Abstract
In recent years positioning sensors have become ubiquitous, and there has been tremendous growth in the amount of trajectory data. It is a huge challenge to efficiently store and query massive trajectory data. Among the typical operation over trajectories, similarity query is an important yet complicated operator. It is useful in navigation systems, transportation optimizations, and so on. However, most existing studies have focused on handling the problem on a centralized system, while with a single machine it is difficult to satisfy the storage and processing requirements of mass data. A distributed framework for the similarity query of massive trajectory data is urgently needed. In this research, we propose DFTHR (distributed framework based on HBase and Redis) to support the similarity query using Hausdorff distance. DFTHR utilizes a segment-based data model with a number of optimizations for storing, indexing and pruning to ensure efficient querying capability. Furthermore, it adopts a bulk-based method to alleviate the cost for adjusting partitions, so that the incremental dataset can be efficiently supported. Additionally, DFTHR introduces a co-location-based distributed strategy and a node-locality-based parallel query algorithm to reduce the inter-worker cost overhead. Experiments show that DFTHR significantly outperforms other schemes.
Highlights
With the rapid development of mobile networks and position technologies, trajectory data of moving object (MO) can be collected more and accurately
In light of that, using Hausdorff distance [13] as the distance function, we propose DFTHR, a distributed framework for trajectory similarity query based on HBase [14] and Redis [15]
It is expensive to compute the similarity between two trajectories directly, so according to the data model of the trajectory segment, we propose a lower bound based pruning method and a MBR
Summary
With the rapid development of mobile networks and position technologies, trajectory data of moving object (MO) can be collected more and accurately. In order to process the similarity query problem of large-scale trajectory data, some distributed framework based schemes [9,10,11,12] are proposed according to the selected distance functions. We proposes a bulk-based partitioning model to deploy trajectory data and indexes in a distributed environment, and on this basis implement the mechanism of cost optimization to efficiently process the state change of each partitions so that our scheme can effectively support the incremental datasets. We devise certain maintenance strategies to ensure co-location between each partition and its corresponding index, and implement node-locality-based parallel query algorithms to reduce the data transmission overhead when querying. We introduce a bulk-based partitioning model and certain optimization strategies to alleviate the cost of adjusting data distribution, so that an incremental dataset can be supported efficiently.
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