Keynote Speakers
Keynote Talk 1:
Effective and Efficient Community Search, Dr. Reynold Cheng, HKU
Abstract:
Given a graph G and a vertex q ∈ G, the community search query returns a subgraph of G that contains vertices related to q. Communities, which are prevalent in attributed graphs such as social networks and knowledge bases, can be used in emerging applications such as product advertisement and setting up of social events. In this talk, we investigate the attributed community query (or ACQ), which returns an attributed community (AC) for an attributed graph. The AC is a subgraph of G, which satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices share common keywords). The AC enables a better understanding of how and why a community is formed (e.g., members of an AC have a common interest in music, because they all have the same keyword “music”). An AC can be “personalized”; for example, an ACQ user may specify that an AC returned should be related to some specific keywords like “research”and “sports”. To enable efficient AC search, we develop the CL-tree index structure and three algorithms based on it. We evaluate our solutions on four large graphs, namely Flickr, DBLP, Tencent, and DBpedia. Our results show that ACs are more effective and efficient than existing community retrieval approaches. Moreover, an AC contains more precise and personalized information than that of existing community search and detection methods.
We further generalize the keyword –based attributed graphs to spatial-based attributed graphs, in which each vertex has a location, and study the spatial-aware community (SAC) search problem. An SAC is a community with high structure cohesiveness and spatial cohesiveness. The structure cohesiveness mainly measures the social connections within the community, while the spatial cohesiveness focuses on the closeness among their geo-locations. We propose two exact algorithms, and three efficient approximation algorithms. Our experiments show that SAC search achieves higher effectiveness than the state-of-the-art CD and CS algorithms.
Bio:
Dr. Reynold Cheng is an Associate Professor of the Department of Computer Science in the University of Hong Kong. He was an Assistant Professor in HKU in 2008-11. He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000, from the Department of Computer Science in the University of Hong Kong. He then obtained his MSc and PhD from Department of Computer Science of Purdue University in 2003 and 2005 respectively. Dr. Cheng was an Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University during 2005-08. He was a visiting scientist in the Institute of Parallel and Distributed Systems in the University of Stuttgart during the summer of 2006.
Dr. Cheng was granted an Outstanding Young Researcher Award 2011-12 by HKU. He was the recipient of the 2010 Research Output Prize in the Department of Computer Science of HKU. He also received the U21 Fellowship in 2011. He received the Performance Reward in years 2006 and 2007 awarded by the Hong Kong Polytechnic University. He is the Chair of the Department Research Postgraduate Committee, and was the Vice Chairperson of the ACM (Hong Kong Chapter) in 2013. He is a member of the IEEE, the ACM, the Special Interest Group on Management of Data (ACM SIGMOD), and the UPE (Upsilon Pi Epsilon Honor Society). He is an editorial board member of TKDE, DAPD and IS, and was a guest editor for TKDE, DAPD, and Geoinformatica. He is an area chair of ICDE 2017, a senior PC member for DASFAA 2015, PC co-chair of APWeb 2015, area chair for CIKM 2014, area chair for Encyclopedia of Database Systems, program co-chair of SSTD 2013, and a workshop co-chair of ICDE 2014. He received an Outstanding Service Award in the CIKM 2009 conference. He has served as PC members and reviewer for top conferences (e.g., SIGMOD, VLDB, ICDE, EDBT, KDD, ICDM, and CIKM) and journals (e.g., TODS, TKDE, VLDBJ, IS, and TMC).
Keynote Talk 2:
Data Analytics enables advanced AIS applications, Ernie Batty, IMIS Global Limited
Abstract:
AIS data is obtained from many different terrestrial and satellite sources. AIS data enables, safety, security, environmental protection and the economic efficiency of the maritime sector. The quality of AIS receivers is not controlled in the same manner as AIS transmitters. This has lead to a situation where AIS data is not as clean as it should / could be. Added to this is the lack of accuracy and standards in entering the voyage data by the mariners such as next port of call.
By using analytics IMIS has been able to process the data stream to eliminate a large portion of the faulty data. This has allowed the resultant data to be used for more accurate detailed analysis such as the long term vessel track, port arrival events and port departure events.
New data that is derived from processing AIS data is adding to the information available to maritime authorities enabling a significant increase in safety, security, environmental protection and economic growth.
The next generation of maritime data communications technology being built on AIS. This is known as the VHF Data Exchange System (VDES) and this technology now opens up further opportunities.
Description of IMIS Global Limited:
IMIS Global Limited (IMIS) is a United Kingdom based company that was formed in 2000 to focus on the then new field of AIS networks. IMIS now runs standards compliant AIS networks using the Software As A Service (SaaS) model and is migrating a number of national networks away from their own bespoke national networks onto a feature rich AIS network environment that is expected to grow on an ongoing basis with new features and capability including the increased use of data analytics along with significantly increased source data being obtained from VDES and the maritime implementation of the Internet of Things (IOT).