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High-performance spatiotemporal trajectory matching across heterogeneous data sources
کارایی بالا تطبیق مسیر مکانی و مکانی در منابع داده ناهمگن - سایپرز ، باشگاه دانش-2020 In the era of big data, the movement of the same object or person can be recorded by different
devices with different measurement accuracies and sampling rates. Matching and conflating these
heterogeneous trajectories help to enhance trajectory semantics, describe user portraits, and discover
specified groups from human mobility. In this paper, we proposed a high-performance approach
for matching spatiotemporal trajectories across heterogeneous massive datasets. Two indicators, i.e.,
Time Weighted Similarity (TWS) and Space Weighted Similarity (SWS), are proposed to measure
the similarity of spatiotemporal trajectories. The core idea is that trajectories are more similar if
they stay close in a longer time and distance. A distributed computing framework based on Spark
is built for efficient trajectory matching among massive datasets. In the framework, the trajectory
segments are partitioned into 3-dimensional space–time cells for parallel processing, and a novel
method of segment reference point is designed to avoid duplicated computation. We conducted
extensive matching experiments on real-world and synthetic trajectory datasets. The experimental
results illustrate that the proposed approach outperforms other similarity metrics in accuracy, and the
Spark-based framework greatly improves the efficiency in spatiotemporal trajectory matching. Keywords: Distributed computing | Spatiotemporal big data | Trajectory similarity | Trajectory matching |
مقاله انگلیسی |
2 |
A generic trajectory similarity operator in moving object databases
یک اپراتور شباهت مسیری عمومی در پایگاه داده های حرکتی شی-2017 Evaluating similarity between trajectories of moving objects is important for wide range
of applications. The existing similarity measures typically define some meaning of similarity and
propose algorithms for computing it. We think that the meaning of similarity is application dependant, and should only be determined by the user. Therefore, there is a need for a generic approach
where users can define the meaning of similarity. In this paper, we propose a parametrized similarity
operator, based on the time warped edit distance, where the meaning of similarity is generic and left
for user to define. Our proposed operator is implemented in SECONDO and evaluated using both synthetic and real datasets. The results were promising and as expected.
KEYWORDS : Trajectory similarity | Moving objects databases | SECONDO | TWED |
مقاله انگلیسی |
3 |
Towards an Efficient Top-K Trajectory Similarity Query Processing Algorithm for Big Trajectory Data on GPGPUs
به سوی یک الگوریتم پردازش پرس و جوی مسیریابی کارا برای داده های بزرگ بر روی GPGPU مشابه-2016 Through the use of location-sensing devices, it
has been possible to collect very large datasets of trajectories. These datasets make it possible to issue spatio-temporal
queries with which users can gather information about the
characteristics of the movements of objects, derive patterns
from that information, and understand the objects themselves.
Among such spatio-temporal queries that can be issued is
the top-K trajectory similarity query. This query finds many
applications, such as bird migration analysis in ecology and
trajectory sharing in social networks. However, the large size
of the trajectory query sets and databases poses significant
computational challenges. In this work, we propose a parallel
GPGPU algorithm Top-KaBT that is specifically designed to
reduce the size of the candidate set generated while processing
these queries, and in doing so strives to address these computational challenges. The experiments show that the state of
the art top-K trajectory similarity query processing algorithm
on GPGPUs, TKSimGPU, achieves a 6.44X speedup in query
processing time when combined with our algorithm and a 13X
speedup over a GPGPU algorithm that uses exhaustive search.
Keywords: Trajectory | Trajectory similarity | GPGPU | High performance |
مقاله انگلیسی |