News (2015)

News (2015)

Algorithms 8 (2015)

Alfredo Cuzzocrea:

Algorithms for managing, querying and processing big data in cloud environments.

Algorithms 9(1),

special issue on algorithms for managing, querying and processing big data in cloud environments:

article 13 (March 2016)

The third paper [12], entitled "A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments", by Jiang & Leung, considers the problem of mining big data for supporting the discovery of useful information and knowledge. In this context, they propose a data analytic algorithm for managing, querying and processing transactions of uncertain big data in Cloud environments. The proposed framework, based on this algorithm, allows users to query these big data by specifying constraints expressing their interests, and processes the user-specified constraints to discover useful information and knowledge. Due to the fact that each item in every transaction in these uncertain big data is associated with an existential probability value expressing the likelihood of that item to be present in a particular transaction, computation could be intensive. In order to cope with this issue, the proposed algorithm makes use of the MapReduce model in a Cloud environment for effective data analytics on uncertain big data. Experimental results show the effectiveness of the overall solution.

References

• 1. Jiang, F.; Leung, C.K. A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments. Algorithms 2015, 8, 1175-1194. [CrossRef]

COMP 4710 - Data Mining

Happy to hear that students loved my data mining class (COMP 4710) and described it as "definitely one of their favorites and a really useful class"

On November 19-20, 2015, Tik Wai Kiral Poon posted on his Facebook:

"Arrrrr... I really love the data mining class but the 1st day of snow is not so safe.... really hesitate to go to UofM or not...."

The data mining class is "definitely one of my favorites and a really useful class"

On December 10, 2015, he added another post on his Facebook:

"Thanks Carson Leung again for the fun and practical class!"

IEEE iThings 2015

• 1. Dr. Carson K. Leung serves as a Program Chair for the Eighth IEEE International Conference on Internet of Things (iThings 2015), which is collocated with 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS 2015), held on December 11-13, 2015 in Sydney, NSW, Australia.

  2. Hua Wang, Carson Leung, Jian Tang, Mohammad S. Obaidat, Mahmoud Daneshmand, Jianhua Ma, Laurence T. Yang:

  3. iThings 2015 (2015 IEEE International Conference on Internet of Things):

  4. Message from iThings2015 Chairs.

  5. In Jinjun Chen, Laurence T. Yang (Eds.):

  6. IEEE DSDIS 2015:

  7. xxviii

     1. Message from iThings2015 Chairs

     2. The Internet of Things (IoT) brings promising opportunities and challenges. It attracts great attentions, and has important economic and social values. IoT will play a key role in the next generation of information, network, and communication developing. Future IoT may bring us an era of “harmony of man with nature”, which means harmony fusion of Physical-space, Cyber-space, and Social-space. We will witness emancipation not only from onerous labor works brought by machine-human interface, but also from the spatial-temporal constrains in Physical-space, and will achieve substantial benefits from IT development.

     3. The 2015 IEEE International Conference on Internet of Things (iThings2015) will provide a high-profile, leading-edge forum for researchers, engineers, and practitioners to present state-of-art advances and innovations in theoretical foundations, systems, infrastructure, tools, testbeds, and applications for the Internet of Things, as well as to identify emerging research topics and define the future. The iThings2015 is the 8th edition of the successful series, previously held as iThings 2014 (Taipei), iThings 2013 (Beijing, China), iThings 2012 (Besançon, France), iThings 2011 (Dalian, China), IOTS 2010 (Hangzhou, China), MINES 2009 (Hangzhou, China), and MINES 2008 (Chengdu, China).

     4. Each accepted paper was peer reviewed by at least three program committee members. The conferene covers a broad range of topics in the field of Internet of Things. We thank the authors for submitting their work and the members of the iThings2015 program committee for managing the reviews of the papers in such a short time.

     5. We would like to express our gratitude to everyone who participates in this symposium. We hope you enjoy the conference and have a great time in Sydney, Australia.

     6. Program Chairs

     7. Hua Wang, Victoria University, Australia

     8. Carson Leung, University of Manitoba, Canada

     9. Jian Tang, Syracuse University, USA

     10. General Chairs

     11. Mohammad S. Obaidat, Fordham University, USA

     12. Mahmoud Daneshmand, Stevens Institute of Technology, USA

     13. Steering Committee Chairs

     14. Jianhua Ma, Hosei University, Japan (Chair)

     15. Laurence T. Yang, St Francis Xavier University, Canada (Chair)

BigDAS 2015

• 1. Dr. Carson K. Leung serves as a Program Chair for the International Conference on Big Data Applications and Services 2015 (BigDAS 2015) held on October 20-23, 2015 in Jeju Island (濟州), South Korea. The conference proceedings are published by the ACM Press.

  2. Yoo-Sung Kim, Young-Koo Lee, Carson K. Leung:

  3. Message from BigDAS 2015 Program Chairs.

  4. In Carson K. Leung, Aziz Nasridinov (Eds.):

  5. BigDAS 2015:

  6. x

     1. Message from BigDAS 2015 Program Chairs

     2. The 2015 International Conference on Big Data Applications and Services (BigDAS 2015) is held, jointly with the 10th International Conference on Digital Information Management (ICDIM 2015), at Seogwipo KAL Hotel in Jeju Island, South Korea, on October 20th-23rd, 2015. The joint BigDAS 2015/ICDIM 2015 conference is organized by Center of Enterprise Information Systems of Chungbuk National University, and hosted by Korea Big Data Service Society and Korea Institute of Enterprise Architecture.

     3. The BigDAS 2015 conference 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 2105 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.

     4. For BigDAS 2015, we have recruited many international experts in Big Data applications and services to join our team of international program committee. As a result, our Program Committee consists of professionals from different parts of the world including Australia, Canada, China, Egypt, Germany, India, Indonesia, Italy, Japan, Malaysia, Poland, South Korea, Spain, Taiwan, Thailand, and USA. This committee has done an excellent job in completing the single-blind review and on-line double-blind debate processes. The paper selection process was thorough and competitive. Each submission was refereed by at least two reviewers. Among these submissions, we accepted 17 high-quality submissions as full research papers (i.e., an acceptance rate of less than 20%). To allow more researchers to express their opinions and vision on exploring new concepts and research directions, we also include some short papers and posters. This year, we have a rich program—which includes several invited talks, research paper presentations, workshops, and exhibitions—spanning over four days (October 20-23, 2015).

     5. BigDAS 2015 would not have been possible without the help and effort of many people and organizations. We thank Korean Federation of Science and Technology Societies (KOFST), Electronics and Telecommunications Research Institute (ETRI), Korea Institute of Science and Technology Information (KISTI), and many other organizations, for their support of this conference. We also express our thanks to BigDAS 2015 Organizing Committee members, especially the Conference Co-Chairs (J.-S. Choi, S.H. Han, J.-Y. Lee, and T. Park) and Organizing Co-Chairs (E. Ariwa, S.-Y. Chi, W.-S. Cho, A. Florea, S. Fong, S.-J. Kang, S. Lee, W. Lee, P. Pichappan, R. Rodriguez, N. Sadek, and K.-H. Yoo) for their valuable advice and suggestions towards the conference. We are grateful to BigDAS 2015 Program Committee members for their professionalism and dedication in the process of judging the contributions of papers and producing constructive comments to the authors. We also thank authors and non-author participants of this conference. Last but not least, we thank the ACM staff (especially, C. Rodkin and A. Lacson) for their help in publishing the current proceedings.

     6. Yoo-Sung Kim, Inha University, South Korea

     7. Young-Koo Lee, Kyung Hee University, South Korea

     8. Carson K. Leung, University of Manitoba, Canada

BigDAS 2015 Invited Talk

• 1. Dr. Carson Leung was also invited to be an Invited Speaker at the International Conference on Big Data Applications and Services 2015 (BigDAS 2015) to give a talk on "big data mining applications and services". This conference is a part of an international joint conference on Big Data Applications and Services 2015 (BigDAS 2015) and Digital Information Management 2015 (ICDIM 2015).

  2. Plenary Speakers

     1. Invited Speakers

     2. Title: Big data mining applications and services

     3. Carson Leung

     4. Carson.Leung [AT] cs.UManitoba.ca

     5. Department of Computer Science, University of Manitoba

     6. Winnipeg, MB, Canada

     7. Abstract:

     8. Data mining and analytics aims to analyze valuable data and extract implicit, previously unknown, and potentially useful information from the data. Due to advances in technology, high volumes of valuable data are generated at a high velocity in high varieties of data sources in various real-life business, scientific and engineering applications. Due to their high volumes, the quality and accuracy of these data depend on their veracity (uncertainty of data). This leads us into the new era of Big Data. This talk presents some works on big data mining and computing, especially on an important task of frequent pattern mining, which computes and mines from big data for interesting knowledge in the forms of frequently occurring sets of merchandise items in shopping markets, interesting co-located events, and/or popular individuals in social networks. The talk also shows how big data mining contributes to real-life applications and services.

• 1. Carson K. Leung: BigDAS 2015 invited talk: Big data mining applications and services. BigDAS 2016: xxi

     1. BigDAS 2015 Invited Talk

        1. Big Data Mining Applications and Services

        2. Carson K. Leung

        3. Department of Computer Science

        4. University of Manitoba

        5. Winnipeg, MB, Canada

        6. kleung@cs.umanitoba.ca

  2. Data mining and analytics aims to analyze valuable data and extract implicit, previously unknown, and potentially useful information from the data. Due to advances in technology, high volumes of valuable data are generated at a high velocity in high varieties of data sources in various real-life business, scientific and engineering applications. Due to their high volumes, the quality and accuracy of these data depend on their veracity (uncertainty of data). This leads us into the new era of Big Data. This talk presents some works on big data mining and computing, especially on an important task of frequent pattern mining, which computes and mines from big data for interesting knowledge in the forms of frequently occurring sets of merchandise items in shopping markets, interesting co-located events, and/or popular individuals in social networks. The talk also shows how big data mining contributes to real-life applications and services.

EAAI 45 (Oct 2015)

Anna Fariha, Chowdhury Farhan Ahmed, Carson K. Leung, Md. Samiullah, Suraiya Pervin, Longbing Cao:

A new framework for mining frequent interaction patterns from meeting databases.

Engineering Applications of Artificial Intelligence (EAAI) 45:

103-118 (October 2015)

Highlights

• • We proposed a DAG-based mining framework to model and mine interactions in meetings.

  • The framework integrates DAG, interaction pattern & weighted frequent pattern mining.

  • It captures temporal and triggering relations among meeting interactions.

  • It incorporates node weight to preserve rank information of meeting participants.

  • It exploits anti-monotone property and is practical in many real-life scenarios.

APWeb 2015

On Sunday, September 20, 2015, Jialiang Yu presented a refereed paper titled "Probabilistic frequent pattern mining by PUH-Mine", which he co-authored with his academic supervisor (Dr. Carson K. Leung) and his fellow lab members (Wenzhu Tong and Dacheng Liu), in APWeb 2015 held in Guangzhou (广州), China. Jialiang Jalen Yu also won one of the 20 Student Travel Awards for APWeb 2015.

SWC 2015 Cybermatics Forum

• 1. Dr. Carson Leung was invited to be a Cybermatics Formum Keynote Speaker at the 2015 Smart World Congress to give a keynote talk on "big data mining and computing in a smart world" on Thursday, August 13, 2015. The congress (i) is sponsored by IEEE, IEEE Computer Society (IEEE CS), and IEEE Technical Committee on Scalable Computing (IEEE TCSC); and (ii) includes five IEEE conferences (e.g., IEEE UIC 2015, IEEE ATC 2015, IEEE ScalCom 2015, IEEE CBDCom 2015, and IEEE IoP 2015) and a series of co-located activities.

1. Cybermatics Forum Keynotes

   1. 1. Cybermatics Forum Keynotes in August 13

• • 1. Speaker:

    2. Carson Leung

    3. Univ. of Manitoba

    4. Title: Big Data Mining and Computing in a Smart World

1. The 2015 Smart World Congress Keynotes (August 10-14, Beijing, China) - Carson Leung

   1. Congress Keynote

• 1. Forum Speaker:

  2. Professor Carson Leung

  3. Univ. of Manitoba

  4. E-mail: Carson.Leung@cs.umanitoba.ca

  5. Abstract:

  6. Data mining and analytics aims to analyze valuable data and extract implicit, previously unknown, and potentially useful information from the data. Due to advances in technology, high volumes of valuable data are generated at a high velocity in high varieties of data sources in various real-life business, scientific and engineering applications. Due to their high volumes, the quality and accuracy of these data depend on their veracity (uncertainty of data). This leads us into the new era of Big Data. This talk presents some works on big data mining and computing, especially on an important task of frequent pattern mining, which computes and mines from big data for interesting knowledge in the forms of frequently occurring sets of merchandise items in shopping markets, interesting co-located events, and/or popular individuals in social networks. The talk also shows how big data mining and computing contributes to the creation of a smart world environment.

  7. Biography:

  8. Carson Leung obtained his BSc(Hons), MSc and PhD from The University of British Columbia, Canada. He is currently a Full Professor at the University of Manitoba, Canada. He has published more than 110 papers on the topics of big data computing, databases, data mining, 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 an organizing committee member of ACM SIGMOD 2008, IEEE ICDM 2011, and IEEE/ACM ASONAM 2014, as well as a PC member of numerous international conferences including ACM KDD, ACM CIKM, and ECML/PKDD. Moreover, this year, he also serves as the PC Chair of the following three conferences--namely, IEEE International Conference Cloud and Big Data Computing (CBDCom) 2015, International Conference on Big Data Applications and Services (BigDAS) 2015, and IEEE International Conference on Internet of Things (iThings) 2015.

  9. Carson Leung:

  10. Big data mining and computing in a smart world.

  11. IEEE UIC-ATC-ScalCom-CBDCom-IoP 2015:

  12. ciii

      1. SWC 2015 (The 2015 Smart World Congress) - Cybermatics Forum Keynotes

      2. Cybermatics Forum Keynote VI

         1. Big Data Mining and Computing in a Smart World

         2. Carson Leung

         3. University of Manitoba

         4. Carson.Leung@cs.umanitoba.ca

         5. http://www.cs.umanitoba.ca/~kleung/

  13. Abstract:

  14. Data mining and analytics aims to analyze valuable data and extract implicit, previously unknown, and potentially useful information from the data. Due to advances in technology, high volumes of valuable data are generated at a high velocity in high varieties of data sources in various real‐life business, scientific and engineering applications. Due to their high volumes, the quality and accuracy of these data depend on their veracity (uncertainty of data). This leads us into the new era of Big Data. This talk presents some works on big data mining and computing, especially on an important task of frequent pattern mining, which computes and mines from big data for interesting knowledge in the forms of frequently occurring sets of merchandise items in shopping markets, interesting co‐located events, and/or popular individuals in social networks. The talk also shows how big data mining and computing contributes to the creation of a smart world environment.

• • 1. Biography:

• 1. Carson Leung obtained his BSc(Hons), MSc and PhD from The University of British Columbia, Canada. He is currently a Full Professor at the University of Manitoba, Canada. He has published more than 110 papers on the topics of big data computing, databases, data mining, 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 an organizing committee member of ACM SIGMOD 2008, IEEE ICDM 2011, and IEEE/ACM ASONAM 2014, as well as a PC member of numerous international conferences including ACM KDD, ACM CIKM, and ECML/PKDD. Moreover, this year, he also serves as the PC Chair of the following three conferences‐‐namely, IEEE International Conference Cloud and Big Data Computing (CBDCom) 2015, International Conference on Big Data Applications and Services (BigDAS) 2015, and IEEE International Conference on Internet of Things (iThings) 2015.

  2. Dr. Carson K. Leung was presented a Certification of Appreciation in grateful recognition as the Keynote Speaker of Cybermatics Forum in conjunction with 2015 Smart World Congress (SWC2015), Beijing, China, August 10-14, 2015.

IEEE CBDCom 2015 Best Service Award

Dr. Carson K. Leung received a Best Service Award for Leading Chairs in recognition for his service and contribution as the Program Chair for the IEEE International Conference on Cloud and Big Data Computing (CBDCom 2015), Beijing, China, August 10-14, 2015.

IEEE CBDCom 2015 Outstanding Leadership Award

Dr. Carson K. Leung received an IEEE Outstanding Leadership Award in recognition for his service and contribution as the Program Chair of the IEEE International Conference on Cloud and Big Data Computing (CBDCom 2015), Beijing, China, August 10-14, 2015.

IEEE CBDCom 2015

• 1. Dr. Carson K. Leung serves as a Program Chair for the IEEE International Conference on Cloud and Big Data Computing (CBDCom 2015) held on August 10-14, 2015 in Beijing (北京), China. IEEE CBDCom was collocated with

     • 12th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2015),

     • 12th IEEE International Conference on Advanced and Trusted Computing (ATC 2015),

     • 15th IEEE International Conference on Scalable Computing and Communications (ScalCom 2015), and

     • IEEE International Conference on Internet of People (IoP 2015),

1. together included in the 2015 Smart World Congress.

   1. Jianhua Ma, Laurence T. Yang:

   2. Message from the UIC-ATC-ScalCom-CBDCom-IoP 2015 Steering Chairs.

   3. IEEE UIC-ATC-ScalCom-CBDCom-IoP 2015:

   4. xxix-xxx.

      1. We would like to express our special thanks to the Program Chairs, Xing Xie, Koji Zettsu, Mirco Musolesi, Changai Sun, ZhangBing Zhou, Beihong Jin, Patrick Hung, Bofeng Zhang, Marco Aldinucci, Lei Guo, Yinglong Xia, Carson K. Leung, Salvatore Distefano, Lei Wang, Tianyi Zang, and Irwin King, for creating excellent technical programs.

      2. Jianhua Ma, Hosei University, Japan

      3. Laurence T. Yang, St. Francis Xavier University, Canada

      4. UIC-ATC-ScalCom-CBDCom-IoP 2015 Steering Chairs

   5. Alvin Chin, Thanos Vasilakos, Weifeng Lv, Huansheng Ning, Weishan Zhang:

   6. Message from the CBDCom 2015 General and Executive Chairs.

   7. IEEE UIC-ATC-ScalCom-CBDCom-IoP 2015:

   8. xxxvii-xxxviii.

• • 1. We would like to give our special thanks to the Program Chairs, Yinglong Xia (IBM T.J. Watson Research Center, USA), Carson K. Leung (University of Manitoba, Canada), and Salvatore Distefano (Politecnico di Milano, Italy), as well as the Program Vice Chairs, Xin Wang (Fudan University, China), Julian Schuttee (Fraunhofer AISEC, Germany), Hongyu Huang (Chongqing University, China), Rubing Duan (A*STAR, Singapore), Renato Ishii (Universidade Federal de Mato Grosso do Sul, Brazil), Zhipeng Xie (Fudan University, China), Xiwei Xu (NICTA, Australia), Shangguang Wang (Beijing University of Post and Telecommunication, China), Yangfan Zhou (The Chinese University of Hong Kong, China), Yuedong Xu (Fudan University, China), Klaus Marius Hansen (University of Copenhagen, Denmark), Qinghua Lu (China University of Petroleum, China), Samia Bouzefrane (Conservatoire National des Arts et Métiers, France), Xiaoli Li (A*STAR, Singapore), Su Yang (Fudan University, China), Xiong Luo (University of Science and Technology Beijing, China), Wenhua Yu (Jiangsu Normal University, China), Roman Vaculin (IBM T.J. Watson Research Center, USA), Kalyana Chadalavada (UIUC, USA), and Manisha Gajbe (UIUC, USA), for their excellent work and great efforts in organizing an outstanding Program Committee, conducting a rigorous reviewing process, selecting high-quality papers from a large number of submissions, and for preparing an excellent conference program. We are also indebted to the members of the Program Committee, who have put in hard work and long hours to review each paper in a professional way. Thanks to them all for their valuable time and effort in reviewing the papers. We are grateful to the workshop chairs Qinghua Lu (China University of Petroleum, China), Xi Guo (University of Science and Technology Beijing, China), Ioan Toma (University of Innsbruck, Austria), Vaskar Raychoudhury (Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, India), and Jaime Lloret Mauri (Universidad Politécnica de Valencia, Spain) for organizing the workshop on emerging areas.

    2. Alvin Chin, BMW Group, USA

    3. Thanos Vasilakos, Kuwait University, Kuwait

    4. Weifeng Lv, Beihang University, China

    5. CBDCom 2015 General Chairs

    6. Huansheng Ning, University of Science and Technology Beijing, China

    7. Weishan Zhang, China University of Petroleum (East China), China

    8. CBDCom 2015 Executive Chairs

1. 9. Yinglong Xia, Carson K. Leung, Salvatore Distefano:

   10. Message from the CBDCom 2015 Program Chairs.

   11. IEEE UIC-ATC-ScalCom-CBDCom-IoP 2015:

   12. xxxix

• • 1. Message from the CBDCom 2015 Program Chairs

    2. Welcome to the IEEE International Conference on Cloud and Big Data Computing (CBDCom 2015), held in Beijing, China, August 10–14, 2015. CBDCom 2015 is one of the five events in the 2015 Smart World Congress, held in conjunction with UIC 2015, ATC 2015, ScalCom 2015, and IoP 2015. This conference is a premier forum for researchers, practitioners, developers, and users who are interested in cloud computing and big data, where we can explore new ideas, techniques, and tools and exchange experiences.

    3. This year we selected 34 papers from the submissions covering 12 topics related to cloud and big data, including big data graph algorithms, industrial experiences, virtualization, networking, and privacy. All submissions were peer reviewed by at least three reviewers from our international Program Committee consisting of professors and industrial researchers in relevant fields from 28 countries. The committee members are the ultimate gatekeepers of quality and timelines associated with the review process. Through many hours of hard work and persistence, we were able to complete the review process for the papers appearing in this proceedings. We would like to extend our gratitude to our Program Committee members for their invaluable contributions and efforts that have culminated in this proceedings. In particular, we are keenly aware of the scarcity and the significant value of our track chairs, who helped us populate the Program Committee, publicize the event, and screen every submitted paper before referral to the reviewers. Therefore, we would like to recognize our effective track chairs: Xin Wang (Fudan University, China), Julian Schuttee (Fraunhofer AISEC, Germany), Hongyu Huang (Chongqing University, China), Rubing Duan (A*STAR, Singapore), Renato Ishii (Universidade Federal de Mato Grosso do Sul, Brazil), Zhipeng Xie (Fudan University, China), Xiwei Xu (NICTA, Australia), Shangguang Wang (Beijing University of Post and Telecommunication, China), Yangfan Zhou (The Chinese University of Hong Kong, China), Yuedong Xu (Fudan University, China), Klaus Marius Hansen (University of Copenhagen, Denmark), Qinghua Lu (China University of Petroleum, China), Samia Bouzefrane (Conservatoire National des Arts et Métiers, France), Xiaoli Li (A*STAR, Singapore), Su Yang (Fudan University, China), Xiong Luo (University of Science and Technology Beijing, China), Wenhua Yu (Jiangsu Normal University, China), Roman Vaculin (IBM T.J. Watson Research Center, USA), Kalyana Chadalavada (UIUC, USA), and Manisha Gajbe (UIUC, USA). All of us involved in the review process would also like to thank the Executive Chairs, Huansheng Ning (University of Science and Technology Beijing, China) and Weishan Zhang (China University of Petroleum, China); the General Chairs, Alvin Chin (BMW Group, USA), Thanos Vasilakos (Kuwait University, Kuwait), and Weifeng Lv (Beihang University, China); the Publicity Chairs, Yangfan Zhou (The Chinese University of Hong Kong, China), Samia Bouzefrane (Conservatoire National des Arts et Métiers, France), Chuanming Liu (National Taipei University of Technology, Taiwan), and Yuehai Xu (Wayne State University, USA); the Financial Chair, Lingfeng Mao (University of Science and Technology Beijing, China); and the webmaster, Qitao Mu (University of Science and Technology Beijing, China), for their selflessly volunteered hours in coordinating, organizing, and managing the conference's publicity, paper submission, and other activities.

    4. Finally, we would like to thank the authors and participants for choosing CBDCom 2015 as the venue at which to present their research findings and to offer constructive feedback. Cloud and big data computing are interesting topics with highly increasing popularity, swamped with brand new ideas and market potential. We hope this conference can foster interaction among researchers in both academia and industry.

    5. Yinglong Xia, IBM T.J. Watson Research Center, USA

    6. Carson K. Leung, University of Manitoba, Canada

    7. Salvatore Distefano, Politecnico di Milano, Italy

    8. CBDCom 2015 Program Chairs

TLDKS XXI (2015)

Alfredo Cuzzocrea, Ueshwar Dayal:

Preface.

LNCS Transactions on Large-Scale Data- and Knowledge-Centered Systems (TLDKS) XXI (LNCS 9260),

selected papers from DaWaK 2012:

V-IX (June 2015)

The sixth paper, titled "Mining Popular Patterns: A Novel Mining Problem and Its Application to Static Transactional Databases and Dynamic Data Streams," by Alfredo Cuzzocrea, Fan Jiang, Carson K. Leung, Dacheng Liu, Aaron Peddle and Syed K. Tanbeer, recognizes that, since the introduction of the frequent pattern mining problem, researchers have extended frequent patterns to different useful patterns such as cyclic, emerging, periodic, and regular patterns. In line with this trend, the paper introduces popular patterns, which captures the popularity of individuals, items, or events among their peers or groups. Moreover, they also propose the Pop-tree structure for capturing the essential information from transactional databases, and the Pop-growth algorithm for mining popular patterns from the Pop-tree. The authors illustrate how the proposed algorithm mines popular friends from social networks, as a relevant case study of the proposed framework. Because the framework is not confined to mining popular patterns from static transactional databases, they extend the work to mining popular patterns from dynamic data streams. Specifically, the Pop-stream structure to capture the popular patterns in batches of data streams is proposed, as well as the Pop-streaming algorithm for mining popular patterns from the Pop-stream structure. Finally, the experimental results show that (a) the proposed tree structure is compact and space efficient and (b) the proposed algorithm is time efficient in mining popular patterns from static transactional databases and dynamic data streams.

IEEE AINA 2015

Dr. Carson K. Leung serves as a Program Vice-Chair for the 29th IEEE International Conference on Advanced Information Networking and Applications (AINA-2015) held on May 24-27, 2015 in Gwangju (光州), South Korea. He oversees the Internet Computing and Web Applications Track.

New Media Manitoba Networking Event

On Tuesday, May 26, 2015, Dr. Carson Leung and parts of his research team (including Terry Fan Jiang, Zhao Han, and Hao Zhang) actively participated in a (UofM TTO and) New Media Manitoba's Interactive Digital Media networking event and met representatives from Bit Space Development, Bold Apps (Bold Innovation Group Ltd.), The Campfire Union, CoElement Inc., Complex Games Inc., Consultica Inc., Electric Monk Media, Evodant Interactive Inc., and Manoverboard Inc.

Attendee Bios

Carson Leung, PhD

Full Professor

Founder & Director of Database & Data Mining Lab

Dr. Carson Leung is a Full Professor in Department of Computer Science at University of Manitoba. His research interests include:

• • Data mining and analysis (including data analytics, e-commerce, data science & business intelligence solutions);

  • Big data, Databases (including image databases), data management, and data warehousing;

  • Data visualization and visual analytics;

  • Health informatics and electronic health;

  • Web technology and services, as well as social computing.

Science Bash 2015

On Thursday, May 14, 2015, Dr. Carson Leung presented in the First Annual Science Bash featuring an image and a 2-minute talk about himself and his research in the UofM CS Database & Data Mining Lab.

2014/15 Science Award for Excellence in TA

Each year, teaching assistants (TAs) in the Faculty of Science are recognized for the excellence of their work. This year, Fan Jiang and Richard MacKinnon are both selected as winners of the 2014/15 Faculty of Science Award for Excellence in Teaching Assistance in recognition of their efforts and work as TAs in the Fall 2014 offering of COMP 3380 taught by Dr. Carson Leung. Among all 29 nominees for this award, only three are from Computer Science. Jiang and MacKinnon are the only winners from Computer Science.

USRA 2015

Two lab members won undergraduate student research awards:

• • Third-year undergraduate student Mr. Edson M. Dela Cruz, who is also enrolled in the B.C.Sc.(Hons.) degree program, 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. He was the only winner in Department of Computer Science.

  • Third-year undergraduate student Mr. Vijay Sekar, from VIT University, won a MITACS Globalink award to conduct a full-time 12-week research project on "Big data mining and analytics" in the area of data mining under the academic supervision of Dr. Carson K. Leung. He was one of the 19 Globalink Research Interns in the entire UofM campus, and one of the two in UofM Department of Computer Science.

MITACS Globalink 2015

Dr. Leung obtained one of the 19 Mitacs Globalink research internships awarded in the entire UofM campus, and one of the two in UofM Department of Computer Science.

Department Seminar - Winter 2015

On Thursday, April 9, 2015, Michael D. Cook gave a presentation on "Utilizing data mining to predict IMDB movie ratings".

The Manitoban 101(56) (2015)

• 1. Jeremiah Yarmie:

  2. Data mining: I heard you like data, so we put information in your information.

  3. The Manitoban 101(56):

  4. 10 (March 25, 2015)

     1. We are making data all the time. Unbeknownst to most individuals, data is stored every time we use our credit cards, post on social media, or make a web search. Sure, we could be interested in the specific details of your search history or Amazon purchases, but the patterns within this data also hold a lot of information. This brings us to the field of data mining.

     2. Data mining, also known as "Knowledge Discovery in Data," is the purposeful identification of implicit patterns that are found within large databases.

     3. "Data mining can be considered as a task of performing advanced analysis on data," said Carson Leung, computer science professor at the University of Manitoba.

     4. Leung's Database and Data Mining Laboratory at the U of M focuses on developing ways to detect frequently occurring patterns and abnormal items within large databases. Leung's research attempts to incorporate more human control over the data mining algorithms, letting the humans do the hard thinking and letting the computers do the hard work.

     5. Data mining is a multidisciplinary field, incorporating elements from fields like artificial intelligence, machine learning, data visualization, and statistics.

     6. Data mining tasks include detecting relationships between different variables, clustering similar data into groups and clusters, and summarizing the data in a concise way. Often these tasks are much more laborious than something a normal human can handle. Computers can do this no sweat.

     7. These tasks allow data miners to also predict likely outcomes. Governments and corporations can learn a great deal more about you as an individual or as a member of a larger group through data mining.

     8. Data mining is what makes it possible for Walmart to analyze its millions of daily transactions.

     9. When you shop for groceries at a supermarket, the stores will often record what kinds of items are frequently purchased by customers. That sort of information is easy to find and retrieve, but data mining allows the store to determine the relationships between individual purchases.

     10. "For example, store managers may find that customers often purchase bread and butter, which you may expect," said Leung. "However, there are some other less obvious patterns too."

     11. These stores can utilize customer behavioural patterns to increase their sales.

     12. [Data mining allows us to find patterns in large amounts of data]

     13. "On the one hand, if many customers purchase items A and B frequently together, the store may place them together for the customer's convenience," said Leung. "On the other hand, the store may put these items apart so that customers would likely walk between the two items and potentially purchase more items along the way."

     14. To a further extent, customer behaviours can also be monitored on the individual basis with the use of loyalty cards, which allow the store to use data mining for analyzing behavioural sequences and do personalized promotion to targeted customers.

     15. This example focuses on the frequency of a behaviour, but data mining can be used to analyze behavioural sequences as well.

     16. The amount of data being stored about us is equal parts beneficial and concerning. You may worry why every single purchase you make with a credit card has to be monitored, but data mining allows for the detection of anomalies. Credit card companies can use data mining to become aware of fraud by detecting uncharacteristic purchases.

     17. Facebook uses data mining to find people you may know.

     18. "If you put on your profile that you are an undergraduate student here at the U of M who started in a particular year, then Facebook can mine their databases and find people with similar backgrounds," said Leung.

     19. Data mining can be used in other scientific fields as well. By analyzing the patterns of variation within our DNA sequences, we can figure out how they relate to things like disease.

  5. Facebook:

     2. • University of Manitoba: Faculty of Science (@umanitobafacultyofscience) via Department of Computer Science, University of Manitoba

        • Friday, April 24, 2015 at 2:30pm

          • [Data mining - The Mantioban]

        • Department of Computer Science, University of Manitoba (@UofMcs)

        • Friday, April 24, 2015 at 1:39pm

        • An article on data mining from The Manitoban featuring Carson Leung.

        • [Data mining - The Mantioban]

        • University of Manitoba: Faculty of Science (@umanitobafacultyofscience) shared a link.

        • Monday, March 30, 2015 at 3:06pm

        • [Data mining - The Mantioban]

MITACS Accelerate 2015

Management and Mining of Big Energy Sector Data

Faculty Supervisor: Dr. Carson Leung, University of Manitoba

Project Year: 2014

Province: Manitoba

University: University of Manitoba

Discipline: Computer science

Sector: Information and communications technologies

Program: Accelerate

With advances in techniques, high volumes of valuable data are generated in many domains (e.g., energy sector) at a rapid rate. Consequently, a scalable and flexible system for efficient storage and fast management of these distributed data is needed. In this proposed research project, we plan to design and implement a cloud-based data storage & management system that is flexible, scalable and fast to handle distributed data in a parallel fashion for the partner organization. In addition, we also plan to design and implement a system that conducts parallelized data mining and machine learning to discover new patterns from the data stored in the above data management system. These discovered patterns would be beneficial to the partner organization as these patterns help complex decision making.

Jiang's Teaching

Fan Terry Jiang, a PhD candidate in our lab, teaches:

• • lecture-based COMP 1270 A02 (Introductory Computer Usage 2) in Winter 2015 on every Monday, Wednesday & Friday at 10:30am-11:20am in Buller 207 from January 07 to April 10, 2015;

  • lecture-based COMP 2150 (Object Orientation) in Summer 2015 on every Tuesday and Thursday 7:30pm-8:50pm in E2-320 EITC from May 05 to August 06, 2015; and

  • distance & online education-based COMP 1010 (D01) (Introductory Computer Science 1) in Fall 2015 from September 10 to December 09, 2015.

By the end of 2015, Jiang has taught four sections of three distinct courses (distance & online education-based COMP 1010 twice, as well as lecture-based COMP 1270 and 2150 once each).