Finding time period-based most frequent path in big trajectory data

Luo, W.; Tan, Haoyu; Chen, Lei; Ni, Lionel M.

doi:10.1145/2463676.2465287

Cited by 196 publications

(116 citation statements)

References 29 publications

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“…The conventional mining scheme of trajectory data mainly aims at finding some spatio-temporal patterns from a set of raw GPS records. Those spatiotemporal patterns include frequent routes [4,10,5,19], clusters of common sub-trajectories [15,17], frequently colocating moving objects [11,13,16,22,30], to name a few, each of which itself has produced a branch of works as a sub topic of trajectory pattern mining. A systematic review on all those sub topics is beyond the scope of this paper, and full survey articles are available [31,9].…”

Section: Related Workmentioning

confidence: 99%

“…When we grow pattern S by appending each category c to S, we invoke RouteGrow to obtain the set of all the pRoutes of the extended pattern, denoted by S + (Lines 8-10). Whenever we grow each pRoute, if the previously extended pRoute includes the pRoute currently extended, we delete the previous one as it is no longer compact (Lines [19][20]. Note that we do not have to check all the previous pRoutes by accessing all the pRoutes in the same trajectory in the ascending order of their starting positions.…”

Section: Definition 15 (Compact Sequential Pattern Mining)mentioning

confidence: 99%

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Efficient mining of regional movement patterns in semantic trajectories

2017

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Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of human movement, which can widely be used in many applications such as location-based advertising, road capacity optimisation, and urban planning. However, most of existing works on semantic trajectory pattern mining focus on the entire spatial area, leading to missing some locally significant patterns within a region. Based on this motivation, this paper studies a regional semantic trajectory pattern mining problem, aiming at identifying all the regional sequential patterns in semantic trajectories. Specifically, we propose a new density scheme to quantify the frequency of a particular pattern in space, and thereby formulate a new mining problem of finding all the regions in which such a pattern densely occurs. For the proposed problem, we develop an e cient mining algorithm, called RegMiner (Regional Semantic Trajectory Pattern Miner), which e↵ec-tively reveals movement patterns that are locally frequent in such a region but not necessarily dominant in the entire space. Our empirical study using real trajectory data shows that RegMiner finds many interesting local patterns that are hard to find by a state-of-the-art global pattern mining scheme, and it also runs several orders of magnitude faster than the global pattern mining algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Definition 15 (Compact Sequential Pattern Mining)mentioning

confidence: 99%

Efficient mining of regional movement patterns in semantic trajectories

2017

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“…The TS-Join may bring significant benefits to a range of applications, including trajectory near-duplicate detection, data cleaning [2,19], ridesharing recommendation [16,17], friend recommendation [17], frequent trajectory based routing [13,19], and traffic congestion prediction. For example, a database may contain several copies of a trajectory or several similar trajectories.…”

Section: Introductionmentioning

confidence: 99%

Trajectory similarity join in spatial networks

Chen²,

et al. 2017

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The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ, the TS-Join returns all pairs of trajectories from the two sets with similarity above θ. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction.With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divideand-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.

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“…For example, taxi companies monitor the mobility information of taxis; telecom operators continuously record the locations of active mobile phones; location-based service (LBS) providers keep the mobile information of the users whenever they use the services. Such large amount of location-based mobile data is valuable for many research fields such as human behavior analysis [1], urban transportation planning [2], customized routing recommendation [3,4], and locationbased advertising and marketing [5].…”

Section: Introductionmentioning

confidence: 99%

Diverse Mobile System for Location‐Based Mobile Data

Liao

Tan

Luo

et al. 2018

Wireless Communications and Mobile Computing

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The value of large amount of location-based mobile data has received wide attention in many research fields including human behavior analysis, urban transportation planning, and various location-based services. Nowadays, both scientific and industrial communities are encouraged to collect as much location-based mobile data as possible, which brings two challenges: (1) how to efficiently process the queries of big location-based mobile data and (2) how to reduce the cost of storage services, because it is too expensive to store several exact data replicas for fault-tolerance. So far, several dedicated storage systems have been proposed to address these issues. However, they do not work well when the ranges of queries vary widely. In this work, we design a storage system based on diverse replica scheme which not only can improve the query processing efficiency but also can reduce the cost of storage space. To the best of our knowledge, this is the first work to investigate the data storage and processing in the context of big location-based mobile data. Specifically, we conduct in-depth theoretical and empirical analysis of the trade-offs between different spatial-temporal partitioning and data encoding schemes. Moreover, we propose an effective approach to select an appropriate set of diverse replicas, which is optimized for the expected query loads while conforming to the given storage space budget. The experiment results show that using diverse replicas can significantly improve the overall query performance and the proposed algorithms for the replica selection problem are both effective and efficient.

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Finding time period-based most frequent path in big trajectory data

Cited by 196 publications

References 29 publications

Efficient mining of regional movement patterns in semantic trajectories

Efficient mining of regional movement patterns in semantic trajectories

Trajectory similarity join in spatial networks

Diverse Mobile System for Location‐Based Mobile Data

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