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  DMIN'14 Programme
 DMIN'14 Tutorials
 DMIN'14 Special Sess.
 WORLDCOMP'14
 

Tutorial Sessions/Invited Talks

All tutorials and invited talks are free to registered conference attendees of all conferences held at WOLDCOMP'13. 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'14 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'14 will be hosting the following tutorials/invited talks:

Tutorials

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

Topic/Title Smartphone Sensor Mining Applications: Ubiquitous Possibilities
Date & Time July 23, 2014 (Wednesday) - 5:40pm
Location Ballroom 1
Description

Smart phones have exploded in popularity over the past half dozen years and these devices are now not just ubiquitous, but powerful and packed with sensors such as an accelerometer, gyroscope, GPS, compass, barometer, audio sensor (microphone), image sensor (camera), and light sensor. We are now also beginning to see these sensors migrate to smartwatches, which are primed to explode in popularity. These mobile sensors make exciting new applications possible. In this tutorial I will survey some of these sensor mining applications, discuss the underlying technology and technology challenges, and describe the role of data mining in these applications. The new breed of applications that have recently hit the market will allow our mobile devices to become truly intelligent and context-aware, and allow them to learn a lot about us and our immediate environment. This tutorial is intended as an introduction to the area and is appropriate for anyone who is interested in the area.

Short Bio

Gary Weiss is a faculty member in the department of Computer and Information Science at Fordham University in New York City. He is the Director of the Wireless Sensor Data Mining (WISDM) Lab, which explores how smartphone and other mobile sensors can be used to support human activity recognition and related applications. The WISDM Lab, which is supported by grants from the U.S. National Science Foundation, Google, and several other corporations, recently released the actitracker activity tracking app (actitracker.com). 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 received a B.S. degree in Computer Science from Cornell University, an M.S. degree in Computer Science from Stanford University, and a Ph.D. degree in Computer Science from Rutgers University. He has published over fifty papers in machine learning and data mining.

Invited Talks

Invited Talk A
Speaker Diego Galar, Division of Operation and Maintenance Engineering,
LuleŚ University of Technology, 971 87 Lulea, Sweden

Topic/Title Big Data Issues for Mining Knowledge in Maintenance Information Systems
Date & Time July 22, 2014 (Tuesday), 12:00-01:00pm
Location Ballroom 1
Description

ABSTRACT
Maintenance is a strategic process and/or service all over the word. The effective monitoring of the assets is a key task in order to guarantee efficient and safe exploitation. Current assets, with plenty of sensors' already installed and pervasive computing on them generate a huge of data along their day‐today working as well as their maintenance. In this scenario, asset managers host a large number of diverse systems where data, regarding different aspects of their activity, are stored. In most cases, these data are captured, stored and processed by different - often incompatible systems -, and further managed by independent departments and not shared at all.

In addition, it is fairly frequent that while large amounts of data are gathered only a small fraction of it is used for a specific purpose; the remainder is simply saved, or even worse, discarded. Important information and knowledge are buried within those extends of data. They could be discovered if the data were properly organized and processed. A number of unused techniques and paradigms in Information Technology allow for this knowledge discovery. On the one hand, Cloud Computing brings a new service delivery paradigm allows for pay-as-you-go services, which adapt to the customer needs, without requiring expensive data centers' infrastructures. From the customer point of view, Cloud Computing brings the possibility of requesting computing resources, storage, and network bandwidth - as needed. This paradigm can be used both from public services or even within‐house private services.

On the other hand, Data Mining techniques, developed along fifteen years have allowed the discovery of non‐trivial knowledge from large databases. As the computing resources increase in their power and decrease in their prices, the capture and storage of data are becoming increasingly affordable leading to huge Big Data techniques to improve asset monitoring and management stores of data. This is increasing in several dimensions, not just size but also, variety - structure, semi-structured, non-structured -, speed of gathering, ... to face their management and processing new computing techniques are required.
They are all included under a new term: Big Data. It refers to systems, algorithms, and procedures suitable to process data sets, which largely overcome the capacity of current single computers. Big Data is one-term drawing attention of many companies and institutions all over the world. Most organizations are speeding up their data processing strategies towards Big Data. This means a clear recognition by industry, agencies and public institutions. That is why, the goal of this talk is to address the current challenges of big data in maintenance; i.e. the analysis, design and implementation of systems that allow for the effective exploitation of data for asset managers.

[see also Special Session on Data Mining Applications for Advanced Manufacturing [more]]

Short Bio Prof. Diego Galar has a Msc in Telecommunications and a PhD degree in Manufacturing from the University of Saragossa. He has become Professor in several universities, including the University of Saragossa or the European University of Madrid. He also was a senior researcher in I3A, Institute for engineering research in Aragon, director of academic innovation and subsequently pro-vice-chancellor of the university.

In industry, he has been also technological director and CBM manager. He has authored more than hundred journal and conference papers, books and technical reports in the field of maintenance.

Currently, he is Professor of Condition Monitoring in the Division of Operation and Maintenance in LTU, LuleŚ University of Technology, where he is coordinating several EU-FP7 projects related to different maintenance aspects and is also involved in the SKF UTC centre located in Lulea focused in SMART bearings.

He is also visiting Professor in the University of Valencia, Polytechnic of Braganza (Portugal), Valley University (Mexico), Sunderland University (UK) and NIU (USA).

 

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

Topic/Title Data Science Emergence
Date & Time July 23, 2014 (Wednesday), 12:00-01:00pm
Location Ballroom 1
Description Explosion of digital data and its diversity over the past decade has been challenging scientists and practitioners in a range of areas. Vast amounts of data are being produced every day. It is estimated that the growth rate is exponential and no saturation point, or gradual flattening, is expected to occur in a near-future. The present trend indicates that the rapid data expansion will interpenetrate into a broader spectrum of scientific, commercial and social spheres. The necessity to cope with these challenges gave birth to Data Science - an emerging field of scientific and educational endeavor. Data Science is a novel interdisciplinary domain that has been progressively forming over the past decade. It addresses two pressing needs: a coherent scientific approach to rising data related challenges and a growing demand for education of new professionals - data scientists. We shall explore both scientific and professional aspects of these emerging initiatives.
Short Bio Dr. Peter Geczy is with the National Institute of Advanced Industrial Science and Technology (AIST). He also held positions at the Institute of Physical and Chemical Research (RIKEN) and the Research Center for Future Technologies. His interdisciplinary scientific interests encompass domains of data science, human interactions and behavior, social intelligence technologies, privacy, information systems, knowledge management and engineering, artificial intelligence, and adaptable systems. His recent research focus also extends to the spheres of service science, engineering, management, and computing. He received several awards in recognition of his accomplishments. Dr. Geczy has been serving on various professional boards and committees, and has been a distinguished speaker in academia and industry.

 

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DMIN'09DMIN'08

DMIN'07DMIN'06

 

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Contact

Robert Stahlbock
General Conference Chair

E-mail: conference-chair@dmin-2014.com


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

Programme Co-Chairs

E-mail: programme-chair@dmin-2014.com

 

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