VLDB Database School (China)

VLDB中国数据库学院

2014年暑期学校招生简章

2014年7月7日~ 7月12日

中国 • 北京

受VLDB（Very Large Databases）基金会资助的VLDB中国数据库学院始办于2002年，隶属于中国计算机学会数据库专业委员会, 创办人陆宏钧教授担任首任院长，自2005年起由王珊教授担任院长。根据2013年6月专委会主任会议意见，自2014年起成立由周傲英、李战怀和王国仁三位教授组成的工作小组负责学院的相关事宜。

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年暑期学校由中国人民大学信息学院承办，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：杜小勇　教授（中国人民大学）

                周傲英　教授（华东师范大学）

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 Management Foundations for Location-Based Services

  Course instructor: Christian Jensen, Professor at Aalborg University, Denmark

  Course abstract:

An increasingly sophisticated infrastructure that encompasses geo-positioning capabilities and Internet-worked mobile computing devices is becoming available to rapidly growing numbers of users. Concurrently, the research community has invented foundations that enable location-based services that exploit this infrastructure. 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 data management foundations for location-based services. In this setting, the location of a mobile object is captured by a trajectory, which is a function from time to points in the Euclidean, spatial-network, or indoor space in which the movement occurs. Tracking denotes the process of using a positioning system for continuously maintaining an up-to-date representation of a (partial) trajectory of an object. Prediction relates to aspects of the future trajectories of objects. Systems underlying location-based services may be subject to workloads that involve frequent updates as well as queries. This calls for efficient update as well as query processing techniques, and this in turn calls for efficient indexing techniques.  A number of 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 of the problem domain.

The following topics are planned: 1. Motivation; 2. Tracking and route prediction; 3. Indexing: bottom-up updates, the TPR-tree family, and the Bx-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 Mobile Computing and Location Based Services

                (3) Trajectory Mining: From subtrajectories, whole trajectories to Trajectory Pattern

Course Instructor: Ling Liu, Professor at Georgia Institute of Technology, USA

  Course abstract:

(1) Spatial alarms are one of the fundamental capabilities for enabling personalization of location-based services (LBSs), especially location-based advertisement, location based entertainment and location based information dissemination. In this lecture, I will describe the alternative architectures and algorithms for scaling spatial alarm processing. We will cover three types of system architectural design: client-centric architecture, client-server architecture, and distributed architecture. The algorithms and optimization techniques for efficient processing of spatial alarms include safe region techniques for optimizing client energy efficiency, server loads, and network bandwidth efficiency. For example, in a distributed architecture, I will describe the fundamentals of safe region-based processing and discuss the suite of techniques for optimal distribution of partial alarm processing tasks from the server to the mobile clients while minimizing unnecessary alarm evaluations at both server and mobile clients. I will also describe different safe region computation algorithms to explore the impact of size and shape of the safe region on network bandwidth, server load and client energy consumption. The development of these alternative safe region computation techniques is critical for supporting client device heterogeneity and scaling spatial alarm processing.

  (2)  We are entering the mobile Internet era where people, vehicles, and hand-held devices are connected at all times. Location becomes a piece of important information for real-time information access, on-demand service discovery and delivery, as well as continuous and personalized service provision. In location-based services, there are conceivably two types of location privacy - personal subscriber level privacy and corporate enterprise-level privacy. Companies need enterprise-level privacy to preserve corporate secrets and maintain competitive edge. Location privacy has attracted attention in mobile computing, mobile data management, and wireless communication research over the past few years. Most of the location privacy solutions try to prevent disclosure of unauthorized location information by techniques that explicitly or implicitly control what and how location information is given to whom and when. In this lecture, I will give an overview of location privacy research and discuss three categories of location privacy protection techniques: (1) Location protection through user-defined or system-supplied privacy policies; (2) Location protection through location anonymization, a system capability to obfuscate the location information such that a state of a subject is not identifiable within the anonymity set; and (3) Location protection through pseudonymity of user identities, which uses an internal pseudonym rather than the user’s actual identity. I will also describe the intrinsic relationships among location privacy, location utility, and personalization. My lecture will end with a list of open issues and technical challenges in location privacy research.

(3) Mining mobile object trajectory datasets has been gaining significant interest in recent years. We can broadly classify the existing research into three categories: mining subtrajectories of mobile objects, mining whole trajectories of mobile objects, and mining trajectory patterns of mobile objects. Density and Euclidean distance measures are commonly used by most of existing approaches to trajectory mining. In this lecture, we show that when the utility of  mining mobile object trajectories is targeted at road network aware location based applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters of mobile objects travelling on road networks. I will describe one or two technical approaches under each of the three categories mentioned above. For example, by taking into account the physical constraints of the road network, the network proximity and the traffic flows among consecutive road segments, the trajectory mining can produe groups of sub-trajectories that describe both dense and highly continuous traffic flows of mobile objects. I will discuss the technical challenges in mining sparial trajectories. 

 

3. Course Title: User Understanding from Large Scale Human Behavioral Data

Course Instructor: Doctor Xing Xie, MSRA(微软亚洲研究院)研究员

  Course abstract:

With the rapid development of positioning, sensor and smart device technologies, large quantities of human behavioral data are now readily available. They reflect various aspects of human mobility and activities in the physical world. The availability of this data presents an unprecedented opportunity to gain a more in depth understanding of users and provide them with personalized online experience while respecting their privacy. In this course, I will present a number of our recent research efforts on this direction, including user mobility understanding and prediction, location and activity recommendation, user linking across multiple networks, psychological trait 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。
9. 网上报名截止时间：2014年 6月 25日

联系人

 

姓名	电子邮箱	电话
李翠平	licuiping@ruc.edu.cn
赵素云	zhaosuyun@ruc.edu.cn	13671391782
史晓薇	vldbss2014@163.com	18810762516

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 VLDB中国数据库学院2014年度暑期学校报名表

VLDB中国数据库学院2014年度暑期学校讲师介绍