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42
Privacy-Aware Mobile Services over Road Networks
"... Consider a mobile client who travels over roads and wishes to receive location-based services (LBS) from untrusted service providers. How might the user obtain such services without exposing her private position information? Meanwhile, how could the privacy protection mechanism incur no disincentive ..."
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Consider a mobile client who travels over roads and wishes to receive location-based services (LBS) from untrusted service providers. How might the user obtain such services without exposing her private position information? Meanwhile, how could the privacy protection mechanism incur no disincentive, e.g., excessive computation or communication cost, for any service provider or mobile user to participate in such a scheme? We detail this problem and present a general model for privacy-aware mobile services. A series of key features distinguish our solution from existing ones: a) it adopts the network-constrained mobility model (instead of the conventional random-waypoint model) to capture the privacy vulnerability of mobile users; b) it regards the attack resilience (for mobile users) and the query-processing cost (for service providers) as two critical measures for designing location privatization solutions, and provides corresponding analytical models; c) it proposes a robust and scalable location anonymization model, XStar, which best leverages the two measures; d) it introduces multi-folded optimizations in implementing XStar, which lead to further performance improvement. A comprehensive experimental evaluation is conducted to validate the analytical models and the efficacy of XStar. 1.
Monitoring path nearest neighbor in road networks
- In SIGMOD
, 2009
"... This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the k-NN with respect to the shortest path connecting the destination and the user’s current location, and thus ..."
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Cited by 25 (3 self)
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This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the k-NN with respect to the shortest path connecting the destination and the user’s current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the k-Path Nearest Neighbor query (k-PNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the k-PNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the k-PNN efficiently. We propose a three-phase Best-first Network Expansion (BNE) algorithm for monitoring the k-PNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the k-PNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates’ exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the k-PNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the k-PNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance.
Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing
- In Proceedings of the 21st SIGSPATIAL GIS
, 2013
"... ABSTRACT With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, ..."
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ABSTRACT With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we also propose approximation and progressive algorithms. We conducted a thorough experimental evaluation on both real-world and synthetic data to compare the performance and accuracy of our proposed approaches.
The multi-rule partial sequenced route query
- GIS
"... Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorith ..."
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Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorithms. By formulating each restriction into rules, we propose a novel form of route query, namely the multi-rule partial sequenced route (MRPSR) query. Our work provides a unified framework that also subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. In this paper, we first prove that MRPSR is NP-hard and then present three heuristic algorithms to search for near-optimal solutions for the MRPSR query. Our extensive simulations show that all of the proposed algorithms can answer the MRPSR query effectively and efficiently. Using both real and synthetic datasets, we investigate the performance of our algorithms with the metrics of the route distance and the response time in terms of the percentage of the constrained points of interest (POI) categories. Compared to the LORD-based brute-force solution, the response times of our algorithms are remarkably reduced while the resulting route length is only slightly longer than the shortest route.
Route search over probabilistic geospatial data
- In SSTD
, 2009
"... Abstract. In a route search over geospatial data, a user provides terms for specifying types of geographical entities that she wishes to visit. The goal is to find a route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geospatial entities that are relevant to ..."
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Cited by 7 (4 self)
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Abstract. In a route search over geospatial data, a user provides terms for specifying types of geographical entities that she wishes to visit. The goal is to find a route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geospatial entities that are relevant to the provided search terms. Earlier work studied the problem of finding a route that is effective in the sense that its length does not exceed a given limit, the relevancy of the objects is as high as possible, and the route visits a single object from each specified type. This paper investigates route search over probabilistic geospatial data. It is shown that the notion of an effective route requires a new definition and, specifically, two alternative semantics are proposed. Computing an effective route is more complicated, compared to the non-probabilistic case, and hence necessitates new algorithms. Heuristic methods for computing an effective route, under either one of the two semantics, are developed. (Note that the problem is NP-hard.) These methods are compared analytically and experimentally. In particular, experiments on both synthetic and realworld data illustrate the efficiency and effectiveness of these methods in computing a route under the two semantics. 1
Interactive Route Search in the Presence of Order Constraints
"... A route search is an enhancement of an ordinary geographic search. Instead of merely returning a set of entities, the result is a route that goes via entities that are relevant to the search. The input to the problem consists of several search queries, and each query defines a type of geographical e ..."
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Cited by 7 (3 self)
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A route search is an enhancement of an ordinary geographic search. Instead of merely returning a set of entities, the result is a route that goes via entities that are relevant to the search. The input to the problem consists of several search queries, and each query defines a type of geographical entities. When visited, some of the entities succeed in satisfying the user while others fail to do so; however, only the probability of success is known prior to arrival. The main task is to find a route that visits at least one satisfying entity of each type. In an interactive search, the route is computed in steps. In each step, only the next entity of the route is given to the user, and after visiting that entity, the user provides a feedback specifying whether the entity satisfies her. This paper investigates interactive route search in the presence of order constraints that specify that some types of entities should be visited before others. We present heuristic algorithms for interactive route search for two cases, depending on whether the constraints define a complete order or a partial one. The main challenge is to utilize the feedback in order to compute a route that is shorter and has a higher degree of success, compared to routes that are computed non-interactively. We also discuss how to compare the results of the algorithms and introduce suitable measures for doing so. Experiments on real-world data illustrate the efficiency and effectiveness of our algorithms. The work of these authors was supported by the German-
An interactive approach to route search
, 2009
"... In a probabilistic route search, there is a start location, a target location, and search queries Q1,..., Qn. Each Qi has an answer set Ai consisting of geo-spatial objects and their probabilities. The probability of an object o ∈ Ai specifies the likelihood that o satisfies Qi. The goal is to compu ..."
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Cited by 6 (3 self)
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In a probabilistic route search, there is a start location, a target location, and search queries Q1,..., Qn. Each Qi has an answer set Ai consisting of geo-spatial objects and their probabilities. The probability of an object o ∈ Ai specifies the likelihood that o satisfies Qi. The goal is to compute a route that is short and yet has a high probability of satisfying all the Qi. This paper investigates interactive route search. Upon arrival at each object, the user provides feedback specifying whether the object satisfies its corresponding query. The goal is to compute the next object to be visited, based on the feedback. Several heuristic algorithms are given and compared experimentally. Categories and Subject Descriptors
MWGen: A Mini World Generator
- In MDM, To Appear
, 2012
"... GMOD (Generic Moving Objects Database) is a database system that manages moving objects traveling through different environments and with multiple transportation modes, like Walk → Car → Indoor, as humans ’ movement can cover several different environments (e.g., road network, indoor) instead of a s ..."
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Cited by 5 (4 self)
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GMOD (Generic Moving Objects Database) is a database system that manages moving objects traveling through different environments and with multiple transportation modes, like Walk → Car → Indoor, as humans ’ movement can cover several different environments (e.g., road network, indoor) instead of a single environment. To evaluate the performance of GMOD, a comprehensive and scalable dataset consisting of all available environments (e.g., roads, bus network, buildings) and moving objects with multiple modes is essentially needed, where the location of a moving object is represented by referencing to the underlying environment. Due to the difficulty of gaining real datasets, in this paper we present a tool that creates the overall space, which is composed of the following environments: road network, bus network, metro network, pavement areas and indoor. Each environment is also called an infrastructure. All outdoor infrastructures are produced from a real road dataset and the indoor environment consisting of a set of buildings is generated from public floor plans. Within each infrastructure, we design a graph as well as the algorithm for trip plannings, like indoor navigation, routing in bus network. The time complexity of the algorithm is also analyzed. A complete navigation system through all environments is developed, which is used to guide data generation for moving objects covering all available environments. The generated data, including all infrastructures and moving objects, is managed by GMOD. We report the experimental results of the data generator by conducting experiments on two real road datasets and a set of public floor plans. 1
Keyword-aware Optimal Route Search
"... Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find “a most popular route such that it passes by shopping mall, restaurant, and pub, and the travel time to and from his hotel is within 4 hou ..."
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Cited by 5 (0 self)
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Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find “a most popular route such that it passes by shopping mall, restaurant, and pub, and the travel time to and from his hotel is within 4 hours. ” However, none of the algorithms in the existing work on route planning can be used to answer such queries. Motivated by this, we define the problem of keywordaware optimal route query, denoted by KOR, which is to find an optimal route such that it covers a set of user-specified keywords, a specified budget constraint is satisfied, and an objective score of the route is optimal. The problem of answering KOR queries is NP-hard. We devise an approximation algorithm OSScaling with provable approximation bounds. Based on this algorithm, another more efficient approximation algorithm BucketBound is proposed. We also design a greedy approximation algorithm. Results of empirical studies show that all the proposed algorithms are capable of answering KOR queries efficiently, while the BucketBound and Greedy algorithms run faster. The empirical studies also offer insight into the accuracy of the proposed algorithms. 1.
