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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 |
|
Description |
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 |
|
Description |
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. |
Tutorials
|
Tutorial A |
|
Speaker |
Diego Galar,
Division of Operation and Maintenance
Engineering,
Luleå University of Technology, 971 87 Lulea,
Sweden,
diego.galar@ltu.se |
 |
|
Topic/Title |
Industrial Big Data: The door to prescriptive
analytics |
|
Date & Time |
Monday, July 25, 2016 - 06:00-07:30pm |
|
Location |
Ballroom 1 |
|
Description |
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 |
 |
|
Topic/Title |
Predicting with Confidence |
|
Date & Time |
Tuesday, July 26, 2016, 06:00-08:00pm |
|
Location |
Ballroom 1 |
|
Description |
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. |
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