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Liangli Ma
College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

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Journal article
Published: 18 August 2019 in Future Internet
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Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

ACS Style

Fei Liao; Liangli Ma; Jingjing Pei; Linshan Tan. Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military. Future Internet 2019, 11, 180 .

AMA Style

Fei Liao, Liangli Ma, Jingjing Pei, Linshan Tan. Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military. Future Internet. 2019; 11 (8):180.

Chicago/Turabian Style

Fei Liao; Liangli Ma; Jingjing Pei; Linshan Tan. 2019. "Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military." Future Internet 11, no. 8: 180.

Journal article
Published: 19 May 2019 in Algorithms
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With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of the continuous query of moving objects, namely the pruning optimization over historical continuous query based on threshold. Firstly, for the problem that the processing cost of the Mindist-based pruning strategy is too large, a pruning strategy based on extended Minimum Bounding Rectangle overlap is proposed to optimize the processing overhead. Secondly, a best-first traversal algorithm based on E3DR-tree is proposed to ensure that an accurate pruning candidate set can be obtained with accessing as few index nodes as possible. Finally, experiments on real data sets prove that our method significantly outperforms other similar methods.

ACS Style

Jiwei Qin; Liangli Ma; Qing Liu; Qin; Ma; Liu. Pruning Optimization over Threshold-Based Historical Continuous Query. Algorithms 2019, 12, 107 .

AMA Style

Jiwei Qin, Liangli Ma, Qing Liu, Qin, Ma, Liu. Pruning Optimization over Threshold-Based Historical Continuous Query. Algorithms. 2019; 12 (5):107.

Chicago/Turabian Style

Jiwei Qin; Liangli Ma; Qing Liu; Qin; Ma; Liu. 2019. "Pruning Optimization over Threshold-Based Historical Continuous Query." Algorithms 12, no. 5: 107.

Journal article
Published: 24 February 2019 in Information
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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.

ACS Style

Jiwei Qin; Liangli Ma; Qing Liu. DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis. Information 2019, 10, 77 .

AMA Style

Jiwei Qin, Liangli Ma, Qing Liu. DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis. Information. 2019; 10 (2):77.

Chicago/Turabian Style

Jiwei Qin; Liangli Ma; Qing Liu. 2019. "DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis." Information 10, no. 2: 77.

Journal article
Published: 03 January 2019 in Future Internet
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The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the colocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes.

ACS Style

Jiwei Qin; Liangli Ma; Jinghua Niu. THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management. Future Internet 2019, 11, 10 .

AMA Style

Jiwei Qin, Liangli Ma, Jinghua Niu. THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management. Future Internet. 2019; 11 (1):10.

Chicago/Turabian Style

Jiwei Qin; Liangli Ma; Jinghua Niu. 2019. "THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management." Future Internet 11, no. 1: 10.