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2014-06-21 17:27:59
2014-06-21
信息来源：www.scholat.com/team/VLDBSS
VLDB中国数据库学院 【VLDB Database School (China)】
2014年暑期学校招生简章
2014年7月7日~ 7月12日 中国 • 北京
学院的宗旨是：充分利用VLDB的资源，通过组织暑期学校等形式，邀请国际知名数据库学者来中国讲学，为我国数据库及相关领域的研究生、青年教师和工程技术人员提供一个集中学习和交流的平台，促进我们对国际数据库学科前沿全面及时的了解，并在此前提下，立足应用研发具有特色的数据管理技术和系统。
2014年课程由中国人民大学信息学院承办，本期暑期学校的主题为“Mobile Computing and Location-Based Services”。主讲教授是国际上享有很高学术声誉的数据库专家：丹麦奥尔堡大学教授现任TODS的主编 Christian Jensen、美国乔治亚理工学院教授刘伶和微软亚洲研究院研究员谢幸博士。我们热忱欢迎全国各地从事数据库研究的高年级研究生、青年教师和工程技术人员报名参加。
组织机构:
VLDB 中国数据库学院
院 长：周傲英 教授（华东师范大学）
副院长：李战怀 教授（西北工业大学） 王国仁 教授（东北大学）
本期组委会:
学术委员会主席：周傲英 教授（华东师范大学） 杜小勇 教授（中国人民大学）
组织委员会主席：李翠平 教授（中国人民大学）
课程安排:
日 期：2014年7月7日至7月12日
主 题：Mobile Computing and Location-Based Services
授课地点：中国人民大学（北京市海淀区中关村大街59号）
课程简介:
1. Course title: Data ManagementFoundations for Location-Based Services
Courseinstructor: Christian Jensen, Professor at Aalborg University, Denmark
Courseabstract:
An increasingly sophisticated infrastructure that encompasses geo-positioningcapabilities and Internet-worked mobile computing devices is becoming availableto rapidly growing numbers of users. Concurrently, the research community has inventedfoundations that enable location-based services that exploit thisinfrastructure. Such services may concern emergency management, transportation,information and social needs, and games, to name but a few possibilities.
These two half-day lectures aim to present an overview of selected datamanagement foundations for location-based services. In this setting, thelocation of a mobile object is captured by a trajectory, which is a functionfrom time to points in the Euclidean, spatial-network, or indoor space in whichthe movement occurs.Tracking denotes the process of using a positioning systemfor continuously maintaining an up-to-date representation of a (partial) trajectoryof an object. Prediction relates to aspects of the future trajectories ofobjects. Systems underlying location-based services may be subject to workloadsthat involve frequent updates as well as queries. This calls for efficientupdate as well as query processing techniques, and this in turn calls forefficient indexing techniques. A numberof such techniques have been proposed that are based on the R-tree and the B-tree.These techniques differ in how they contend with skew and other properties ofthe problem domain.
The following topics are planned: 1. Motivation; 2. Tracking androute prediction; 3. Indexing: bottom-up updates, the TPR-tree family, and theBx-tree family; 4. Indoor data management techniques: modeling, trajectories,and query processing
2. Course Title: (1) Spatial Alarms:Architectures and Algorithms
(2) Privacy Challenges in MobileComputing and Location Based Services
(3)Trajectory Mining: From subtrajectories, whole trajectories toTrajectory Patterns
Course Instructor: Ling Liu, Professor at Georgia Institute ofTechnology, USA
Course abstract:
(1) Spatial alarms are one of the fundamental capabilities forenabling personalization of location-based services (LBSs), especiallylocation-based advertisement, location based entertainment and location basedinformation dissemination. In this lecture, I will describe the alternativearchitectures and algorithms for scaling spatial alarm processing. We willcover three types of system architectural design: client-centric architecture,client-server architecture, and distributed architecture. The algorithms andoptimization techniques for efficient processing of spatial alarms include saferegion techniques for optimizing client energy efficiency, server loads, andnetwork bandwidth efficiency. For example, in a distributed architecture, Iwill describe the fundamentals of safe region-based processing and discuss thesuite of techniques for optimal distribution of partial alarm processing tasksfrom the server to the mobile clients while minimizing unnecessary alarmevaluations at both server and mobile clients. I will also describe differentsafe region computation algorithms to explore the impact of size and shape ofthe safe region on network bandwidth, server load and client energyconsumption. The development of these alternative safe region computationtechniques is critical for supporting client device heterogeneity and scalingspatial alarm processing.
(2) We are entering the mobile Internet era wherepeople, vehicles, and hand-held devices are connected at all times. Locationbecomes a piece of important information for real-time information access,on-demand service discovery and delivery, as well as continuous andpersonalized service provision. In location-based services, there areconceivably two types of location privacy - personal subscriber level privacyand corporate enterprise-level privacy. Companies need enterprise-level privacyto preserve corporate secrets and maintain competitive edge. Location privacyhas attracted attention in mobile computing, mobile data management, and wirelesscommunication research over the past few years. Most of the location privacysolutions try to prevent disclosure of unauthorized location information bytechniques that explicitly or implicitly control what and how locationinformation is given to whom and when. In this lecture, I will give an overviewof location privacy research and discuss three categories of location privacyprotection techniques: (1) Location protection through user-defined orsystem-supplied privacy policies; (2) Location protection through locationanonymization, a system capability to obfuscate the location information suchthat a state of a subject is not identifiable within the anonymity set; and (3)Location protection through pseudonymity of user identities, which uses an internalpseudonym rather than the user’s actual identity. I will also describe theintrinsic relationships among location privacy, location utility, andpersonalization. My lecture will end with a list of open issues and technicalchallenges in location privacy research.
(3) Mining mobile object trajectory datasets has been gainingsignificant interest in recent years. We can broadly classify the existingresearch into three categories: mining subtrajectories of mobile objects,mining whole trajectories of mobile objects, and mining trajectory patterns ofmobile objects. Density and Euclidean distance measures are commonly used bymost of existing approaches to trajectory mining. In this lecture, we show thatwhen the utility of mining mobile objecttrajectories is targeted at road network aware location based applications,density and Euclidean distance are no longer the effective measures. This isbecause traffic flows in a road network and the flow-based densitycharacterization become important factors for finding interesting trajectoryclusters of mobile objects travelling on road networks. I will describe one ortwo technical approaches under each of the three categories mentioned above.For example, by taking into account the physical constraints of the roadnetwork, the network proximity and the traffic flows among consecutive roadsegments, the trajectory mining can produe groups of sub-trajectories thatdescribe both dense and highly continuous traffic flows of mobile objects. Iwill discuss the technical challenges in mining sparial trajectories.
3. Course Title: Understanding from LargeScale Human Behavioral Data
Course Instructor: Xing Xie博士, MSRA(微软亚洲研究院)研究员
Courseabstract:
With the rapid development of positioning, sensor and smart devicetechnologies, large quantities of human behavioral data are now readilyavailable. They reflect various aspects of human mobility and activities in thephysical world. The availability of this data presents an unprecedentedopportunity to gain a more in depth understanding of users and provide themwith personalized online experience while respecting their privacy. In thiscourse, I will present a number of our recent research efforts on thisdirection, including user mobility understanding and prediction, location andactivity recommendation, user linking across multiple networks, psychologicaltrait inference, life pattern analysis, and driving behavior understanding.
报名须知
1.本年度计划招收学员100名。
2.学员录取由VLDB 中国数据库学院负责，将根据申请条件以及学校地区平衡的原则进行录取。录取通知将于2014年6月27日发出。
3.申请条件：具有博士学位的青年教师、博士研究生、成果突出的硕士研究生。
4.注册费每人500元，于收到录取通知后缴费。住宿有会议宾馆可优先优惠预订，但费用需自行承担。
5.学院为所有学员提供课程资料和讲义、会议期间的餐饮。不负责订返程票，不接送站（火车、飞机等）。
6.为了资助边远地区学校的师生参会,学院将提供5份奖学金,用于支付注册费和住宿费.由学院在提出申请的符合条件的学员中进行遴选.
7.请报名者填写报名表（报名表格下载）
邮寄：北京市海淀区中国人民大学信息楼429室 史晓薇 ，邮政编码：100872
（邮寄请注明：VLDB 2014 Summer School）
同时，发送扫描件至 vldbss2014@163.com
8.纸质报名表需由申请者所在单位签署意见并加盖公章。 纸质报名表下载 ： 点击下载
9.网上报名截止时间：2014年 6月 25日
联系人
赵素云 zhaosuyun@ruc.edu.cn
讲师介绍
Christian Jensen, Aalborg University, Denmark
ChristianS. Jensenis an Obel Professor of Computer Science at Aalborg University, Denmark. He wasa Professor at Aarhus University for a 3-year period from 2010 to 2013. Priorto that, he was a Professor at Aalborg University, Denmark. During the 1990s,he spent four sabbaticals at University of Arizona, and he recently spent aone-year sabbatical at Google Inc., Mountain View. He received the Ph.D. degreefrom Aalborg University in 1991 after 2 1/2 years of study at University ofMaryland, and he received the Dr. Techn. degree fromAalborg University in 2000. His research concerns data management anddata-intensive systems, and it concerns primarily temporal and spatio-temporaldata management. He is an ACM and an IEEE fellow, and he is a member of theAcademia Europaea, the Royal Danish Academy of Sciences and Letters, the DanishAcademy of Technical Sciences, and the EDBT Endowment, as well as a trusteeemeritus of the VLDB Endowment. He received the Ib Henriksen Research Award2001 for his research in mainly temporal data management, Telenor's NordicResearch Award 2002 for his research in mobile services, and the Villum KannRasmussen Award for Technical and Scientific Research 2011 for his generalresearch contributions. He is Editor-in-Chief of ACM TODS and anEditor-in-Chief of The VLDB Journal, and he is an Area Editor (temporaldatabases) for the Encyclopedia of Database Systems. He has served on theeditorial boards of ACM TODS, IEEE TKDE, and the IEEE Data EngineeringBulletin. He was PC chair or co-chair for SSTD 2001, EDBT 2002, VLDB 2005,MobiDE 2006, MDM 2007, DMSN 2008, TIME 2008, ACM SIGSPATIAL GIS 2011, AP Web2012, IEEE ICDE 2013, and DASFAA 2014.
Ling Liu, Georgia Institute ofTechnology, USA
Xing Xie博士, MSRA(微软亚洲研究院)研究员
Dr. Xing Xie iscurrently a senior researcher in Microsoft Research Asia, and a guest Ph.D.advisor for the University of Science and Technology of China. He received hisB.S. and Ph.D. degrees in Computer Science from the University of Science andTechnology of China in 1996 and 2001, respectively. He joined MicrosoftResearch Asia in July 2001, working on spatial data mining, location basedservices, social networks and ubiquitous computing. During the past years, hehas published over 140 referred journal and conference papers. He has more than50 patents filed or granted. He currently serves on the editorial boards of ACMTransactions on Intelligent Systems and Technology (TIST), SpringerGeoInformatica, Elsevier Pervasive and Mobile Computing, Journal of LocationBased Services, and Communications of the China Computer Federation (CCCF). Hehas worked as a guest editor of IEEE Transactions on Multimedia and IEEEIntelligent Systems. In recent years, he was involved in the program or organizingcommittees of over 70 conferences and workshops. Especially, he initiated theLBSN workshop series and served as program co-chair of ACM UbiComp 2011 andprogram chair of the 8th Chinese Pervasive Computing Conference (PCC 2012). InOct. 2009, he founded the SIGSPATIAL China chapter which was the first regionalchapter of ACM SIGSPATIAL. He is a member of Joint Steering Committee of theUbiComp and Pervasive Conference Series. He is a senior member of ACM, theIEEE, and China Computer Federation (CCF).
