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Special Sessions

Tutorial Sessions/Invited Talks

All tutorials and invited talks are free to registered conference attendees of all conferences held at WOLDCOMP'15. Those who are interested in attending one or more of the tutorials are to sign up on site at the conference registration desk in Las Vegas. A complete & current list of WORLDCOMP Tutorials can be found here.

In addition to tutorials at other conferences, DMIN'16 aims at providing a set of tutorials dedicated to Data Mining topics. The 2007 key tutorial was given by Prof. Eamonn Keogh on Time Series Clustering. The 2008 key tutorial was presented by Mikhail Golovnya (Senior Scientist, Salford Systems, USA) on Advanced Data Mining Methodologies. DMIN'09 provided four tutorials presented by Prof. Nitesh V. Chawla on Data Mining with Sensitivity to Rare Events and Class Imbalance, Prof. Asim Roy on Autonomous Machine Learning, Dan Steinberg (CEO of Salford Systems) on Advanced Data Mining Methodologies, and Peter Geczy on Emerging Human-Web Interaction Research. DMIN'10 hosted a tutorial presented by Prof. Vladimir Cherkassky on Advanced Methodologies for Learning with Sparse Data. He was a keynote speaker as well (Predictive Data Modeling and the Nature of Scientific Discovery). In 2011, Gary M. Weiss (Fordham University, USA) presented a tutorial on Smart Phone-Based Sensor Data Mining. Michael Mahoney (Stanford University, USA) gave a tutorial on Geometric Tools for Identifying Structure in Large Social and Information Networks. DMIN'12 hosted a talk given by Sofus A. Macskassy (Univ. of Southern California, USA) on  Mining Social Media: The Importance of Combining Network and Content as well as a talk given by Haym Hirsh (Rutgers University, USA): Getting the Most Bang for Your Buck: The Efficient Use of Crowdsourced Labor for Data Annotation. Professor Hirsh was a WORLDCOMP keynote speaker, too. In addition, we hosted tutorials and invited talks held by Peter Geczy on Web Mining, Data Mining and Privacy: Water and Fire?, and Data Mining in Organizations. DMIN'13 hosted the following tutorials: EXTENSIONS and APPLICATIONS of UNIVERSUM LEARNING presented by Vladimir Cherkassky (Dept. Electrical & Computer Eng., University of Minnesota, Minneapolis, USA), Visualization & Data Mining for High Dimensional Datasets presented by Alfred Inselberg, (School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel) as well as invited talks: Big Data = Big Challenges? given by Peter Geczy (National Institute of Advanced Industrial Science and Technology (AIST), Japan) and The Problem of Induction: When Karl Popper meets Big Data given by Vladimir Cherkassky.

DMIN' 16 will host the following tutorials/invited talks (as of March 11):

Invited Talks

Invited Talk A
Speaker Peter Geczy
National Institute of Advanced Industrial Science and Technology (AIST), Japan

Topic/Title Data Science: Where Academia Meets Commerce
Date & Time Tuesday, July 26, 2016 - 01:40-02:40pm
Location Ballroom 1

Exponential expansion of data has significantly contributed to notable changes in business and academic environments. Data has been growing in both volume and diversity. Numerous organizations have been actively involved in generation, collection and utilization of data. Rich data has become the new treasure throw for extraction of actionable knowledge. However, the scale and growth of data considerably outpace technological capacities of organizations to properly process and manage it. An increasing gap between the data expansion and technological means to cope with it presents new challenges. Data Science has emerged as an interdisciplinary endeavor to tackle data related challenges. Recent developments highlight the pressing need for closer alignment between academia and businesses. We shall explore approaches and trends at the intersection of academic and commercial interests in data science.

Short Bio

Dr. Peter Geczy holds a senior position at the National Institute of Advanced Industrial Science and Technology (AIST). His recent research interests are in information technology intelligence. This multidisciplinary research encompasses development and exploration of future and cutting-edge information technologies. It also examines their impacts on societies, organizations and individuals. Such interdisciplinary scientific interests have led him across domains of technology management and innovation, data science, service science, knowledge management, business intelligence, computational intelligence, and social intelligence. Dr. Geczy received several awards in recognition of his accomplishments. He has been serving on various professional boards and committees, and has been a distinguished speaker in academia and industry. He is a senior member of IEEE and has been an active member of INFORMS and INNS.


Invited Talk B
Speaker Gary M. Weiss, Associate Professor & Director of Wireless Sensor Data Mining (WISDM) Lab, Dept. of Computer and Information Science, Fordham Univesity, Bronx, NY, USA

Topic/Title Mining Smartphone and Smartwatch Sensor Data: Activity Recognition, Biometrics, and Beyond
Date & Time Monday, July 25, 2016 - 01:40-02:40pm
Location Ballroom 1
Smartphones have become ubiquitous and smartwatches are increasing in popularity. Both of these mobile devices contain an accelerometer and gyroscope that can describe their user's motion. In this talk I will describe data mining research conducted in my WIreless Sensor Data Mining (WISDM) lab that exploits these capabilities to identify what a user is doing (activity recognition), to identify/authenticate a user (biometrics), and to diagnose problems with a user's gait. I will conclude with a discussion of the future of mobile and wearable sensor mining applications.
Short Bio
Gary Weiss is an associate professor in the department of Computer and Information Science at Fordham University in New York City. He is the director for the Master's degree program in Computer Science, as well as the director of the Wireless Sensor Data Mining (WISDM) Lab. The WISDM Lab explores how smartphones, smartwatches, and other mobile sensors can be used to support human activity recognition, biometrics, and other sensor-based applications. His work is funded by the US National Science Foundation, Google, and several other industry partners. Prior to coming to Fordham, Dr. Weiss worked at AT&T Labs as a software engineer, expert system developer, and finally as a data scientist. He has published over fifty papers in machine learning and data mining.



Tutorial A
Speaker Diego Galar, Division of Operation and Maintenance Engineering,
Luleå University of Technology, 971 87 Lulea, Sweden
, diego.galar@ltu.se


Industrial Big Data: The door to prescriptive analytics

Date & Time Monday, July 25, 2016 - 06:00-07:30pm
Location Ballroom 1

Industrial systems are complex with respect to technology and operations with involvement in a wide range of human actors, organizations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems, such as computerized control systems, or advanced monitoring and diagnostic systems. Moreover, assets cannot compromise the safety of the users by applying operation and maintenance activities. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”.

Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining.

This tutorial discusses the possibilities that lie within applying the maintenance 4.0 concept in the industry and the positive effects on technology, organization and operations from a systems perspective.

The way of presenting the state of the art of Industrial big data and its benefits will be as a case oriented tutorial where success stories from different sectors will be presented and exemplified by applying it on industrial, transportation and infrastructure assets.

The tutorial is recommended for researchers and practitioners who are interested in the development of Industrial Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualization techniques.

Short Bio Prof. Diego Galar holds a M.Sc. in Telecommunications and a PhD degree in Design and Manufacturing from the University of Saragossa. He has been Professor in several universities, including the University of Saragossa or the European University of Madrid, researcher in the Department of Design and Manufacturing Engineering in the University of Saragossa, researcher also in I3A, Institute for engineering research in Aragon, director of academic innovation and subsequently pro-vice-chancellor.

He has authored more than two hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences.

In industry, he has been technological director and CBM manager of international companies, and actively participated in national and international committees for standardization and R&D in the topics of reliability and maintenance.

Currently, he is Professor of Reliability and Maintenance in Skovde University, holding the VOLVO chair for maintenance, and Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology, where he is coordinating several EU-FP7 projects related to different maintenance aspects, and was also involved in the SKF UTC center located in Lulea focused in SMART bearings. He is also actively involved in national projects with the Swedish industry and also funded by Swedish national agencies like Vinnova.

In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA), currently, University of Sunderland (UK) and University of Maryland (USA). He is also guest professor in the Pontificia Universidad Católica de Chile.
Tutorial B
Speaker Ulf Johansson, Department of Computer Science and Informatics, Jönköping University, Sweden, ulf.johansson@ju.se


Predicting with Confidence

Date & Time Tuesday, July 26, 2016, 06:00-08:00pm
Location Ballroom 1

How good is your prediction? In risk-sensitive applications, it is crucial to be able to assess the quality of a prediction, but traditional classification and regression models don't provide their users with any information regarding prediction trustworthiness.
Conformal predictors, on the other hand, are predictive models that associate each of their predictions with a precise measure of confidence. Given a user-defined significance level E, a conformal predictor outputs, for each test pattern, a multivalued prediction region (class label set or real-valued interval) that, under relatively weak assumptions, contains the test pattern’s true output value with probability 1-E. In other words, given a significance level E, a conformal predictor makes an erroneous prediction with probability E. The conformal prediction framework allows any traditional classification or regression model to be transformed into a confidence predictor with little extra work, both in terms of implementation and computational complexity.
Some key properties of conformal prediction are:
• We obtain probabilities/error bounds per instance
• Probabilities are well-calibrated: 95% means 95%
• We don't need to know the priors
• We make a single assumption - that the data is exchangeable ~ i.i.d.
• We can apply it to any machine learning algorithm
• It is rigorously proven and straightforward to implement
• There is no magic involved – only mathematics and algorithms
Hence, confidence predictors is an important tool that every data scientist should carry in their toolboxes, and conformal prediction represents a straight-forward way of associating the predictions of any predictive machine learning algorithm with confidence measures.
This tutorial aims to provide an introduction and an example-oriented exposition of the conformal prediction framework, directed at machine learning researchers and professionals. A publicly available Python library, developed by one of the authors of the tutorial, will be used for the running examples. The goal of the tutorial is to provide attendees with the knowledge necessary for implementing functional conformal predictors, and to highlight current research on the subject.

Short Bio Prof. Ulf Johansson holds a M.Sc. in Computer Engineering and Computer Science from Chalmers University of Technology, and a PhD degree in Computer Science from the Institute of Technology, Linköping University, Sweden.
Ulf Johansson’s research focuses on developing machine learning algorithms for data analytics. Most of the research is applied, and often co-produced with industry. Application areas include drug discovery, health science, marketing, high-frequency trading, game AI, sales forecasting and gambling. In 2011, he had his 15 minutes of fame when called as an expert witness in the Swedish Supreme Court regarding whether Poker is a game of skill or chance. In the court, Prof. Johansson argued that skill predominates over chance using, among other sources, his paper “Fish or Shark – Data Mining Online Poker”, originally presented at DMIN 2009.
Ulf Johansson has published extensively in the fields of artificial intelligence, machine learning, soft computing and data mining. He is also a regular program committee member of the leading conferences in computational intelligence and machine learning. During the last few years, Prof. Johansson has published several papers on conformal prediction, some presented in top-tier venues like the Machine Learning journal and the ICDM conference.










Robert Stahlbock
General Conference Chair

E-mail: conference-chair@dmin-2016.com

Robert Stahlbock. Sven F. Crone, Gary M. Weiss

Programme Co-Chairs

E-mail: programme-chair@dmin-2016.com


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