>>>Summer Program
Fuzhou University Geographical Big Data Analysis and Applications International Summer Program
July 3-7, 2015 Fuzhou, China


  • Massive, heterogeneous and unstructured data sets are quickly acquired from sensors on the Internet of Things, earth observation and navigation, location-based services, social networks and crowd sourcing. Many of Big Data has a geospatial tag. New data processing and analysis methods are needed to deal with this new challenge.
  • The International Summer Program on Geographical Big Data Analysis and Applications (jointly with the 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM2015) will be held on 3-7 July 2015 at Fuzhou University, Fuzhou, China.
  • Big Data is bringing profound information revolution to how we live, work and think. Decision-making process is evolving from requirement-driven to data-driven. Significant change will also occur in how we conduct scientific research in fields such as earth sciences. The main objectives of this summer program are to discuss new technologies and illustrative applications of big data and data analysis/mining for geographical knowledge services. Internationally renowned pioneers in the field of data analysis/mining will cover a wide range of multidisciplinary topics. They will impart the knowledge and skills of young researcher and professionals working in the area. It will also provide an excellent opportunity to meet internationally well recognized scholars in data analysis/mining communities and young graduate students and scientists to strengthen the data analysis/mining research and applications, geographical knowledge service and decision support in Big Data era.

Lecturer & Title

  • Diansheng Guo : Spatial Mobility and Multivariate Data Analytics, Visualization and Applications Download
  • Tahar Kechadi : Geographical Big Data Analysis and Cloud Computing: Hadoop and MapReduce based Approach Download1 Download2 Download3
  • Domenico Talia : Parallel and distributed data mining techniques Download
  • Paul Longley :Big Data analytics and the global geography of family names
  • Xindong Wu : Data analysis with big Data Download

Biography for Lecturers

  • Paul Longley
    Prof. of Geographic Information Science at University College London, UK; Director of the ESRC Consumer Data Research Centre. A co-author of the best-selling book of Geographic Information Science and Systems.
  • Topic: Big Data analytics and the global geography of family names
    This workshop will review work undertaken at University College London to create and maintain a database that is representative of two billion of our planet's population. Participants will be able to use our websites to understand changes in the spatial distributions of bearers of particular names over time, as well as investigate the spatial distribution of many names across 26 countries. It is also hoped that participants will be able to use a new tool, which will use spatial analysis methods to identify the probable locations to which lists of names pertain. The presentations will also address the observed relationships between family names and genetic profiles. These examples will be used to illustrate wider issues in spatial Big Data analytics.
  • Biography: Paul Longley (B.Sc., Ph.D., D.Sc., FAcSS) is Professor and chair of Geographic Information Science at University College London, UK, where he also directs the ESRC Consumer Data Research Centre.more
  • Domenico Talia
    Prof. of Computer Engineering and Chair of the ICT Center DIMES, University of Calabria and DtoK Lab, the former director of ICAR-CNR, Italy
  • Topic: Parallel and Distributed Data Mining Techniques
    The analysis of the massive and distributed data repositories that are today available, require the combined use of smart data analysis techniques and scalable architectures to find and extract useful information from them. Parallel systems, and distributed computing platforms offer an effective support for addressing both the computational and data storage needs of Big Data mining and parallel analytics applications. In fact, complex data mining tasks involve data- and compute-intensive algorithms that require large storage facilities together with high performance processors to get results in suitable times. In this lecture we introduce the most relevant topics and the main research issues in high performance data mining focusing on parallel data mining strategies and distributed analysis techniques. We also present some data mining frameworks designed for developing distributed data analytics applications.
  • Biography:Domenico Talia is a full professor of computer engineering at the University of Calabria, Italy. He is a co-founder of the DtoK Lab start-up and a partner of Exeura. His research interests include parallel and dis-tributed data mining algorithms, Cloud computing, distributed knowledge discovery, social data analysis, peer-to-peer systems, and parallel pro-gramming models. more
  • Xindong Wu
    Prof. of Computer Science, University of Vermont, USA, Fellow of AAAS and IEEE; Yangtse River Scholar; the Thousand Talents Plan Scholar in China
  • Topic: Data Analysis with Big Data
    This lecture starts with characteristics and challenges for data analysis in the era of Big Data. We discuss and compare existing models for Big Data, such as the 4V model, 5 R's, the 4P medical model, and the HACE theorem. Based on the common characteristics in these models, we present techniques, algorithms and research problems for processing data streams and feature streams, including classification, association analysis, clustering, and link mining. Noise handling is an essential part of Big Data analysis. Random noise and systematic noise are discussed in data mining applications.
  • Biography: Xindong Wu is a Professor of Computer Science at the University of Vermont (USA), a Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology (China), and a Fellow of the IEEE and the AAAS. He is the Thousand Talents Plan Scholar in China. more
  • Diansheng Guo
    Associate Prof., Dept. of Geography, University of South Carolina, USA; Joint-appointment Prof. at Fuzhou University; Director of Spatial Data Mining and Visual Analytics Lab
  • Topic: Spatial Mobility and Multivariate Data Analytics, Visualization and Applications
  • Biography: Diansheng Guo is Associate Professor of geography at the University of South Carolina. He received his B.S. degree from Peking University (1996), M.S. degree from the Chinese Academy of Sciences (1999), and Ph.D. degree in geography from the Pennsylvania State University (2003).more
  • Tahar Kechadi
    Prof. of Computer Science and informatics, Investigator of the Insight national Centre for Data Analytics, University College Dublin, Ireland
  • Topic: Geographical Big Data Analysis and Cloud Computing: Hadoop and MapReduce based Approach
    Among all the data we collect these days, spatial data represents a very significant portion. Nearly 80% of the data we collect has an element of space, which is not only extremely large but it also need a very special focus on the mining and analytics techniques for analysing it. These spatial (or geographical) datasets store a large amount of space-related data, such as maps, remote sensing, medical imaging data, environment images, etc. They carry topological and/or distance information, usually organised by sophisticated, multidimensional spatial indexing structures that are accessed by spatial data access methods and often require reasoning, geometric computation, and spatial knowledge representation techniques. more
  • Biography: Tahar Kechadi is a Professor of Computer Science and informatics at the University College Dublin, Ireland. He was awarded PhD and Masters degree - in Computer Science from University of Lille 1, France. He was appointed as lecturer at the Computer Science Department of Lille University. more

Summer School Schedule:

July 3 (Fri) July 4 (Sat) July 5 (Sun) July 6 (Mon) July 7 (Tue)
8:30-10:00 D. Guo T. Kechadi D. Talia P. Longley X. Wu
10:00-10:30 Coffee Break Coffee Break Coffee Break Coffee Break Coffee Break
10:30-12:00 D. Guo T. Kechadi D. Talia P. Longley X. Wu
12:00-13:30 Lunch break Lunch break Lunch break Lunch break Lunch break
13:30-15:00 D. Guo T. Kechadi D. Talia P. Longley X. Wu
15:00-15:30 Coffee Break Coffee Break Coffee Break Coffee Break Coffee Break
15:30-17:00 D. Guo T. Kechadi D. Talia P. Longley X. Wu


  • The workshop will be held in Science Hall, Zhicheng College of Fuzhou University, Fuzhou City, Fujian Province, China.
  • CampusMap


  • The registration fees is 2000 Yuan; All registration fees should be paid before June 30, 2015. Registration fee of participants will cover: Attending all lectures, obtaining lecture package, and coffee breaks and certificate. You are also entitled to attend the ICSDM2015 sessions without additional payment.
  • Note: Space is limited and is first come first serve. Please download the registration form, fill in the form, pay the registration fee, scan the payment stub and send them to icsdm2015@fzu.edu.cn early.

Way of Payment:

A.Bank Transfer

The bank account for the conference is as follows:

Way of Payment by Foreign Guests
Account NO. 1402029109341002562
Postal Code  
Beneficiary's Name FUZHOU UNIVERSITY
Beneficiary's Address 
Way of Payment by Domestic Guests
Banker Name 中国工商银行福建省福州凤凰支行
Account NO. 1402013409008800551 
Banker Address  
Postal Code  
Beneficiary's Name 福州大学
Beneficiary's Address  
Remark: Please fill in the remark of the remittance bill as follows:"SS-2015, Your name and affiliation . ";请在转账附言中注明:"SS-2015, SIRC, 您的单位和姓名 . "

Contact us

  • 13328269460 (Mr. Huang);
    13655091007 (Ms. Lin)
  • Fax: 0591 28308090;
  • Email: icsdm2015@fzu.edu.cn

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