News (2017)

News (2017)

COMP 3380 (Fall 2017)

Bryan Wodi was one of four TAs for this course for about 192 students, he "marked student's midterms and assignments and returned them by the next class." He listed two interesting facts on https://talk2bryan.github.io/teaching/2017-fall-ta-2: (a) "This course records the fastest feedback time on assignments and tests in the entire department.", and (b) He "received an honorable mention from the Faculty of Science for Excellence in Teaching Assistance."

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.

David Kirkpatrick's Visit

On Thursday, November 23, 2017, Dr. David G. Kirkpatrick (a Professor Emeritus of Computer Science at UBC) gave a UofM CS seminar on "Minimizing uncertainty in the proximity of moving agents". Kirkpatrick is a Fellow of the Royal Society of Canada (FRSC).

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.

LabTREK 2017

On Wednesday, September 06, 2017, Dr. Carson K. Leung shared his research and inspired the next group of science students in the First Annual LabTREK Research Open House event co-organized by Science Student Association (SSA) and Faculty of Science at University of Manitoba. The event helps undergraduate students to discover scientific breakthroughs of tomorrow and explore undergraduate student research opportunities.

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. Proceedings are published as part of the Lecture Notes on Data Engineering and Communications Technologies book serie (LNDECT) by Springer.

BigDAS 2017

    1. 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.

    2. Wookey Lee, Carson K. Leung:

    3. Preface.

    4. Big Data Applications and Services 2017: The 4th International Conference on Big Data Applications and Services:

    5. vii-ix

      1. Preface

      2. The International Conference on Big Data Applications and Services (BigDAS) aims to address the need of the academic community and industry about Big Data. It encourages academic and industrial interaction and promotes collaborative research in Big Data applications and services by bringing together academics, government, and industry professionals to discuss recent progress and challenges in Big Data applications and services. Moreover, BigDAS also serves as a platform for theoreticians and practitioners to exchange their original research ideas on academic or industrial aspects of Big Data applications and services, present their new findings or innovative results on theoretical or practical aspects of Big Data, share their experiences on integrating new technologies into products and applications, discuss their work on performing Big Data applications and services in real-life situations, describe their development and operations of challenging Big Data related systems, and identify unsolved challenges.

      3. Since the First International Conference on Big Data Applications and Services (1st BigDAS 2015), three BigDAS conferences have been held in the following venues:

        • 1st BigDAS 2015: Seogwipo KAL Hotel, Jeju Island, South Korea, on October 20–23, 2015;

        • 2nd BigDAS 2016: Korea Software HRD Center, Phnom Penh, Cambodia, on January 22–27, 2016; and

        • 3rd BigDAS-L 2016: Landmark Mekong Riverside Hotel, Vientiane, Laos, on December 20–23, 2016.

    6. With their success, the Fourth International Conference on Big Data Applications and Services (4th BigDAS 2017) is held in the following venues:

        • 4th BigDAS 2017: National University of Uzbekistan, Tashkent, Uzbekistan, on August 15–18, 2017.

    7. The BigDAS 2017 was held in Tashkent, Uzbekistan (which is located in the center of Great Silk Road). The conference was hosted by (i) The Korea Big Data Service Society and (ii) National University of Uzbekistan. It was organized by (i) Bigdata Research Institute, Chungbuk National University; (ii) New Industrial Convergence R&D Center, Ajou University; (iii) Inha University in Tashkent; and (iv) Korea China Yeouido Leaders Forum. It was sponsored by (i) Electronics and Telecommunications Research Institute (ETRI); (ii) UNISEM Co., Ltd.; and (iii) WIZCORE, Inc. The program consists of the following events:

        • Four keynote speeches on big data in industry and government:

            • "The Fourth Industrial Revolution and New Industry Development Directions" by Marn-ki Jeong (ex-First Vice Minister of the Ministry of Trade, Industry and Energy, South Korea)

            • "Prediction Markets a Computational Mechanism for Aggregating Information" by Sarvar Abdullaev (Inha University in Tashkent, Uzbekistan)

            • "Big Data Approach and Challenges in Government" by Myoung Hee Kim (President of National Computing and Information Service (NCIS), Ministry of Interior, South Korea)

            • "Initiation of an Innovation Ecosystem: Uzbekistan's Road Towards Building Silicon Valley" by Bokhodir Ayupov (Ministry of Information Technologies and Communications, Uzbekistan)

        • eight technical sessions with 54 regular and short paper presentations on the following topics: (i) Big Data models and algorithms, (ii) Big Data architectures, (iii) Big Data applications, (iv) Big Data mining and analysis, (v) Big Data security, (vi) Big Data visualization, (vii) social network analysis, and (viii) Internet of things (IoT) and health care;

        • three poster sessions with eight poster paper presentations on the following topics: (i) Big Data applications, (ii) Big Data in industry, and (iii) Big Data in business; and

        • two workshops: (i) E-government and Big Data, and (ii) Big Data composition.

    8. For BigDAS 2017, we have recruited many international experts in Big Data applications and services to join our team of international committee. As a result, our Committee consists of professionals from different parts of the world such as Bangladesh, Canada, China, Japan, South Korea, and Uzbekistan. This committee has done an excellent job in organizing the conference and selecting a collection of 54 high-quality regular and short papers, as well as 8 poster papers. Among these papers, a small number of them were selected and revised to be included in the current volume.

      1. BigDAS 2017 would not have been possible without the help and effort of many people and organizations. We thank BigDAS 2017 Organizing Committee members, especially Honorary Co-chairs (S. H. Han and N. Yusupbekov), General Co-chairs (J.-Y. Lee and A. R. Marakhimov), and Organizing Co-chairs (K.-H. Yoo, W.-S. Cho, and Y.-S. Kim) for their valuable advice and suggestions toward the conference. We are grateful to other members for their professionalism and dedication in different aspects of this conference, including the selection of papers presented at the conference and the further selection of papers published in the current volume. We also express our thanks to authors and non-author participants of this conference. We also thank A. Nasridinov for local arrangement. Last but not least, we thank the staff at Springer for their help in publishing the current volume.

            1. Incheon, South Korea

            2. Winnipeg, Canada

            3. August 2017

            1. Wookey Lee

            2. Program Co-chair of BigDAS 2017

            3. Carson K. Leung

            4. Program Co-chair of BigDAS 2017

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".

  1. Invited Talks

    1. Data and Visual Analytics for Emerging Databases

          1. Carson Leung, Ph.D

          2. Senior member of ACM and IEEE

          3. Professor

          4. University of Manitoba

          5. Canada

    1. Abstract

    2. 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.

    3. Biography

    4. 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.

    5. Sungwon Jung, Min Song: Preface. EDB 2017: v-vi

      1. Preface

      2. In addition to the oral and poster sessions, the technical program has provided one keynote speech by Dr. Mukesh Mohania (IBM Academy of Technology, Australia), two invited talks by Prof. Alfredo Cuzzocrea (University of Trieste, Italy) and Prof. Carson Leung (University of Manitoba, Canada), and one tutorial by Prof. Jae-Gil Lee (KAIST, Republic of Korea).

      3. Sungwon Jung

      4. Min Song

      5. Program Committee Co-chairs

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. Proceedings are published by the IEEE.

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 Mr. 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, Ms. Katharine A. King (a fourth-year B.C.Sc.(Hons.) co-op student) and Mr. Ye Yuan were also selected as winners of this award.

    • Fourth-year undergraduate student Mr. 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 on "Design of big data science solution for data mining and analytics" 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, Ms. Chenxi Fan (a third-year B.Sc.(Hons.) student with a joint major in CS & statistics and minor in management) and Ms. Katharine A. King were also selected as winners of this award.

    • The following two students each won a MITACS Globalink award to conduct a full-time 12-week research project in the area of data mining under the academic supervision of Dr. Carson K. Leung:

      • Fourth-year undergraduate student Mr. Zhida Zhang (章志达), from Tongji University (同济大学), conducted a research project on "Mining useful information from social networks"; and

      • Fourth-year student Mr. Pranjal Gupta, who is enrolled in both B.E.(Hons.) degree program in CS and M.Sc.(Hons.) degree program in Math from Birla Institute of Technology and Science, Pilani (BITS Pilani), conducted a research project on "Visual analytics of interesting data and knowledge".

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 (2017)

Dr. Carson K. Leung has been elected to the grade of Senior Member of the Institute of Electrical and Electronics Engineers, Inc. (IEEE) on 22 April 2017.

SMACM (2017)

Carson K.S. Leung has been honored with the designation of ACM Senior Member on March 3, 2017.

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 in E3-270 EITC from January 19 to April 20, 2017;

      • COMP 1012 in Summer 2017 on every Monday and Wednesday at 7pm-8:15pm in E2-320 EITC from May 01 to August 02, 2017.

      • COMP 1012 A02 in Fall 2017 on every Monday, Wednesday & Friday at 12:30pm-1:20pm in Fletcher Argue 100 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 in E2-165 EITCfrom September 07 to December 07, 2017, whereas A01 is taught by Dr. Leung.

  1. 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).

    1. 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 in Armes 115 from May 02 to August 01, 2017. By the end of Summer 2017, Jiang has taught a total of seven sections of four distinct courses (distance & online education-based offering of COMP 1010 in both Fall 2014 & Fall 2015; lecture-based offering of COMP 1010 in Summer 2017; COMP 1270 in both Winter 2015 & Winter 2016; COMP 2150 in Summer 2015; and COMP 4380 in Winter 2016).

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.