Bolong Zheng

Interactive Top-k Spatial Keyword queries

Conventional top-k spatial keyword queries require users to explicitly specify their preferences between spatial proximity and keyword relevance. In this work we investigate how to eliminate this requirement by enhancing the conventional queries with interaction, resulting in Interactive Top-k Spatial Keyword (ITkSK) query. Having confirmed the feasibility by theoretical analysis, we propose a three-phase solution focusing on both effectiveness and efficiency. The first phase substantially narrows down the search space for subsequent phases by efficiently retrieving a set of geo-textual k-skyband objects as the initial candidates. In the second phase three practical strategies for selecting a subset of candidates are developed with the aim of maximizing the expected benefit for learning user preferences at each round of interaction. Finally we discuss how to determine the termination condition automatically and estimate the preference based on the users feedback. Empirical study based on real PoI datasets verifies our theoretical observation that the quality of top-k results in spatial keyword queries can be greatly improved through only a few rounds of interactions.

SharkDB: An In-Memory Column-Oriented Trajectory Storage

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of in-memory databases, we try to explore the potential opportunities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss. The independence between frames also makes them natural working units when parallelizing data processing on a multi-core environment. Lastly we run a variety of common trajectory queries on both real and synthetic datasets in order to demonstrate advantages and study the limitations of our proposed storage.

Keyword-aware continuous kNN query on road networks

It is nowadays quite common for road networks to have textual contents on the vertices, which describe auxiliary information (e.g., business, traffic, etc.) associated with the vertex. In such road networks, which are modelled as weighted undirected graphs, each vertex is associated with one or more keywords, and each edge is assigned with a weight, which can be its physical length or travelling time. In this paper, we study the problem of keyword-aware continuous k nearest neighbour (KCkNN) search on road networks, which computes the k nearest vertices that contain the query keywords issued by a moving object and maintains the results continuously as the object is moving on the road network. Reducing the query processing costs in terms of computation and communication has attracted considerable attention in the database community with interesting techniques proposed. This paper proposes a framework, called a Labelling AppRoach for Continuous kNN query (LARC), on road networks to cope with KCkNN query efficiently. First we build a pivot-based reverse label index and a keyword-based pivot tree index to improve the efficiency of keyword-aware k nearest neighbour (KkNN) search by avoiding massive network traversals and sequential probe of keywords. To reduce the frequency of unnecessary result updates, we develop the concepts of dominance interval and region on road network, which share the similar intuition with safe region for processing continuous queries in Euclidean space but are more complicated and thus require more dedicated design. For high frequency keywords, we resolve the dominance interval when the query results changed. In addition, a path-based dominance updating approach is proposed to compute the dominance region efficiently when the query keywords are of low frequency. We conduct extensive experiments by comparing our algorithms with the state-of-the-art methods on real data sets. The empirical observations have verified the superiority of our proposed solution in all aspects of index size, communication cost and computation time.

Popularity-aware spatial keyword search on activity trajectories

The proliferation of GPS-enabled smart mobile devices enables us to collect a large-scale trajectories of moving objects with GPS tags. While the raw trajectories that only consists of positional information have been studied extensively, many recent works have been focusing on enriching the raw trajectories with semantic knowledge. The resulting data, called activity trajectories, embed the information about behaviors of the moving objects and support a variety of applications for better quality of services. In this paper, we propose a Top-k Spatial Keyword (TkSK) query for activity trajectories, with the objective to find a set of trajectories that are not only close geographically but also meet the requirements of the query semantically. Such kind of query can deliver more informative results than existing spatial keyword queries for static objects, since activity trajectories are able to reflect the popularity of user activities and reveal preferable combinations of facilities. However, it is a challenging task to answer this query efficiently due to the inherent difficulties in indexing trajectories as well as the new complexity introduced by the textual dimension. In this work, we provide a comprehensive solution, including the novel similarity function, hybrid indexing structure, efficient search algorithm and further optimizations. Extensive empirical studies on real trajectory set have demonstrated the scalability of our proposed solution.

SharkDB: an in-memory column-oriented storage for trajectory analysis

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of in-memory databases, we try to explore the potential opportunities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss. The independence between frames also makes them natural working units when parallelizing data processing on a multi-core environment. Lastly we run a variety of common trajectory queries on both real and synthetic datasets in order to demonstrate advantages and study the limitations of our proposed storage.

Cost-Efficient Spatial Network Partitioning for Distance-Based Query Processing

The efficiency of spatial query processing is crucial for many applications such as location-based services. In spatial networks, queries like k-NN queries are all based on network distance evaluation. Classic solutions for these queries rely on network expansion and are not efficient enough for large networks. Some approaches have improved the query efficiency but brought considerable space cost for index. To address these problems, we propose a hierarchical graph partitioning based index named Partition Tree. It organizes the vertices of a spatial network into a hierarchy through a series of graph partitioning processes. Meanwhile precomputed distances are associated with this hierarchy to facilitate efficient query processing. Inspired by the observation that queries are usually invoked around objects of interest, we propose a query-oriented optimization on top of the Partition Tree. It uses a cost model to evaluate the influence of the object distribution and partitioning topology on the query efficiency. Then a cost-efficient graph partitioning method is developed based on this cost model. Experimental results on real datasets demonstrate that our proposed index and algorithms have superior performance over the state-of-the-art approaches and are scalable to large spatial networks.

Landmark-Based Route Recommendation with Crowd Intelligence

Route recommendation is one of the most widely used location-based services nowadays, as it is vital for nice-driving experience and smooth public traffic. Given a pair of user-specified origin and destination, a route recommendation service aims to provide users with the routes of the best travelling experience according to given criteria. However, even the routes recommended by the big-thumb service providers can deviate significantly from the ones travelled by experienced drivers, which motivates the previous research that leverages crowds’ knowledge to improve the recommendation quality. Since route recommendation is normally an online task, low-latency response to drivers’ queries is required in this kind of systems. Unfortunately, latency of crowdsourced systems is usually high, because they need to generate tasks and wait for workers’ feedbacks before answering queries. To address this issue, we extend our previous system—CrowdPlanner—by proposing some strategies to reuse existing answers (truths) to deal with newly coming queries more efficiently. A prototype system has been deployed to many voluntary mobile clients and extensive tests on real-scenario queries have shown the superiority of our system in comparison with the results given by map services and popular route-mining algorithms.

Probesim: scalable single-source and top-k simrank computations on dynamic graphs

Single-source and top-

Location Prediction in Social Networks

k

Predicting Passenger’s Public Transportation Travel Route Using Smart Card Data

SimRank queries are two important types of similarity search in graphs with numerous applications in web mining, social network analysis, spam detection, etc. A plethora of techniques have been proposed for these two types of queries, but very few can efficiently support similarity search over large dynamic graphs, due to either significant preprocessing time or large space overheads. This paper presents ProbeSim, an index-free algorithm for single-source and top-