What's Inside

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chi-Yin Chow (Ted)

Assistant Professor


Department of Computer Science
City University of Hong Kong
83 Tat Chee Avenue
Kowloon
Hong Kong

office: Room Y6423, 6/F, Academic Building
phone: +852-3442-8679
email: chiychow@cityu.edu.hk
homepage: http://www.cs.cityu.edu.hk/~chiychow
Research interests: Big Data Analytics, Urban Computing, Machine Learning, Databases, Spatial and Spatio-temporal Databases, Location-based Services, Wireless Sensor Networks, Mobile Peer-to-Peer Computing, and Location Privacy

  News
 

I reveived VLDB 2016 10-Year Best Paper Award in The 42nd International Conference on Very Large Data Bases on September 8, 2016 in New Delhi, India(VLDB 2016) for my VLDB paper "The New Casper: Query Processing for Location Services without Compromising Privacy", co-authorized by Mohamed F. Mokbel, Chi-Yin Chow, Walid G. Aref and published in VLDB 2006 for inspiring research to support privacy in spatial query processing.


YouTube video and Pictures: 1, 2, 3, 4, 5, and 6

My application project "A one-stop self-service platform for foodies and restaurants" was featured in CityU Today Magazine (January 2015) and Singtao Daily (March 24, 2015)

My application project "Location-aware video streaming" was featured in PC Market Magazine (March 2013)

 


  Selected Research Projects
 

 
Recommender systems have become very common in recent years and are applied in various valuable applications to recommend items, e.g., news, articles, books, music, movies, search queries, products, and services. The key tasks of a recommender system are to model a user’s preferences on an item and return the most desired items to the user. In this project, we aim to recommend points of interests (POIs) based on social interactions, geographical mobility, visiting sequence, categorical preferences, aspect interests, and opinion expression. Advanced machine learning models are built to provide high-quality recommendations for social networks in terms of scalability, computational efficiency, precision, and recall. The results of this project have been reported in IEEE TSC 2016, IEEE TKDE 2016, IEEE TSC 2015, ACM TIST 2015, ACM CIKM 2015, ACM SIGIR 2015, Information Sciences 2015, ACM SIGSPATIAL 2014, and ACM SIGSPATIAL 2013.

 
In this project, we aim to using machine learning and optimization techniques to solve real-world problems using real large scale spatio-temporal data sets (for example, mobile broadband data, trajectories of electrical vehicles, trajectories of taxis, and cellular data dumps) in realistic settings. Especially, we are interested in real-world spaito-temporal problems, for example, sampling techniques for trajectory data, optimizing the placement of charging stations for electrical vehicles, optimizing the placement of points of interest (POIs), and designing intelligent transportation systems. The results of this project have been reported in IEEE SmartCity 2016, GeoInformatica 2016, IEEE ICDE 2015, ACM CIKM 2015, ACM SIGSPATIAL 2015, and ACM SIGSPATIAL 2014.

  A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. MobiFeed is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. We have further extended MobiFeed to schedule locaton-based advertising in temporary social networks (TSNs). A TSN is confined to a specific place (e.g., hotel and shopping mall) or activity (e.g., concert and exhibition) in which the TSN service provider allows nearby third party vendors (e.g., restaurants and stores) to advertise their goods or services to its registered users based on their preferences and locations. The results of this project have been reported in IEEE TSC 2016, ACM SIGSPATIAL 2013, and ACM SIGSPATIAL 2012.

  Spatial mashups are server-side Web-based applications that retrieve the real-time turn-by-turn directions and travel times between two location points from a Web mapping service provider, e.g., Google Maps and Microsoft Bing Maps. This project will develop a spatial mashup framework for processing location-based queries in a time-dependent road network environment where the distance metric is based on dynamic travel time. Unfortunately, spatial mashups have several major limitations, e.g., processing an external Web mapping request is much more expensive than accessing local data sources and existing Web mapping services only support simple spatial operations. To support more valuable location-based services, the proposed framework will support various popular types of location-based queries, e.g., nearest-neighbor (NN) queries, reverse-NN queries, and aggregate NN queries, and moving data objects. In addition, we will design information sharing and parallel processing techniques to minimize the number of external Web mapping requests and query processing time. The results of this project have been reported in IEEE TC 2016, APWeb 2016, DAPD 2013, and SSTD 2011.

  Monitoring personal locations with a potentially untrusted server poses privacy threats to the monitored individuals. To this end, we propose a privacy-preserving location monitoring system for wireless sensor networks. In our system, we design two in-network location anonymization algorithms, namely, resource- and quality-aware algorithms, that aim to enable the system to provide high quality location monitoring services for system users, while preserving personal location privacy. Both algorithms rely on the well established k-anonymity privacy concept to enable trusted sensor nodes to provide the aggregate location information of monitored persons for our system. Each aggregate location is in a form of a monitored area A along with the number of monitored persons residing in A, where A contains at least k persons. The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas. To utilize the aggregate location information to provide location monitoring services, we use a spatial histogram approach that estimates the distribution of the monitored persons based on the gathered aggregate location information. The estimated distribution is used to provide location monitoring services through answering range queries. The results of this project have been reported in IEEE TDSC 2015, IEEE TMC 2011, MDM 2009 (Best Paper Aaward), and ACM SIGMOD 2008 (Demo). [Video]


  This project tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users’ exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. The results of this project have been reported in GeoInformatica, MDM 2010, ACM TODS 2009, SSTD 2009, IEEE ICDE 2007 (Demo), SSTD 2007, VLDB 2006 (10-Year Best Paper Award), and ACM GIS 2006. [Video]


  Caching is a key technique for improving the data retrieval performance of mobile client. The emergence of the state-of-the-art peer-to-peer communication technologies now brings to reality what we call "cooperative caching" in which mobile clients can help one another in caching. They not only can retrieve data items from mobile support stations, but also from the cache in their peers, realizing a new dimension for mobile data caching. In this project, we have proposed a COoperative CAching scheme, called COCA, which can be tailored for pull-based, push-based and hybrid mobile environments. Also, we have proposed three centralized and distributed techniques to improve system performance, data replica allocation, cache signature scheme, group-based cooperative cache management. The results of this project have been reported in IEEE J-SAC 2007 and MDM 2005, ICPP 2004, and ICA3PP 2015 (Best Paper Award).