Semantic trajectory extraction framework for indoor space
LUO Xinyuan1, CHEN Xin1, SHOU Lidan1,2, CHEN Ke1,3, WU Yanjing4
1. Department of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; 2. CAD & CG State Key Lab, Zhejiang University, Hangzhou 310027, China; 3. Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, Hangzhou 310027, China; 4. Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
Abstract：Using massive location data to analyze user behavior and tap the potential value of customers is getting much attention. Mature solutions have been studied for outdoor space, while the accuracy and robustness of WiFi positioning data are main challenges for indoor space. The method of extracting the semantic trajectory for indoor space is studied. It can reduce error, compress original position data and enhance the expression capability of trajectory for indoor spatio-temporal data mining. Based on four modules including indoor space modeling, data cleansing, event extraction and semantic enhancement, a framework for calculating indoor semantic trajectory is proposed. Experiments are conducted on real and simulated datasets, and the results show that this method can accurately and effectively extract trajectory data containing semantic information from indoor positioning data with incompleteness and errors, which can be used for high-level analytical applications.
 ORELLANA D, WACHOWICZ M, ANDRIENKO N, et al. Uncovering interaction patterns in mobile outdoor gaming[C]//2009 International Conference on Advanced Geographic Information Systems & Web Services. Cancun, Mexico:IEEE, 2009:177-182.  WU F, FU K, WANG Y, et al. A spatial-temporal-semantic neural network algorithm for location prediction on moving objects[J]. Algorithms, 2017, 10(2):37-37.  MARKETOS G, FRENTZOS E, NTOUTSI I, et al. Building real-world trajectory warehouses[C]//Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access. New York, USA:ACM, 2008:8-15.  ZHENG Y. Trajectory data mining:An overview[J]. ACM Transactions on Intelligent Systems and Technology, 2015, 6(3):29-29.  LEE S H, LIM I K, LEE J K. Method for improving indoor positioning accuracy using extended Kalman filter[J]. Mobile Information Systems, 2016, 2369103.  WALTON L, WORBOYS M. An algebraic approach to image schemas for geographic space[C]//Proceedings of the 9th International Conference on Spatial Information Theory. Berlin, Germany:Springer-Verlag, 2009:357-370.  XIE X K, LU H, PEDERSEN T B. Efficient distance-aware query evaluation on indoor moving objects[C]//2013 IEEE 29th International Conference on Data Engineering. Brisbane, QLD, Australia:IEEE, 2013:434-445.  ALVARES L O, BOGORNY V, KUIJPERS B, et al. A model for enriching trajectories with semantic geographical information[C]//Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems. New York, USA:ACM, 2007:22-30.  LI Q N, ZHENG Y, XIE X, et al. Mining user similarity based on location history[C]//Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York,USA:ACM, 2008:34-43.  GONG L, SATO H, YAMAMOTO T, et al. Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines[J]. Journal of Modern Transportation, 2015, 23(3):202-213.  ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Palo Alto, USA:AAAI Press, 1996:226-231.  BIRANT D, KUT A. ST-DBSCAN:An algorithm for clustering spatial-temporal data[J]. Data & Knowledge Engineering, 2007, 60(1):208-221.  LUO T, ZHENG X W, XU G L, et al. An improved DBSCAN algorithm to detect stops in individual trajectories[J]. ISPRS International Journal of Geo-Information, 2017, 6(3):63-78.  YAN Z X, CHAKRABORTY D, PARENT C, et al. Semantic trajectories:Mobility data computation and annotation[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2013, 4(3):1-38.  JENSEN C S, LU H, YANG B. Graph model based indoor tracking[C]//2009 Tenth International Conference on Mobile Data Management:Systems, Services and Middleware. Taipei, China:IEEE, 2009:122-131.  JUNG Y, JOO M. Building information modelling (BIM) framework for practical implementation[J]. Automation in Construction, 2011, 20(2):126-133.  YANG B, LU H, JENSEN C S. Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space[C]//Proceedings of the 13th International Conference on Extending Database Technology. New York, USA:ACM, 2010:335-346.  SANDER J, ESTER M, KRIEGEL H P, et al. Density-based clustering in spatial databases:The algorithm gdbscan and its applications[J]. Data Mining and Knowledge Discovery, 1998, 2(2):169-194.  LI H, LU H, CHEN X, et al. Vita:A versatile toolkit for generating indoor mobility data for real-world buildings[J]. Proceedings of the VLDB Endowment, 2016, 9(13):1453-1456.  CHEN L, ÖZSU M T, ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. New York, USA:ACM, 2005:491-502.