to voting on a pre-defined set of alternatives. The ubiquitous availability of the Internet has changed this. We now have the technical infrastructure that is necessary to involve large numbers of participants in the whole decision making process: starting with the joint identification of what should be decided upon, including collaborative drafting of alternatives up to reaching collective decisions. Online participation is the process of using this potential to solve real-world decision making problems.
Online participation is already widely used in practice, in particular at the local government level. Examples are participatory budgeting or municipal construction planning. While some applications of online participation are considered to be very successful, many others have not fully met the expectations that have been placed in them or are even considered outright failures. This makes online participation a very interesting field of research: We know that it can work. We know that it often does not work. And we want to understand why this is and what we can do to make it work consistently. Obviously this is not a research topic that can be fully addressed by computer scientists. It requires an interdisciplinary effort to find answers to those questions. However, it is equally obvious that computer science will play a major role in providing the functionality required to make online participation successful on a consistent basis.
In my talk I will present an example for online participation at the Heinrich-Heine-University in Düsseldorf, where we drafted new doctoral degree regulations involving all professors and Ph.D. students of the faculty for mathematics and natural sciences - a total of around 1000 participants. Based on the experience we made with this experiment, we identified the ability to support large scale online discussions as a critical functionality that requires better solutions than those that are currently available. In the second part of my talk I will therefore highlight a novel approach to support online discussions with many participants. Its main idea is to simulate a real-world discussion with two participants: one of those participants it the current user and the other one is the aggregated representation of all users that have already participated in the past.
Prof. Dr. Martin Mauve is professor for computer networks and communication systems at the Heinrich-Heine University in Düsseldorf, Germany. For almost ten years his focus of research was on inter-vehicle communication and ad-hoc networks. In 2013 he was fascinated by the ideas put forward by the Pirate Party on how to involve their members in decision making processes and started investigating online participation as a research topic. He is now heading an interdisciplinary graduate school on online participation (http://www.fortschrittskolleg.de) and working on supporting large scale online discussions.
24.11.2016 - Artificial Intelligence Meets Finance: Algorithmic Trading, Credit Networks, and Agent-Based Modeling
Speaker: Prof. Michael Wellman, Ph.D.
Host: Prof. Sven Seuken, Ph.D.
AI is already a ubiquitous presence in the financial system, most obviously in the guise of algorithmic trading, but also in the automation of credit issuance and other finance-related decisions. Understanding the implications of AI on the financial system, both generally and with respect to specific techniques, requires modeling the complexity of financial activity and the computational ingredients in decision making. Agent-based modeling (ABM) affords direct representation of these computational elements, and accommodates the heterogeneity and complexity characteristic of financial environments. Although ABM is typically presented as an alternative to mainstream economic approaches, I argue that it is actually compatible and even complementary with standard frameworks, and demonstrate this with recent finance-related studies conducted in my research group. We focus on two domains: algorithmic trading in financial markets, and dynamics of payment and credit in distributed financial networks.
Prof. Michael P. Wellman, Ph.D. is the Lynn A. Conway Collegiate Professor of Computer Science & Engineering at the University of Michigan, where he also serves as Associate Dean for Academic Affairs in the College of Engineering. He received a PhD from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. For the past 20+ years, his research has focused on computational market mechanisms and game-theoretic reasoning methods, with applications in electronic commerce, finance, and other domains of strategic decision making. Michael previously served as Chair of the ACM Special Interest Group on Electronic Commerce (SIGecom), and as Executive Editor of the Journal of Artificial Intelligence Research. He is a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery, and 2014 recipient of the SIGAI Autonomous Agents Research Award.
08.12.2016 - Crowd-Powered Data Management
Speaker: Prof. Tova Milo, Ph.D.
Host: Prof. Abraham Bernstein, Ph.D.
Modern data analysis combines general knowledge stored in databases with individual knowledge obtained from the crowd, capturing people habits and preferences. To account for such mixed knowledge, along with user interaction and optimization issues, data management platforms must employ a complex process of reasoning, automatic crowd-task generation and result analysis. In this talk, I will introduce the notion of crowd mining and describe a generic architecture for crowd mining applications. This architecture allows us to examine and compare the components of existing crowdsourcing systems and point out extensions required by crowd mining. It also highlights new research challenges and potential reuse of existing techniques/components. I will exemplify this for the OASSIS project, a system developed in Tel Aviv University, and for other prominent crowdsourcing frameworks.
Prof. Tova Milo, Ph.D., received her Ph.D. degree in Computer Science from the Hebrew University, Jerusalem, in 1992. After graduating she worked at the INRIA research institute in Paris and at University of Toronto and returned to Israel in 1995, joining the School of Computer Science at Tel Aviv university, where she is now a full Professor. She is the head of the Database research group and holds the Chair of Information Management. She served as the Head of the Computer Science Department from 2011-2014. Her research focuses on advanced database applications such as data integration, XML and semi-structured information, Data centered Business Processes and Crowd-sourcing, studying both theoretical and practical aspects. Tova served as the Program Chair of several international conferences, including PODS, VLDB, ICDT, XSym, and WebDB, and as a member of the VLDB Endowment and the ICDT executive board. She also served as the chair of the PODS Executive Committee and an editor of TODS and the Logical Methods in Computer Science Journal.
Tova has received grants from the Israel Science Foundation, the US-Israel Binational Science Foundation, the Israeli and French Ministry of Science and the European Union. She is an ACM Fellow, a member of Academia Europaea, and a recipient of the 2010 ACM PODS Alberto O. Mendelzon Test-of-Time Award and of the prestigious EU ERC Advanced Investigators grant.
15.12.2016 - A Process Mining Framework for Analyzing Learning Clickstream Data
Speaker: Prof. Harry Jiannan Wang, Ph.D.
Host: Prof. Daning Hu, Ph.D.
Learning analytics is an emerging field of research that aims to utilize a wide range of educational data to establish a deep understanding of the learning processes and learner behavior. In this paper, we propose a process mining framework for analyzing large-scale learning clickstream data collected from a major US university’s learning management system. We address a number of modeling and analysis challenges from a process mining perspective and propose new concepts for process-centric learning analytics. Based on the process mining results, we conduct an experiment to see how social influence affects learning processes and results.
Prof. Harry Jiannan Wang, Ph.D., is an Associate Professor of Management Information Systems (MIS) and JPMorgan Chase Fellow in the Lerner College of Business and Economics, University of Delaware. He is also the Vice President of Technology for the Association for Information Systems. He received Ph.D. in MIS from the Eller College of Management, University of Arizona, USA and B.S. in MIS from Tianjin University, China. His research interests involve business process management, business analytics and intelligence, services computing, and enterprise systems. He has published research articles in journals, such as Information Systems Research, Decision Support Systems, ACM Transactions on Management Information Systems, Journal of Database Management, and Information and Management. Dr. Wang has serviced as associate editor, special issue guest editor, and editorial board member for several journals and organized the 2014 Workshop on Business Processes and Service and the 2013 China Summer Workshop on Information Management as a conference co-char. He has also been a program co-chair, program committee member, and track co-chair for numerous conferences.
