Challenges in Cloud Computing

Abstract: Cloud computing has potential to revolutionize the IT industry as it enables clients to focus on application design rather than the scalability and availability of the underlying database and hardware. In this tutorial, I will review some existing cloud platforms, present several important challenges in building the cloud platforms, and brainstorm on the killer applications that will enable the cloud to be the next generation IT platform.
     In particular, the tutorial will focus on the following aspects:
     i) An introduction to current cloud platforms. In particular, I will focus on Microsoft's newly released cloud platform: Azure and SQL Azure.
     ii) The choice of cloud data storage. Data storage is the core component of all cloud-based solutions. In this tutorial, I will discuss the pros and cons of the current options -- key value pairs and RDBMS.
     iii) Data consistency. If data consistency issues can be solved, it will be possible to support a full fledging RDBMS in the cloud. In this tutorial, I will show that a fundamental challenge in data storage design for the cloud environment is how to trade off scalability/availability with data consistency.
     iv) Cloud enabling- techniques. Current cloud solutions are still very primitive in supporting advanced applications. Without a killer application, the platform will not prevail. In this tutorial, I will focus on two killer applications: i) DBMS -- Since the eventual goal of cloud computing is to better serve a large variety of applications, the need for advanced data management will persist, and matured cloud-based architectures will have to satisfy this need. 2) Parallel machine learning applications in the cloud ¨C Since the cloud will be the repository of large scale data of all sorts, the need of mining the data for knowledge discovery is the top priority for many applications.

Speaker: Haixun Wang, Microsoft Research Asia

Biography: Haixun Wang joined Microsoft Research Asia in Beijing, China in 2009, and he leads research in data management. Before joining Microsoft, he had been a research staff member at IBM T. J. Watson Research Center for 9 years. He was Technical Assistant to Stuart Feldman (Vice President of Computer Science of IBM Research) from 2006 to 2007, and Technical Assistant to Mark Wegman (Head of Computer Science of IBM Research) from 2007 to 2009. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He has published more than 120 research papers in referred international journals and conference proceedings. He was PC Vice Chair of KDD'10, ICDM'09, SDM'08, and KDD'08, and he served as demo/workshop/sponsor Chair of various conferences, including SIGMOD'08, ICDM'08, ICDE'09, ICDM'11, etc. He serves on the editorial board of IEEE Transactions of Knowledge and Data Engineering (TKDE), and Journal of Computer Science and Technology (JCST). He is an adjunct professor of Nanjing University and Renmin University of China.

 

Near-duplicate Video Retrieval

Abstract: Exponential growth of online videos and increasing user involvements to video-related activities has been observed as a constant phenomenon during last decade. User time spent on video capturing, editing, uploading, searching and viewing has boosted to an unprecedented level. Massive publishing and sharing of videos has given rise to the existence of a large amount of near-duplicate content and imposed urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted lots of attention. As discovered in recent works, latest improvements and progresses in near-duplicate video retrieval as well as related topics including shot boundary detection, feature extraction and high-dimensional indexing, are employed to assist the process. In this tutorial, existing variations of the definition of near-duplicate video will be discussed. We will describe a generic framework, summarize state-of-the-art practices, analyze the scalability of approaches, and explore the emerging trends of this research topic.

Speaker: Heng Tao Shen, The University of Queensland, Brisbane, Australia

Biography: Heng Tao Shen is a Reader in School of ITEE at The University of Queensland. He obtained his BSc (with 1st class Honours) and PhD from Department of Computer Science, National University of Singapore in 2000 and 2004 respectively, then joined The University of Queensland as a Lecturer in June 2004 and Senior Lecturer in March 2007. His research interests include Multimedia/Mobile/Web Search, Database Management, P2P/Cloud Computing, etc. Heng Tao has published and served on program committees in most prestigious international publication venues of interests, such as ACM SIGMOD, ACM Multimedia, VLDB, ICDE, etc. He is the winner of CORE Australasia *Chris Wallace award* 2009 for outstanding research contribution in the field of computer science. The prize was awarded to an academic for research undertaken within a university or research institution in Australia or New Zealand for a notable breakthrough or a contribution of particular significance.