News

5th BigDAS (2017)

Dr. Carson K. Leung serves as a Program Chair for the Fifth International Conference on Big Data Applications and Services (5th BigDAS) held November 23-25, 2017, as part of Asia Data Week (ADW 2017), in Jeju (濟州), South Korea.


IEEE SC2-2017

Dr. Carson K. Leung serves as the Demo/Poster Chair for the Seventh IEEE International Symposium on Cloud and Service Computing (SC2-2017) held November 22-25, 2017 in Kanazawa (金沢), Japan.


INCoS 2017

Dr. Carson K. Leung serves as a Track Chair for the Ninth International Conference on Intelligent Networking and Collaborative Systems (INCoS 2017) held August 24-26, 2017 in Toronto, ON, Canada. He oversees the Data Mining, Machine Learning and Collective Intelligence track.


BigDAS 2017

Dr. Carson K. Leung serves as a Program Chair for the Fourth International Conference on Big Data Applications and Services (BigDAS 2017) held August 15-18, 2017 in Tashkent, Uzbekistan.


EDB 2017

Dr. Carson K. Leung serves as a Tutorial Chair for the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory (EDB 2017) held August 7-9, 2017 in Busan (釜山), South Korea.


EDB 2017 Invited Talks

  1. Dr. Carson Leung was also invited to be an Invited Speaker at the Seventh International Conference on Emerging Databases: Technologies, Applications, and Theory (EDB 2017) to give the following two talks:
    • On Monday, August 07, 2017, he gave the Invited Talk 1 on "Data analytics of social network data: mining of the 'following' patterns from social networks".
    • On Tuesday, August 08, 2017, he gave the Invited Talk 2 on "Data and visual analytics for emerging databases".

  2. Invited Talks
  3. Data and Visual Analytics for Emerging Databases

    Carson Leung, Ph.D
    Senior member of ACM and IEEE
    Professor
    University of Manitoba
    Canada

    Abstract

    With advances in technology, high volumes of valuable data of different veracity can be generated at a high velocity in wide varieties of data sources in various real-life applications. As a popular data mining tasks, frequent pattern mining discovers implicit, previously unknown and potentially useful knowledge in the form of sets of frequently co-occurring items or events. Many existing data mining algorithms return to users with long textual lists of frequent patterns, which may not be easily comprehensible. Given a picture is worth a thousand words, having a visual means for humans to interact with computers would be beneficial. This talk presents a system for data and visual analytics for emerging databases.

    Biography

    Carson Leung is currently a Full Professor at the University of Manitoba, Canada. He obtained his BSc(Hons), MSc and PhD from the University of British Columbia, Canada. He has published more than 170 papers on the topics of databases, data mining, big data computing, social network analysis, as well as visual analytics--including papers in ACM Transactions on Database Systems (TODS), Social Network Analysis and Mining (SNAM), Future Generation Computer Systems (FGCS), Journal of Organizational Computing and Electronic Commerce (JOCEC), IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM), and Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Over the past few years, he has served as (i) a General Chair of IEEE CBDCom 2016, (ii) a Program Chair of IEEE HPCC 2016 and BigDAS 2017, (iii) an organizing committee member of ACM SIGMOD 2008, IEEE ICDM 2011, IEEE/ACM ASONAM 2014, and IEEE BigComp 2017, as well as (iv) a PC member of numerous international conferences including ACM KDD, ACM CIKM, and ECML/PKDD. He is a senior member of the ACM and of the IEEE.


Expert Systems with Applications 79 (August 2017)

Ashis Kumar Chanda, Chowdhury Farhan Ahmed, Md. Samiullah, Carson K. Leung:
A new framework for mining weighted periodic patterns in time series databases.
Expert Systems with Applications 79:
207-224 (August 2017)

Highlights

  • Developing a new weight-based framework for periodic pattern mining.
  • Devising an efficient weighted periodic pattern mining algorithm with suffix trie.
  • Different pruning strategies are introduced to accelerate the performance.
  • Capable of mining symbol, partial, full-cycle periodicity in a single run.
  • The results on real datasets show efficiency and effectiveness of our approach.


HPCS-ISE 2017

Dr. Carson K. Leung serves as a Program Chair for the International Symposium on Information Systems and Engineering (ISE 2017) held as part of the 15th International Conference on High Performance Computing & Simulation (HPCS 2017) on July 17-21, 2017 in Genoa, Italy.


ACM WIMS 2017

Dr. Carson K. Leung serves as a Publicity Chair for the Seventh ACM International Conference on Web Intelligence, Mining and Semantics (WIMS'17) held June 19-22, 2017 in Amantea, Italy.


Co-op Success 2017

Two lab members, Caitlin S. Martins and H. Bryan Wodi, were profiled by UofM CS Co-op Office in Celebrating Rockstars - Co-op Success Summer 2017.


USRA 2017

Several lab members won undergraduate student research awards:
  • Third-year undergraduate student Henry Bryan Wodi, who is also enrolled in the B.Sc.(Maj.) co-op program with major in CS and minor in both economics & statistics, won a Faculty of Science Undergraduate Student Research Award (USRA) to conduct a full-time 16-week research project in the area of data mining under the academic supervision of Dr. Carson K. Leung. His fellow students, Katharine A. King (a fourth-year B.C.Sc.(Hons.) co-op student) and Ye Yuan also won this award.
  • Fourth-year undergraduate student Ye Yuan, who is enrolled in the B.C.Sc.(Hons.) co-op program, won a UofM Vice-President (Research and International) Undergraduate Research Award (URA) to conduct a full-time 16-week research project in the area of data mining (big data science) under the academic supervision of Dr. Carson K. Leung. Among ~25,000 undergraduate students across the campus, he was one of 102 winners of this award. His fellow students, Chenxi Fan (a third-year B.Sc.(Hons.) student with a joint major in CS & statistics and minor in management) and Katharine A. King also won this award.
  • Fourth-year undergraduate student Zhida Zhang (章志达) won a MITACS Globalink award to conduct a full-time 12-week research project on "Mining useful information from social networks" in the area of data mining (social network mining) under the academic supervision of Dr. Carson K. Leung.
  • Fourth-year student Pranjal Gupta, who is enrolled in both B.E.(Hons.) degree program in computer science and M.Sc.(Hons.) degree program in mathematics, won a MITACS Globalink award to conduct a full-time 12-week research project on "Visual analytics of interesting data and knowledge" in the area of data science (visual analytics) under the academic supervision of Dr. Carson K. Leung.


OBDC (2017)

Joan Lu, Qiang Xu (Eds.):
Ontologies and Big Data Considerations for Effective Intelligence.
(April 2017)
ISBN 978-1-5225-2058-0

Detailed Table of Contents (pp. vii-xiii??)

Section 1
Big Data Considerations and Data Technologies

High volumes of a wide variety of data can be easily generated at a high velocity in many real-life applications. Implicitly embedded in these big data is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for either retrieving information about the data or mining the data to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. However, many of the existing visualizers were not designed to visualize these mined frequent sets. This book chapter presents an interactive next-generation visual analytic system. The system enables the management, visualization, and advanced analysis of the original data and the frequent sets mined from the data.

Preface (pp. xv-xx) [igi]

Chapter 1 presents an investigation into interactive visual analytics of big data. High volumes of a wide variety of data can be easily generated at a high velocity in many real-life applications. Implicitly embedded in these big data is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for either retrieving information about the data or mining the data to find frequent sets, which are usually presented in a lengthy textual list. As "a picture is worth a thousand words", the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. However, many of the existing visualizers were not designed to visualize these mined frequent sets. This book chapter presents an interactive next-generation visual analytic system. The system enables the management, visualization, and advanced analysis of the original data and the frequent sets mined from the data.

About the Contributors (pp. 626-630) [igi]

Christopher L. Carmichael received his B.C.Sc. (Hons.) and M.Sc. degrees, both from University of Manitoba, Canada, under the academic supervision of Prof. Carson K. Leung. Before that, Carmichael earned his diploma in mechanical engineering technology from Red River College, Canada, and spent a long career in designing and programming commercial control systems for building heating/air conditioning ventilation systems. Carmichael is currently conducting research in the areas of data mining, data visualization, and visual analytics, as well as prototyping private P2P network systems for audio, video, email and webpages with use of low-powered computers like Raspberry Pi.

Patrick Johnstone received his B.Sc. degree—with major in computer science—from the University of Manitoba, Canada. During his study, Johnstone acquired research experience in the areas of data mining and visual analytics under the academic supervision of Prof. Carson K. Leung. Since graduation, Johnstone has been working as a software developer focused on distributed computing and system integration for a company offering software solutions for to the telecommunication sector. Recently, he has moved to a new role as a technical business analyst focused on in-depth analysis and system design in the same company in Winnipeg, Canada.

Carson K. Leung received his B.Sc. (Hons.), M.Sc., and Ph.D. degrees all from the University of British Columbia, Vancouver,Canada. He is currently a Professor at the University of Manitoba,Canada. He has contributed more than 150 refereed publications on the topics of big data analytics, databases, data mining, information retrieval,social network analysis, as well as visual analytics---including papers in ACM Transactions on Database Systems (TODS), Future Generation Computer Systems (FGCS), Journal of Organizational Computing and Electronic Commerce, Social Network Analysis and Mining, World Wide Web Journal (WWW), IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM), the SCA 2012 Best Paper on social computing and its applications, the IEEE/ACM ASONAM-FAB 2016 Best Paper on foundations and applications of big data analytics, as well as five book chapters and encyclopedia entries for IGI Global.

Roy Ruokun Xing received his B.Sc. degree—with major in computer science specialized in human computer interaction (HCI)—from the University of Manitoba, Canada. During his study, Xing acquired research experience in the areas of data mining and visual analytics under the academic supervision of Prof. Carson K. Leung. Currently, Xing is working as a software developer in oil and gas industry in Calgary, Canada. Xing is interested in the software and database development, with a focus on user interface and database structure design.

David Sonny Hung-Cheung Yuen received his B.Sc. degree with a major in computer science specialized in databases, human-computer interaction (HCI) and software engineering from the University of Manitoba, Canada. During his study, Yuen acquired research experience in the areas of data mining and visual analytics under the academic supervision of Prof. Carson K. Leung. Currently, Yuen is working for IBM Canada Ltd in Toronto, Canada.


SMIEEE

IEEE

In recognition of professional standing
the Officers and Board of Directors of the IEEE certify that

Carson K. Leung

has been elected to the grade of

Senior Member

The Institute of Electrical and Electronics Engineers, Inc.
Founded New York 1884

22 April 2017

Karen Bartieson
President, IEEE

William Waish
Secretary, IEEE


SMACM

acm Association for
Computing Machinery
Advancing Computing as a Science & Profession

This is to certify that

Carson K.S. Leung

has been honored with the designation of

ACM SENIOR MEMBER

March 3, 2017
Date
Vicki L. Hanson
ACM President
Vinton Cerf
Awards Committee Co-Chair
John R. White
Awards Committee Co-Chair


UofM VP(RI) URA

Researcher's Lists - Computer Science

Dr. Carson Leung
Professor, UofM Computer Science
Email: Carson.Leung@cs.umanitoba.ca

Research within the Database and Data Mining Laboratory in Department of Computer Science at the University of Manitoba focuses on databases and data mining, which includes efficient and effective management of, knowledge discovery from, as well as analysis of, large amounts of data (such as transactional, uncertain, social media, Web, graphs, data streams, rich data, and/or Big Data). Current and past research programs have been focused on Big data science, data mining, data analytics, visual analytics, data warehousing and OLAP (on-line analytical processing), and applications of database and data mining technologies to areas such as bioinformatics, health informatics and social network mining. Our lab members--including former winners of this URA, as well as Science & NSERC USRA--have been actively designed efficient and effective algorithms for finding frequently occurring patterns (say, merchandise items frequently purchased together by customers) or detecting exceptional or abnormal items (say, detect malfunction devices). The resulting algorithms have been applied to various real-life applications.


IEEE BigComp 2017

Dr. Carson K. Leung serves as a Web Chair for the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp 2017) held February 13-16, 2017 in Jeju Island, South Korea.


Jiang's and Pazdor's Teaching

  1. Adam G. Pazdor, a graduate student in our lab, teaches:
    • COMP 1012 A03 (Computer Programming for Scientists and Engineers) in Winter 2017 on every Tuesday and Thursday at 2:30pm-3:45pm from January 18 to April 21, 2017;
    • COMP 1012 A01 in Summer 2017 on every Monday and Wednesday at 7pm-8:15pm the coming summer from May 01 to August 02, 2017.
    • COMP 1012 A02 in Fall 2017 on every Monday, Wednesday & Friday at 12:30pm-1:20pm from September 08 to December 08, 2017.
    • COMP 3380 A02 (Databases: Concepts and Usage) also in Fall 2017 on every Tuesday and Thursday at 8:30am-9:45am from September 07 to December 07, 2017.
    By the end of Fall 2017, Pazdor has taught a total of six sections of two distinct courses (COMP 1012 four times and COMP 3380 twice).
  2. Dr. Fan Terry Jiang, an alumnus from our lab, teaches COMP 1010 A01 (Introductory Computer Science 1) in Summer 2017 on every Tuesday and Thursday at 7pm-8:15pm from May 02 to August 03, 2017. By the end of Summer 2017, Jiang has taught a total of seven sections of four distinct courses (two distance & online education-based offerings and one lecture-based offering of COMP 1010, lecture-based COMP 1270 twice, as well as lecture-based COMP 2150 and 4380 once each).


CF (2017)

Vishal Bhatnagar (Ed.):
Collaborative Filtering Using Data Mining and Analysis.
(July 2016)
ISBN 978-1-5225-0489-4

Detailed Table of Contents (pp. vii-xiii??)

Section 3
Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering

Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable information to users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.

Preface (pp. xvi-xxiii) [igi]

In chapter 9 Prof. Carson K. Leung, Fan Jiang, Edson M. Dela Cruz and Vijay Sekar Elango presents that Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.

About the Contributors (pp. 302-306) [igi]

Edson M. Dela Cruz is currently a student in the Department of Computer Science at the University of Manitoba, Canada. He won a Faculty of Science Undergraduate Student Research Award (USRA) to conduct full-time research in the area of data mining under the academic supervision of Prof. Leung. Dela Cruz is interested in the research area of data mining with a focus on association rule mining and collaborative filtering.

Vijay Sekar Elango is currently a student in the School of Computer Science and Engineering at the VIT University, Vellore, India. He won a MITACS Globalink Research Internship award to conduct full-time research in the Department of Computer Science at the University of Manitoba, Canada, in the area of data mining under the academic supervision of Prof. Leung. Elango is interested in the research area of data mining with a focus on collaborative filtering and data analytics.

Fan Jiang received his B.C.Sc. (Hons.) and M.Sc. degrees from the University of Manitoba, Canada. He is currently a Ph.D. candidate in the Department of Computer Science at the same university under the academic supervision of Prof. Leung. Jiang is interested in conducting research in the area of data mining with a focus on association rule mining, collaborative filtering, and social network analytics.

Carson Leung received his B.Sc. (Hons.), M.Sc., and Ph.D. degrees all from the University of British Columbia, Vancouver, Canada. He is currently a Professor at the University of Manitoba, Canada. He has contributed more than 130 refereed publications on the topics of big data analytics, databases, data mining, social network analysis, as well as visual analytics—including papers in ACM Transactions on Database Systems (TODS), Future Generation Computer Systems (FGCS), Journal of Organizational Computing and Electronic Commerce, Social Network Analysis and Mining, IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM), the SCA 2012 Best Paper on social computing and its applications, as well as four chapters for IGI Global, published in books & encyclopedias.