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Ride-sharing (RS) has great values in saving energy and alleviating traffic pressure. Existing studies can be improved for better efficiency. Therefore, we propose a new ride-sharing model, where each driver has a requirement that if the driver shares a ride with a rider, the shared route percentage (i.e., the ratio of the shared route's distance to the driver's total travel distance) exceeds an expectation rate of the driver, e.g., 0.8. We consider two variants of this problem. The first considers multiple drivers and multiple riders and aims to compute driver-rider pairs to maximize the overall shared route percentage (SRP). We model this problem as the maximum weighted bigraph matching problem, where the vertices are drivers and riders, edges are driver-rider pairs, and edge weights are driver-rider's SRP. However, it is rather expensive to compute the SRP values for large numbers of driver-rider pairs on road networks. To address this problem, we propose an efficient method to prune many unnecessary driver-rider pairs and avoid computing the SRP values for every pair. To improve the efficiency, we propose an approximate method with error bound guarantee. The basic idea is that we compute an upper bound and a lower bound for each driver-rider pair in constant time. Then, we estimate an upper bound and a lower bound of the graph matching. Next, we select some driver-rider pairs, compute their real shortest-route distance, and update the lower and upper bounds of the maximum graph matching. We repeat above steps until the ratio of the upper bound to the lower bound is not larger than a given approximate rate. The second considers multiple drivers and a single rider and aims to find the top- $k$ drivers for the rider with the largest SRP. We first prune a large number of drivers that cannot meet the SRP requirements. Then, we propose a best-first algorithm that progressively selects the drivers with high probability to be in the top- $k$ results and prunes the drivers that cannot be in the top- $k$ results. Extensive experiments on real-world datasets demonstrate the superiority of our method.

SimRank is a widely adopted similarity measure for objects modeled as nodes in a graph, based on the intuition that two objects are similar if they are referenced by similar objects. The recursive nature of SimRank definition makes it expensive to compute the similarity score even for a single pair of nodes. This defect limits the applications of SimRank. To speed up the computation, some existing works replace the original model with an approximate model to seek only rough solution of SimRank scores. In this work, we propose a novel solution for computing all-pair SimRank scores. In particular, we propose to convert SimRank to the problem of solving a linear system in matrix form, and further prove that the system is non-singular, diagonally dominate, and symmetric definite positive (for undirected graphs). Those features immediately lead to the adoption of Conjugate Gradient (CG) and Bi-Conjugate Gradient (BiCG) techniques for efficiently computing SimRank scores. As a result, a significant improvement on the convergence rate can be achieved; meanwhile, the sparsity of the adjacency matrix is not damaged all the time. Inspired by the existing common neighbor sharing strategy, we further reduce the computational complexity of the matrix multiplication and resolve the scalable issues. The experimental results show our proposed algorithms significantly outperform the state-of-the-art algorithms.

The $k$ nearest neighbor ( $k$ NN) search on road networks is an important function in web mapping services. These services are now dealing with rapidly arriving queries, that are issued by a massive amount of users. While overlay graph-based indices can answer shortest path queries efficiently, there have been no studies on utilizing such indices to answer $k$ NN queries efficiently. In this paper, we fill this research gap and present two efficient $k$ NN search solutions on overlay graph-based indices. Experimental results show that our solutions offer very low query latency (0.1 ms) and require only small index sizes, even for 10-million-node networks.

In the recent decades, we have witnessed the rapidly growing popularity of location-based systems. Three types of location-based queries on road networks, single-pair shortest path query, $k$ nearest neighbor ( $k$ NN) query, and keyword-based $k$ NN query, are widely used in location-based systems. Inspired by $\tt R$ - $\tt tree$ , we propose a height-balanced and scalable index, namely $\tt G$ - $\tt tree$ , to efficiently support these queries. The space complexity of $\tt G$ - $\tt tree$ is $\mathcal {O}(|\mathcal {V}|\log {|\mathcal {V}|})$ where ${|\mathcal {V}|}$ is the number of vertices in the road network. Unlike previous works that support these queries separately, $\tt G$ - $\tt tree$ supports all these queries within one framework. The basis for this framework is an assembly-based method to calculate the shortest-path distances between two vertices. Based on the assembly-based method, efficient search algorithms to answer $k$ NN queries and keyword-based $k$ NN queries are developed. Experiment results show $\tt G$ - $\tt tree$ ’s theoretical and practical superiority over existing methods.

A large number of web data repositories are hidden behind restrictive web interfaces, making it an important challenge to enable data analytics over these hidden web databases. Most existing techniques assume a form-like web interface which consists solely of categorical attributes (or numeric ones that can be discretized). Nonetheless, many real-world web interfaces (of hidden databases) also feature checkbox interfaces—e.g., the specification of a set of desired features, such as A/C, navigation, etc., for a car-search website like Yahoo! Autos. We find that, for the purpose of data analytics, such checkbox-represented attributes differ fundamentally from the categorical/numerical ones that were traditionally studied. In this paper, we address the problem of data analytics over hidden databases with checkbox interfaces. Extensive experiments on both synthetic and real datasets demonstrate the accuracy and efficiency of our proposed algorithms.

Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers’ messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R - tree based index by integrating textual descriptions into R - tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer

The online shortest path problem aims at computing the shortest path based on live traffic circumstances. This is very important in modern car navigation systems as it helps drivers to make sensible decisions. To our best knowledge, there is no efficient system/solution that can offer affordable costs at both client and server sides for online shortest path computation. Unfortunately, the conventional client-server architecture scales poorly with the number of clients. A promising approach is to let the server collect live traffic information and then broadcast them over radio or wireless network. This approach has excellent scalability with the number of clients. Thus, we develop a new framework called live traffic index (LTI)which enables drivers to quickly and effectively collect the live traffic information on the broadcasting channel. An impressive result is that the driver can compute/update their shortest path result by receiving only a small fraction of the index. Our experimental study shows that LTI is robust to various parameters and it offers relatively short tune-in cost (at client side), fast query response time (at client side), small broadcast size (at server side), and light maintenance time (at server side)for online shortest path problem.