Long Guo

Efficient continuous top-k spatial keyword queries on road networks

With the development of GPS-enabled mobile devices, more and more pieces of information on the web are geotagged. Spatial keyword queries, which consider both spatial locations and textual descriptions to find objects of interest, adapt well to this trend. Therefore, a considerable number of studies have focused on the interesting problem of efficiently processing spatial keyword queries. However, most of them assume Euclidean space or examine a single snapshot query only. This paper investigates a novel problem, namely, continuous top-k spatial keyword queries on road networks, for the first time. We propose two methods that can monitor such moving queries in an incremental manner and reduce repetitive traversing of network edges for better performance. Experimental evaluation using large real datasets demonstrates that the proposed methods both outperform baseline methods significantly. Discussion about the parameters affecting the efficiency of the two methods is also presented to reveal their relative advantages.

Location-Aware Pub/Sub System: When Continuous Moving Queries Meet Dynamic Event Streams

In this paper, we propose a new location-aware pub/sub system, Elaps, that continuously monitors moving users subscribing to dynamic event streams from social media and E-commerce applications. Users are notified instantly when there is a matching event nearby. To the best of our knowledge, Elaps is the first to take into account continuous moving queries against dynamic event streams. Like existing works on continuous moving query processing,Elaps employs the concept of safe region to reduce communication overhead. However, unlike existing works which assume data from publishers are static, updates to safe regions may be triggered by newly arrived events. In Elaps, we develop a concept called \textit{impact region} that allows us to identify whether a safe region is affected by newly arrived events. Moreover, we propose a novel cost model to optimize the safe region size to keep the communication overhead low. Based on the cost model, we design two incremental methods, iGM and idGM, for safe region construction. In addition, Elaps uses boolean expression, which is more expressive than keywords, to model user intent and we propose a novel index, BEQ-Tree, to handle spatial boolean expression matching. In our experiments, we use geo-tweets from Twitter and venues from Foursquare to simulate publishers and boolean expressions generated from AOL search log to represent users intentions. We test user movement in both synthetic trajectories and real taxi trajectories. The results show that Elaps can significantly reduce the communication overhead and disseminate events to users in real-time.

Elaps: An efficient location-aware pub/sub system

The prevalence of social networks and mobile devices has facilitated the real-time dissemination of local events such as sales, shows and exhibitions. To explore nearby events, mobile users can query a location based search engine for the desired data. However, operating under such a pull based model means that users may miss interesting events (because no explicit queries are issued) or processing/communication overheads may be high (because users have to continuously issue queries). In this demo, we present Elaps, an efficient location-aware publish/subscribe system that can effectively disseminate interesting events to moving users. Elaps is based on the push model and notifies mobile users instantly whenever there is a matching event around their locations. Through the demo, we will demonstrate that Elaps is scalable to a large number of subscriptions and events. Moreover, Elaps can effectively monitor the subscribers without missing any event matching, and incur low communication overhead.

Targeted Advertising in Public Transportation Systems with Quantitative Evaluation

In spite of vast business potential, targeted advertising in public transportation systems is a grossly unexplored research area. For instance, SBS Transit in Singapore can reach 1 billion passengers per year but the annual advertising revenue contributes less than

CO 2 : Inferring Personal Interests From Raw Footprints by Connecting the Offline World with the Online World

35 million. To bridge the gap, we propose a probabilistic data model that captures the motion patterns and user interests so as to quantitatively evaluate the impact of an advertisement among the passengers. In particular, we leverage hundreds of millions of bus/train boarding transaction records to quantitatively estimate the probability as well as the extent of a user being influenced by an ad. Based on the influence model, we study a top-k retrieval problem for bus/train ad recommendation, which acts as a primitive operator to support various advanced applications. We solve the retrieval problem efficiently to support real-time decision making. In the experimental study, we use the dataset from SBS Transit as a case study to verify the effectiveness and efficiency of our proposed methodologies.

Influence Maximization in Trajectory Databases

User-generated trajectories (UGTs), such as travel records from bus companies, capture rich information of human mobility in the offline world. However, some interesting applications of these raw footprints have not been exploited well due to the lack of textual information to infer the subject’s personal interests. Although there is rich semantic information contained in the spatial- and temporal-aware user-generated contents (STUGC) published in the online world, such as Twitter, less effort has been made to utilize this information to facilitate the interest discovery process. In this article, we design an effective probabilistic framework named CO2 to <underline>c</underline>onnect the <underline>o</underline>ffline world with the <underline>o</underline>nline world in order to discover users’ interests directly from their raw footprints in UGT. CO2 first infers trip intentions by utilizing the semantic information in STUGC and then discovers user interests by aggregating the intentions. To evaluate the effectiveness of CO2, we use two large-scale real-world datasets as a case study and further conduct a questionnaire survey to show the superior performance of CO2.