{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T16:06:36Z","timestamp":1762272396749,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,24]],"date-time":"2019-02-24T00:00:00Z","timestamp":1550966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802425"],"award-info":[{"award-number":["61802425"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info10020077","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T03:06:52Z","timestamp":1551064012000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis"],"prefix":"10.3390","volume":"10","author":[{"given":"Jiwei","family":"Qin","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Liangli","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Qing","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10707-016-0256-z","article-title":"Design principles of a stream-based framework for mobility analysis","volume":"21","author":"Salmon","year":"2017","journal-title":"GeoInformatica"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, Z., and Ma, X. (2018). Distributed Indexing Method for Timeline Similarity Query. Algorithms, 11.","DOI":"10.3390\/a11040041"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Morse, M.D., and Patel, J.M. (2007, January 11\u201314). An efficient and accurate method for evaluating time series similarity. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Beijing, China.","DOI":"10.1145\/1247480.1247544"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.cad.2011.02.008","article-title":"Polyline approach for approximating Hausdorff distance between planar free-form curves","volume":"43","author":"Bai","year":"2011","journal-title":"Comput. Aided Des."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, M., and Wong, M.H. (2007, January 15\u201320). Boundary-Based Lower-Bound Functions for Dynamic Time Warping and Their Indexing. Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.368999"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhu, Y., and Shasha, D. (2003, January 9\u201312). Warping indexes with envelope transforms for query by humming. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, CA, USA.","DOI":"10.1145\/872757.872780"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ranu, S., Deepak, P., Telang, A.D., Deshpande, P., and Raghavan, S. (2015, January 13\u201317). Indexing and matching trajectories under inconsistent sampling rates. Proceedings of the 2015 IEEE 31st International Conference on Data Engineering (ICDE), Seoul, Korea.","DOI":"10.1109\/ICDE.2015.7113351"},{"key":"ref_8","first-page":"455","article-title":"SharkDB: An in-memory column oriented storage for trajectory analysis","volume":"21","author":"Zheng","year":"2018","journal-title":"WWW"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Peixoto, D.A., and Hung, N.Q.V. (2016, January 28\u201330). Scalable and Fast Top-k Most Similar Trajectories Search Using MapReduce In-Memory. Proceedings of the Australasian Database Conference, Sydney, Australia.","DOI":"10.1007\/978-3-319-46922-5_18"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jin, C., Mao, J., Yang, X., and Zhou, A. (2017, January 7\u20139). TrajSpark: A Scalable and Efficient In-Memory Management System for Big Trajectory Data. Proceedings of the Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, Beijing, China.","DOI":"10.1007\/978-3-319-63579-8_2"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.14778\/3137628.3137655","article-title":"Distributed trajectory similarity search","volume":"10","author":"Xie","year":"2017","journal-title":"Proc. VLDB Endow."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shang, Z., Li, G., and Bao, Z. (2018, January 10\u201315). DITA: A Distributed In-Memory Trajectory Analytics System. Proceedings of the 2018 International Conference on Management of Data, Houston, TX, USA.","DOI":"10.1145\/3183713.3193553"},{"key":"ref_13","first-page":"307","article-title":"Computing the hausdorff distance between curved objects","volume":"18","author":"Alt","year":"2008","journal-title":"JCG Appl."},{"key":"ref_14","unstructured":"(2019, January 08). Apache HBase. Available online: https:\/\/hbase.apache.org\/."},{"key":"ref_15","unstructured":"(2019, January 08). Redis. Available online: https:\/\/redis.io\/."},{"key":"ref_16","first-page":"75","article-title":"Computing the Fr\u00e9chet distance between two polygonal curves","volume":"5","author":"Alt","year":"1995","journal-title":"IJCGA"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M. (2007). Information Retrieval for Music and Motion, Springer.","DOI":"10.1007\/978-3-540-74048-3"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, K., and Shahabi, C. (2004, January 13). A PCA-based similarity measure for multivariate time series. Proceedings of the 2nd ACM International Workshop on Multimedia Databases, Washington, DC, USA.","DOI":"10.1145\/1032604.1032616"},{"key":"ref_19","unstructured":"Vlachos, M., Kollios, G., and Gunopulos, D. (March, January 26). Discovering similar multidimensional trajectories. In Data Engineering, 2002. Proceedings of the 18th International Conference, San Jose, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, L., \u00d6zsu, M.T., and Oria, V. (2005, January 14\u201316). Robust and fast similarity search for moving object trajectories. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, New York, NY, USA.","DOI":"10.1145\/1066157.1066213"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xie, D., Li, F., Yao, B., Zhou, L., and Guo, M. (July, January 26). Simba: Efficient in-memory spatial analytics. Proceedings of the 2016 International Conference on Management of Data, San Francisco, CA, USA.","DOI":"10.1145\/2882903.2915237"},{"key":"ref_22","unstructured":"Vora, M.N. (2011, January 24\u201326). Hadoop-HBase for large-scale data. Proceedings of the 2011 International Conference on Computer Science and Network Technology (ICCSNT), Harbin, China."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vashishtha, H., and Stroulia, E. (2011, January 26\u201328). Enhancing query support in hbase via an extended coprocessors framework. Proceedings of the European Conference on a Service-Based Internet, Poznan, Poland.","DOI":"10.1007\/978-3-642-24755-2_7"},{"key":"ref_24","unstructured":"Han, J., Haihong, E., Le, G., and Du, J. (2011, January 26\u201328). Survey on NoSQL database. Proceedings of the 2011 6th International Conference, Pervasive Computing and Applications (ICPCA), Port Elizabeth, South Africa."},{"key":"ref_25","unstructured":"Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., and Stoica, I. (2010, January 22\u201325). Spark: Cluster computing with working sets. Proceedings of the Usenix Conference on Hot Topics in Cloud Computing, Boston, MA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1145\/356924.356930","article-title":"The quadtree and related hierarchical data structures","volume":"16","author":"Samet","year":"1984","journal-title":"CSUR"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/373626.373678","article-title":"Querying multi-dimensional data indexed using the Hilbert space-filling curve","volume":"30","author":"Lawder","year":"2001","journal-title":"SIGMOD"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"506","DOI":"10.14778\/2002974.2002978","article-title":"An Incremental Hausdorff Distance Calculation Algorithm","volume":"4","author":"Nutanong","year":"2011","journal-title":"Proc. VLDB Endow."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1017\/S0373463307004298","article-title":"Automatic Identification System (AIS): Data reliability and human error implications","volume":"60","author":"Wall","year":"2007","journal-title":"J. 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