Man Lung Yiu

Group-by skyline query processing in relational engines

The skyline operator was first proposed in 2001 for retrieving interesting tuples from a dataset. Since then, 100+ skyline-related papers have been published; however, we discovered that one of the most intuitive and practical type of skyline queries, namely, group-by skyline queries remains unaddressed. Group-by skyline queries find the skyline for each group of tuples. In this paper, we present a comprehensive study on processing group-by skyline queries in the context of relational engines. Specifically, we examine the composition of a query plan for a group-by skyline query and develop the missing cost model for the BBS algorithm. Experimental results show that our techniques are able to devise the best query plans for a variety of group-by skyline queries. Our focus is on algorithms that can be directly implemented in todays commercial database systems without the addition of new access methods (which would require addressing the associated challenges of maintenance with updates, concurrency control, etc.).

Discovery of convoys in trajectory databases

As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the well-known filter-refinement framework. In the filter step, we apply line-simplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the papers proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.

SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services

In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the users actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.

Aggregate nearest neighbor queries in road networks

Aggregate nearest neighbor queries return the object that minimizes an aggregate distance function with respect to a set of query points. Consider, for example, several users at specific locations (query points) that want to find the restaurant (data point), which leads to the minimum sum of distances that they have to travel in order to meet. We study the processing of such queries for the case where the position and accessibility of spatial objects are constrained by spatial (e.g., road) networks. We consider alternative aggregate functions and techniques that utilize Euclidean distance bounds, spatial access methods, and/or network distance materialization structures. Our algorithms are experimentally evaluated with synthetic and real data. The results show that their relative performance depends on the problem characteristics.Aggregate nearest neighbor queries return the object that minimizes an aggregate distance function with respect to a set of query points. Consider, for example, several users at specific locations ...

PAD: privacy-area aware, dummy-based location privacy in mobile services

Location privacy in mobile services has the potential to become a serious concern for service providers and users. Existing privacy protection techniques that use k-anonymity convert an original query into an anonymous query that contains the locations of multiple users. Such techniques, however, generally fail in offering guaranteed large privacy regions at reasonable query processing costs. In this paper, we propose the PAD approach that is capable of offering privacy-region guarantees. To achieve this, PAD uses so-called dummy locations that are deliberately generated according to either a virtual grid or circle. These cover a users actual location, and their spatial extents are controlled by the generation algorithms. The PAD approach only requires a lightweight server-side front-end in order for it to be integrated into an existing client/server mobile service system. In addition, query results are organized according to a compact format on the server, which not only reduces communication cost, but also facilitates the result refinement on the client side. An empirical study shows that our proposal is effective in terms of offering location privacy, and efficient in terms of computation and communication costs.

Path prediction and predictive range querying in road network databases

In automotive applications, movement-path prediction enables the delivery of predictive and relevant services to drivers, e.g., reporting traffic conditions and gas stations along the route ahead. Path prediction also enables better results of predictive range queries and reduces the location update frequency in vehicle tracking while preserving accuracy. Existing moving-object location prediction techniques in spatial-network settings largely target short-term prediction that does not extend beyond the next road junction. To go beyond short-term prediction, we formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical object trajectories. The model aims to capture the turning patterns at junctions and the travel speeds on road segments at the level of individual objects. Based on the mobility model, we present a maximum likelihood and a greedy algorithm for predicting the travel path of an object (for a time duration h into the future). We also present a novel and efficient server-side indexing scheme that supports predictive range queries on the mobility statistics of the objects. Empirical studies with real data suggest that our proposals are effective and efficient.

The Bdual-Tree: indexing moving objects by space filling curves in the dual space

Existing spatiotemporal indexes suffer from either large update cost or poor query performance, except for the Bx-tree (the state-of-the-art), which consists of multiple B+-trees indexing the 1D values transformed from the (multi-dimensional) moving objects based on a space filling curve (Hilbert, in particular). This curve, however, does not consider object velocities, and as a result, query processing with a Bx-tree retrieves a large number of false hits, which seriously compromises its efficiency. It is natural to wonder “can we obtain better performance by capturing also the velocity information, using a Hilbert curve of a higher dimensionality?”. This paper provides a positive answer by developing the Bdual-tree, a novel spatiotemporal access method leveraging pure relational methodology. We show, with theoretical evidence, that the Bdual-tree indeed outperforms the Bx-tree in most circum- stances. Furthermore, our technique can effectively answer progressive spatiotemporal queries, which are poorly supported by Bx-trees.

Efficient continuously moving top-k spatial keyword query processing

Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content.

Clustering objects on a spatial network

Clustering is one of the most important analysis tasks in spatial databases. We study the problem of clustering objects, which lie on edges of a large weighted spatial network. The distance between two objects is defined by their shortest path distance over the network. Past algorithms are based on the Euclidean distance and cannot be applied for this setting. We propose variants of partitioning, density-based, and hierarchical methods. Their effectiveness and efficiency is evaluated for collections of objects which appear on real road networks. The results show that our methods can correctly identify clusters and they are scalable for large problems.

Probabilistic spatial queries on existentially uncertain data

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.