Reverse-Nearest Neighbor Queries on Uncertain Moving Object Trajectories

Emrich, Tobias; Kriegel, Hans‐Peter; Mamoulis, Nikos; Niedermayer, Johannes; Renz, Matthias; Züfle, Andreas

doi:10.1007/978-3-319-05813-9_7

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…The problem of finding reverse k nearest neighbors (RkNNs) have been studied for spatial data [75,27,16,27] and spatio-temporal data [46]. Solutions for skyline queries on uncertain data have been proposed in [87,73,103,45,109].…”

Section: Advanced Techniques For Managing Uncertain Spatial Datamentioning

confidence: 99%

Uncertain Spatial Data Management:An Overview

Zuefle

2020

Preprint

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Both the current trends in technology such as smart phones, general mobile devices, stationary sensors, and satellites as we as a new user mentality of using this technology to voluntarily share enriched location information produces a flood of geo-spatial and geo-spatio-temporal data. This data flood provides a tremendous potential of discovering new and useful knowledge. But in addition to the fact that measurements are imprecise, spatial data is often interpolated between discrete observations. To reduce communication and bandwidth utilization, data is often subjected to a reduction, thereby eliminating some of the known/recorded values. These issues introduce the notion of uncertainty in the context of spatio-temporal data management, an aspect raising imminent need for scalable and flexible solutions. The main scope of this chapter is to survey existing techniques for managing, querying, and mining uncertain spatio-temporal data. First, this chapter surveys common data representations for uncertain data, explains the commonly used possible worlds semantics to interpret an uncertain database, and surveys existing system to process uncertain data. Then this chapter defines the notion of different probabilistic result semantics to distinguish the task of enrich individual objects with probabilities rather than enriched entire results with probabilities. To distinguish between result semantics is important, as for many queries, the problem of computing object-level result probabilities can be done efficiently, whereas the problem of computing probabilities of entire results is often exponentially hard. Then, this chapter provides an overview over probabilistic query predicates to quantify the required probability of a result to be included in the result. Finally, this chapter introduces a novel paradigm to efficiently answer any kind of query on uncertain data: the Paradigm of Equivalent Worlds, which groups the exponential set of possible database worlds into a polynomial number of set of equivalent worlds that can be processed efficiently. Examples and use-cases of querying uncertain spatial data are provided using the example of uncertain range queries.

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Section: Advanced Techniques For Managing Uncertain Spatial Datamentioning

confidence: 99%

Uncertain Spatial Data Management:An Overview

Zuefle

2020

Preprint

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“…The influence-factor of o is f if o is the nearest neighbor of f trajectories for all candidate points in O. Cheema et al [7] proposed a continuous reverse nearest neighbors query to monitor a moving object and find all static points that take the moving object query as a k nearest neighbor. Recently, Emrich et al [9] solved a problem of RNN search with "uncertain" moving object trajectories using a Markov model approach. A moving object is treated as a result when it always takes the query object as a nearest neighbor for every time stamp within a given time interval.…”

Section: Rknnmentioning

confidence: 99%

“…Based on the point-route distance function, the kNN search of a transition point t is defined as: In particular, two types of kNN are supported for a transition T , which can also be found in [9].…”

Section: Problem Definitionmentioning

confidence: 99%

Reverse $k$ Nearest Neighbor Search over Trajectories

Wang

Bao

Culpepper

et al. 2018

IEEE Trans. Knowl. Data Eng.

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GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query -Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT , and a query route Q, an RkNNT query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then we introduce a filter refinement framework for processing RkNNT queries using the proposed indexes. Next, we show how to use RkNNT to solve the optimal route planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route from a start location to an end location that could attract the maximum (or minimum) number of passengers based on a pre-defined travel distance threshold. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. To the best of our best knowledge, this is the first work to study the RkNNT problem for route planning.

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“…A new method for answering this kind of query on moving objects proposed by Rahmati [39]. Another research on RNN by considering uncertain movement of objects has been performed by Emrich [40]. A recent study on reverse k nearest neighbour has been done by Yang [41].…”

Section: B Rnn Querymentioning

confidence: 99%

Continuous Mutual Nearest Neighbour Processing on Moving Objects in Spatiotemporal Datasets

Ghorbani¹,

Mobini²,

Minaei‐Bidgoli³

2017

IJIET

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Abstract-This paper proposed a new algorithm for answering a novel kind of nearest neighbour search, that is, continuous mutual nearest neighbour (CMNN) search. In this kind of query, by providing a set of objects O and a query object q, CMNN continuously returns the set of objects from O, which is among the k 1 nearest neighbours of q; meanwhile, q is one of their k 2 nearest neighbours. CMNN queries are important in many applications such as decision making, pattern recognition and although it is useful in service providing systems, such as police patrol, taxi drivers, mobile car repairs and so forth. In this paper, we have proposed the first work for handling CMNN queries efficiently, without any assumption on object movements. The most important feature of this work is incremental evaluation and scalability. Utilizing an incremental evaluation technique led to a significant decrease in processing time.Index Terms-Moving objects, nearest neighbor, query processing, spatio-temporal.

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Reverse-Nearest Neighbor Queries on Uncertain Moving Object Trajectories

Cited by 13 publications

References 26 publications

Uncertain Spatial Data Management:An Overview

Uncertain Spatial Data Management:An Overview

Reverse $k$ Nearest Neighbor Search over Trajectories

Continuous Mutual Nearest Neighbour Processing on Moving Objects in Spatiotemporal Datasets

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