微信服务号

微信订阅号

2025-4-15- 3
Home > Archive>Volume 29, Issue 7, 2018 >2018-2045. DOI:10.13328/j.cnki.jos.005576
PDF HTML XML Export Cite reminder
Semantic Understanding of Spatio-Temporal Data: Technology & Application
Author:
Affiliation:

Fund Project:

National Natural Science Foundation of China (61472403, 61303243, 61702470)

  • Article
    • | |
  • Metrics
    • |
  • Reference [168]
    • |
  • Related [20]
    • | | |
  • Comments
    Abstract:

    With the development of mobile internet and widespread use of mobile phones, a large amount of data that contains user' time and space attributes has been generated and collected. Investigating the semantic information of the collective data plays an important role in understanding the needs, analyzing preference of the user, even recommending and predicting space and time. Recently, many researchers all over the world have turned their focus on understanding the spatio-temporal semantic data. This paper summarizes the related works regarding the spatio-temporal semantic data. Firstly, according to the tasks, the basic concepts and research frameworks are introduced; then, the works of location semantic understanding, user behavior semantic understanding and event semantic understanding are summarized. Additionally, the application scenarios of recommending and predicting space and time field are described. Finally, the future research directions of spatio-temporal data semantic understanding are discussed.

    Reference
    [1] Eldawy A, Mokbel MF. The era of big spatial data. In:Proc. of the 32nd IEEE Int'l Conf. on Data Engineering (ICDE). IEEE, 2016. 1424-1427.
    [2] https://baike.baidu.com/item/%E8%AF%AD%E4%B9%89/9716033?fr=aladdin
    [3] Liu DY, Chen HL, Qi H, Yang B. Advances in spatiotemporal data mining. Journal of Computer Research and Development, 2013,50(2):225-239(in Chinese with English abstract).
    [4] Ji GL, Zhao B. A survey of spatiotemporal data mining for big data. Journal of Nanjing Normal University (Natural Science Edition), 2014,37(1):1-7(in Chinese with English abstract).
    [5] Zheng Y. Introduction to urban computing. Geomatics and Information Science of Wuhan University, 2015,40(1):1-13(in Chinese with English abstract).
    [6] Zheng Y. Trajectory data mining:An overview. ACM Trans. on Intelligent Systems and Technology (TIST), 2015,6(3):29.
    [7] Gao Q, Zhang FL, Wang RJ, Zhou F. Trajectory big data:A review of key technologies in data processing. Ruan Jian Xue Bao/Journal of Software, 2017,28(4):959-992(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5143.htm[doi:10. 13328/j.cnki.jos.005143]
    [8] Feng Z, Zhu Y. A survey on trajectory data mining:Techniques and applications. IEEE Access, 2016,4:2056-2067.
    [9] Sabarish BA, Karthi R, Gireeshkumar T. A survey of location prediction using trajectory mining. In:Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Springer India, 2015. 119-127.
    [10] Besse P, Guillouet B, Loubes JM, et al. Review and perspective for distance based trajectory clustering. arXiv Preprint arXiv:1508.04904, 2015.
    [11] Castro PS, Zhang D, Chen C, et al. From taxi GPS traces to social and community dynamics:A survey. ACM Computing Surveys (CSUR), 2013,46(2):17.
    [12] Parent C, Spaccapietra S, Renso C, et al. Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR), 2013,45(4):42.
    [13] Zheng Y, Xie X, Ma WY. GeoLife:A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin, 2010,33(2):32-39.
    [14] Yuan J, Zheng Y, Zhang C, et al. T-Drive:Driving directions based on taxi trajectories. In:Proc. of the 18th SIGSPATIAL Int'l Conf. on Advances in Geographic Information Systems. ACM, 2010. 99-108.
    [15] Kwak H, Lee C, Park H, et al. What is Twitter, a social network or a news media. In:Proc. of the 19th Int'l Conf. on World Wide Web. ACM, 2010. 591-600.
    [16] http://www.mobile.yahoo.com/flickr
    [17] https://nationalzoo.si.edu/scbi/migratorybirds/research/data/
    [18] https://catalog.data.gov/dataset/zebra-and-quagga-mussel-distribution-in-north-america-direct-download
    [19] http://www.marinetraffic.com/
    [20] https://catalog.data.gov/dataset/joint-typhoon-warning-center-storm-wallets
    [21] http://datacenter.mep.gov.cn/
    [22] Tsoukatos Ⅱ, Gunopulos D. Efficient mining of spatiotemporal patterns. In:Proc. of the Int'l Symp. on Spatial and Temporal Databases. Berlin, Heidelberg:Springer-Verlag, 2001. 425-442.
    [23] Tong YX, Yuan Y, Cheng YR, Chen L, Wang GR. A survey of spatiotemporal crowdsourced data management techniques. Ruan Jian Xue Bao/Journal of Software, 2017,28(1):35-58(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5140. htm[doi:10.13328/j.cnki.jos.005140]
    [24] Zhao Y, Han Q. Spatial crowdsourcing:Current state and future directions. IEEE Communications Magazine, 2016,54(7):102-107.
    [25] Haklay M, Weber P. Openstreetmap:User-Generated street maps. IEEE Pervasive Computing, 2008,7(4):12-18.
    [26] https://www.mapbox.com/osm-data-report/
    [27] Ding Y, Zheng J, Tan H, et al. Inferring road type in crowdsourced map services. In:Proc. of the Int'l Conf. on Database Systems for Advanced Applications. Springer Int'l Publishing, 2014. 392-406.
    [28] Hu H, Zheng Y, Bao Z, et al. Crowdsourced poi labelling:Location-Aware result inference and task assignment. In:Proc. of the ICDE. 2016.
    [29] Schnitzler F, Artikis A, Weidlich M, et al. Heterogeneous stream processing and crowdsourcing for traffic monitoring:Highlights. In:Proc. of the Joint European Conf. on Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg:Springer-Verlag, 2014. 520-523.
    [30] Artikis A, Weidlich M, Schnitzler F, et al. Heterogeneous stream processing and crowdsourcing for urban traffic management. In:Proc. of the EDBT. 2014. 712-723.
    [31] Hu H, Li G, Bao Z, et al. Crowdsourcing-Based real-time urban traffic speed estimation:From trends to speeds. In:Proc. of the 32nd IEEE Int'l Conf. on Data Engineering (ICDE). IEEE, 2016. 883-894.
    [32] Yan Z, Chakraborty D, Parent C, et al. SeMiTri:A framework for semantic annotation of heterogeneous trajectories. In:Proc. of the 14th Int'l Conf on Extending Database Technology. ACM, 2011. 259-270.
    [33] Yan Z, Chakraborty D, Parent C, et al. Semantic trajectories:Mobility data computation and annotation. ACM Trans. on Intelligent Systems and Technology (TIST), 2013,4(3):49.
    [34] Spaccapietra S, Parent C, Damiani ML, et al. A conceptual view on trajectories. Data & Knowledge Engineering, 2008,65(1):126-146.
    [35] Alvares LO, Bogorny V, Kuijpers B, et al. A model for enriching trajectories with semantic geographical information. In:Proc. of the 15th Annual ACM Int'l Symp. on Advances in Geographic Information Systems. ACM, 2007. 22.
    [36] Guc B, May M, Saygin Y, et al. Semantic annotation of gps trajectories. In:Proc. of the 11th AGILE Int'l Conf. on Geographic Information Science. 2008,38(6):1-9.
    [37] Moreno B, Times VC, Renso C, et al. Looking inside the stops of trajectories of moving objects. In:Proc. of the Geoinfo. 2010. 9-20.
    [38] Mousavi SM, Harwood A, Karunasekera S, et al. Geometry of interest (GOI):Spatio-Temporal destination extraction and partitioning in gps trajectory data. arXiv Preprint arXiv:1603.04110, 2016.
    [39] Su H, Zheng K, Zeng K, et al. Making sense of trajectory data:A partition-and-summarization approach. In:Proc. of the 31st IEEE Int'l Conf. on Data Engineering. IEEE, 2015. 963-974.
    [40] Lv M, Chen L, Xu Z, et al. The discovery of personally semantic places based on trajectory data mining. Neurocomputing, 2016, 173:1142-1153.
    [41] Nabo RGB, Fileto R, Nanni M, et al. Annotating trajectories by fusing them with social media users posts. In:Proc. of the GeoInfo. 2014. 25-36.
    [42] Zheng B, Yuan N J, Zheng K, et al. Approximate keyword search in semantic trajectory database. In:Proc. of the 31st IEEE Int'l Conf. on Data Engineering. IEEE, 2015. 975-986.
    [43] Leme LAPP, Renso C, Nunes BP, et al. Searching for data sources for the semantic enrichment of trajectories. In:Proc. of the Int'l Conf. on Web Information Systems Engineering. Springer Int'l Publishing, 2016. 238-246.
    [44] Kafsi M, Grossglauser M, Thiran P. Traveling salesman in reverse:Conditional Markov entropy for trajectory segmentation. In:Proc. of the 2015 IEEE Int'l Conf. on Data Mining (ICDM). IEEE, 2015. 201-210.
    [45] Zhao K, Cong G, Sun A. Annotating points of interest with geo-tagged Tweets. In:Proc. of the 25th ACM Int'l on Conf. on Information and Knowledge Management. ACM, 2016. 417-426.
    [46] Wu F, Li Z, Lee WC, et al. Semantic annotation of mobility data using social media. In:Proc. of the 24th Int'l Conf. on World Wide Web. ACM, 2015. 1253-1263.
    [47] Wu F, Wang H, Li Z, et al. SemMobi:A semantic annotation system for mobility data. In:Proc. of the 24th Int'l Conf. on World Wide Web. ACM, 2015. 255-258.
    [48] Hegde V, Parreira JX, Hauswirth M. Semantic tagging of places based on user interest profiles from online social networks. In:Proc. of the European Conf. on Information Retrieval. Berlin, Heidelberg:Springer-Verlag, 2013. 218-229.
    [49] Lian D, Xie X. Learning location naming from user check-in histories. In:Proc. of the 19th ACM SIGSPATIAL Int'l Conf on Advances in Geographic Information Systems. ACM, 2011. 112-121.
    [50] Ye M, Shou D, Lee WC, et al. On the semantic annotation of places in location-based social networks. In:Proc. of the 17th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2011. 520-528.
    [51] Sarda S, Eickhoff C, Hofmann T. Semantic place descriptors for classification and map discovery. arXiv Preprint arXiv:1601.05952, 2016.
    [52] Bhattacharya T, Kulik L, Bailey J. Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections. Pervasive and Mobile Computing, 2015,19:86-107.
    [53] Vu DD, Shin WY. Low-Cmplexity detection of POI boundaries using geo-tagged tweets:A geographic proximity based approach. In:Proc.of the 8th ACM SIGSPATIAL Int'l Workshop on Location-Based Social Networks. ACM, 2015.
    [54] Yuan J, Zheng Y, Xie X. Discovering regions of different functions in a city using human mobility and POIs. In:Proc. of the 18th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2012. 186-194.
    [55] Noulas A, Scellato S, Mascolo C, et al. Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. The Social Mobile Web, 2011,11:2.
    [56] Lichman M, Smyth P. Modeling human location data with mixtures of kernel densities. In:Proc. of the 20th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2014. 35-44.
    [57] Lloyd A, Cheshire J. Deriving retail centre locations and catchments from geo-tagged Twitter data. Computers, Environment and Urban Systems, 2017,61:108-118.
    [58] Zhang C, Zhang K, Yuan Q, et al. Regions, periods, activities:Uncovering urban dynamics via cross-modal representation learning. In:Proc. of the 26th Int'l Conf. on World Wide Web. Int'l World Wide Web Conferences Steering Committee, 2017. 361-370.
    [59] Imamichi T, Osogami T, Raymond R. Truncating shortest path search for efficient map-matching. In:Proc. of the 25th Int'l Joint Conf. on Artificial Intelligence (IJCAI 2016). 2016. 589-595
    [60] Li Y, Su H, Demiryurek U, et al. PerNav:A route summarization framework for personalized navigation. In:Proc. of the 2016 ACM SIGMOD Int'l Conf. on Management of Data. 2016. 2125-2128.
    [61] Dai J, Yang B, Guo C, et al. Path cost distribution estimation using trajectory data. Proc. of the VLDB Endowment, 2016,10(3):85-96.
    [62] Hung CC, Peng WC, Lee WC. Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. The VLDB Journal, 2015,24(2):169-192.
    [63] Evans MR, Oliver D, Shekhar S, et al. Summarizing trajectories into k-primary corridors:A summary of results. In:Proc. of the 20th Int'l Conf. on Advances in Geographic Information Systems. ACM, 2012. 454-457.
    [64] Blei DM, Lafferty JD. Dynamic topic models. In:Proc. of the 23rd Int'l Conf. on Machine Learning. ACM, 2006. 113-120.
    [65] Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003,3:993-1022.
    [66] Wang X, McCallum A. Topics over time:A non-Markov continuous-time model of topical trends. In:Proc. of the 12th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2006. 424-433.
    [67] Wang C, Wang J, Xie X, et al. Mining geographic knowledge using location aware topic model. In:Proc. of the 4th ACM Workshop on Geographical Information Retrieval. ACM, 2007. 65-70.
    [68] Yin Z, Cao L, Han J, et al. Geographical topic discovery and comparison. In:Proc. of the 20th Int'l Conf. on World Wide Web. ACM, 2011. 247-256.
    [69] Hong L, Ahmed A, Gurumurthy S, et al. Discovering geographical topics in the twitter stream. In:Proc. of the 21st Int'l Conf. on World Wide Web. ACM, 2012. 769-778.
    [70] Eisenstein J, O'Connor B, Smith NA, et al. A latent variable model for geographic lexical variation. In:Proc. of the 2010 Conf. on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2010. 1277-1287.
    [71] Hu B, Ester M. Spatial topic modeling in online social media for location recommendation. In:Proc. of the 7th ACM Conf. on Recommender Systems. ACM, 2013. 25-32.
    [72] Ahmed A, Hong L, Smola AJ. Hierarchical geographical modeling of user locations from social media posts. In:Proc. of the 22nd Int'l Conf. on World Wide Web. ACM, 2013. 25-36.
    [73] Liu Y, Ester M, Hu B, et al. Spatio-Temporal topic models for check-in data. In:Proc. of the 2015 IEEE Int'l Conf. on Data Mining (ICDM). IEEE, 2015. 889-894.
    [74] Mei Q, Liu C, Su H, et al. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In:Proc. of the 15th Int'l Conf. on World Wide Web. ACM, 2006. 533-542.
    [75] Yuan Q, Cong G, Ma Z, et al. Who, where, when and what:Discover spatio-temporal topics for twitter users. In:Proc. of the 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2013. 605-613.
    [76] Hu B, Jamali M, Ester M. Spatio-Temporal topic modeling in mobile social media for location recommendation. In:Proc. of the 13th IEEE Int'l Conf. on Data Mining. IEEE, 2013. 1073-1078.
    [77] Agrawal R, Srikant R. Mining sequential patterns. In:Proc. of the 11th Int'l Conf. on Data Engineering. IEEE, 1995. 3-14.
    [78] Tsoukatos Ⅱ, Gunopulos D. Efficient mining of spatiotemporal patterns. In:Proc. of the Int'l Symp. on Spatial and Temporal Databases. Berlin, Heidelberg:Springer-Verlag, 2001. 425-442.
    [79] Wang J, Hsu W, Lee ML. Flowminer:Finding flow patterns in spatio-temporal databases. In:Proc. of the 16th IEEE Int'l Conf. on Tools with Artificial Intelligence. IEEE, 2004. 14-21.
    [80] Cao H, Mamoulis N, Cheung DW. Mining frequent spatio-temporal sequential patterns. In:Proc. of the 5th IEEE Int'l Conf. on Data Mining (ICDM 2005). IEEE, 2005. 8.
    [81] Alvares LO, Bogorny V, Kuijpers B, et al. Towards semantic trajectory knowledge discovery. Data Mining and Knowledge Discovery, 2007.
    [82] Ying JJC, Lee WC, Weng TC, et al. Semantic trajectory mining for location prediction. In:Proc. of the 19th ACM SIGSPATIAL Int'l Conf. on Advances in Geographic Information Systems. ACM, 2011. 34-43.
    [83] Chen CC, Kuo CH, Peng WC. Mining spatial-temporal semantic trajectory patterns from raw trajectories. In:Proc. of the 2015 IEEE Int'l Conf. on Data Mining Workshop (ICDMW). IEEE, 2015. 1019-1024.
    [84] Zhang C, Han J, Shou L, et al. Splitter:Mining fine-grained sequential patterns in semantic trajectories. Proc. of the VLDB Endowment, 2014,7(9):769-780.
    [85] Zhang C, Zheng Y, Ma X, et al. Assembler:Efficient discovery of spatial co-evolving patterns in massive geo-sensory data. In:Proc. of the 21st ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2015. 1415-1424.
    [86] Li Z, Ding B, Han J, et al. Mining periodic behaviors for moving objects. In:Proc. of the 16th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2010. 1099-1108.
    [87] Li Z, Wang J, Han J. ePeriodicity:Mining event periodicity from incomplete observations. IEEE Trans. on Knowledge and Data Engineering, 2015,27(5):1219-1232.
    [88] Jindal T, Giridhar P, Tang LA, et al. Spatiotemporal periodical pattern mining in traffic data. In:Proc. of the 2nd ACM SIGKDD Int'l Workshop on Urban Computing. ACM, 2013. 11.
    [89] Zarezade A, Jafarzadeh S, Rabiee HR. Spatio-Temporal modeling of check-ins in location-based social networks. arXiv Preprint arXiv:1611.07710, 2016.
    [90] Chen L, Özsu MT, Oria V. Robust and fast similarity search for moving object trajectories. In:Proc. of the 2005 ACM SIGMOD Int'l Conf. on Management of Data. ACM, 2005. 491-502.
    [91] Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In:Proc. of the KDD Workshop. 1994,10(16):359-370.
    [92] Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories. In:Proc. of the 18th Int'l Conf. on Data Engineering. IEEE, 2002. 673-684.
    [93] Chen J, Wang R, Liu L, et al. Clustering of trajectories based on Hausdorff distance. In:Proc. of the 2011 Int'l Conf. on Electronics, Communications and Control (ICECC). IEEE, 2011. 1940-1944.
    [94] Yuan G, Sun P, Zhao J, et al. A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 2016, 1-22.
    [95] da Silva TLC, Zeitouni K, de Macêdo JAF, et al. A framework for online mobility pattern discovery from trajectory data streams. In:Proc. of the 17th IEEE Int'l Conf. on Mobile Data Management (MDM). IEEE, 2016,1:365-368.
    [96] Lee JG, Han J, Whang KY. Trajectory clustering:A partition-and-group framework. In:Proc. of the 2007 ACM SIGMOD Int'l Conf. on Management of Data. ACM, 2007. 593-604.
    [97] Kim Y, Han J, Yuan C. TOPTRAC:Topical trajectory pattern mining. In:Proc. of the 21st ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2015. 587-596.
    [98] Zhang C, Zhang K, Yuan Q, et al. GMove:Group-Level mobility modeling using geo-tagged social media. In:Proc. of the 22nd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2016.
    [99] Yin H, Zhou X, Shao Y, et al. Joint modeling of user check-in behaviors for point-of-interest recommendation. In:Proc. of the 24th ACM Int'l Conf. on Information and Knowledge Management. ACM, 2015. 1631-1640.
    [100] Cho E, Myers SA, Leskovec J. Friendship and mobility:User movement in location-based social networks. In:Proc. of the 17th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2011. 1082-1090.
    [101] Zhang C, et al. Spatiotemporal activity modeling under data scarcity:A graph-regularized cross-modal embedding approach. In:Proc. of the AAAI. 2018.
    [102] Zhang C, Zhang K, Yuan Q, et al. ReAct:Online multimodal embedding for recency-aware spatiotemporal activity modeling. In:Proc. of the 40th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2017. 245-254.
    [103] Chen Q, Song X, Yamada H, et al. Learning deep representation from big and heterogeneous data for traffic accident inference. In:Proc. of the 30th AAAI Conf. on Artificial Intelligence. 2016.
    [104] Song X, Kanasugi H, Shibasaki R. Deeptransport:Prediction and simulation of human mobility and transportation mode at a citywide level. In:Proc. of the IJCAI. 2016.
    [105] Higgs B, Abbas M. Segmentation and clustering of car-following behavior:Recognition of driving patterns. IEEE Trans. on Intelligent Transportation Systems, 2015,16(1):81-90.
    [106] Liu S, Ni LM, Krishnan R. Fraud detection from taxis' driving behaviors. IEEE Trans. on Vehicular Technology, 2014,63(1):464-472.
    [107] Yuan Q, Cong G, Zhao K, et al. Who, where, when, and what:A nonparametric bayesian approach to context-aware recommendation and search for twitter users. ACM Trans. on Information Systems (TOIS), 2015,33(1):2.
    [108] Wang J, Li M, Han J, et al. Modeling check-in preferences with multidimensional knowledge:A minimax entropy approach. In:Proc. of the 9th ACM Int'l Conf. on Web Search and Data Mining. ACM, 2016. 297-306.
    [109] Zhang D, Guo L, Nie L, Shao J, Wu S, Shen HT. Targeted advertising in public transportation systems with quantitative evaluation. ACM TOIS, 2017,35(3):20:1-20:29.[doi:10.1145/3003725]
    [110] Guo L, Zhang D, Wu H, Cui B, Tan KL. From raw footprints to personal interests:Bridging the semantic gap via trip intention aggregation. In:Proc. of the 33rd IEEE Int'l Conf. on Data Engineering (ICDE). IEEE, 2007. 123-126.[doi:10.1109/ICDE.2017. 55]
    [111] Wu F, Li Z. Where did you go:Personalized annotation of mobility records. In:Proc. of the 25th ACM Int'l Conf. on Information and Knowledge Management. ACM, 2016. 589-598.
    [112] Zhao K, Liu Y, Yuan Q, et al. Towards personalized maps:Mining user preferences from geo-textual data. Proc. of the VLDB Endowment, 2016,9(13):1545-1548.
    [113] Wang W, Yin H, Sadiq S, et al. SPORE:A sequential personalized spatial item recommender system. In:Proc. of the 32nd IEEE Int'l Conf. on Data Engineering (ICDE). IEEE, 2016. 954-965.
    [114] Zhong Y, Yuan N J, Zhong W, et al. You are where you go:Inferring demographic attributes from location check-ins. In:Proc. of the 8th ACM Int'l Conf. on Web Search and Data Mining. ACM, 2015. 295-304.
    [115] Cao W, Wu Z, Wang D, et al. Automatic user identification method across heterogeneous mobility data sources. In:Proc. of the 32nd IEEE Int'l Conf. on Data Engineering (ICDE). IEEE, 2016. 978-989.
    [116] Rossi L, Walker J, Musolesi M. Spatio-Temporal techniques for user identification by means of GPS mobility data. EPJ Data Science, 2015,4(1):1.
    [117] Rossi L, Williams M J, Stich C, et al. Privacy and the city:User identification and location semantics in location-based social networks. arXiv Preprint arXiv:1503.06499, 2015.
    [118] Du N, Dai H, Trivedi R, et al. Recurrent marked temporal point processes:Embedding event history to vector. In:Proc. of the 22nd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2016. 1555-1564.
    [119] Van Canneyt S, Schockaert S, Dhoedt B. Categorizing events using spatio-temporal and user features from Flickr. Information Sciences, 2016,328:76-96.
    [120] Krumm J, Horvitz E. Eyewitness:Identifying local events via space-time signals in twitter feeds. In:Proc. of the 23rd SIGSPATIAL Int'l Conf. on Advances in Geographic Information Systems. ACM, 2015. 20.
    [121] Foley J, Bendersky M, Josifovski V. Learning to extract local events from the Web. In:Proc. of the 38th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2015. 423-432.
    [122] Liu Y, Zhou B, Chen F, et al. Graph topic scan statistic for spatial event detection. In:Proc. of the 25th ACM Int'l on Conf. on Information and Knowledge Management. ACM, 2016. 489-498.
    [123] Liang Y, Caverlee J, Cao C. A noise-filtering approach for spatio-temporal event detection in social media. In:Proc. of the European Conf. on Information Retrieval. Springer Int'l Publishing, 2015. 233-244.
    [124] Hristova D, Liben-Nowell D, Noulas A, et al. If you've got the money, I've got the time:Spatio-Temporal footprints of spending at sports events on foursquare. In:Proc. of the 10th Int'l AAAI Conf. on Web and Social Media. 2016.
    [125] Quezada M, Peña-Araya V, Poblete B. Location-Aware model for news events in social media. In:Proc. of the 38th Intl ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2015. 935-938.
    [126] Zhang W, Qi G, Pan G, et al. City-Scale social event detection and evaluation with taxi traces. ACM Trans. on Intelligent Systems and Technology (TIST), 2015,6(3):40.
    [127] Ouyang RW, Srivastava A, Prabahar P, et al. If you see something, swipe towards it:Crowdsourced event localization using smartphones. In:Proc. of the 2013 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. ACM, 2013. 23-32.
    [128] Ouyang RW, Srivastava M, Toniolo A, et al. Truth discovery in crowdsourced detection of spatial events. IEEE Trans. on Knowledge and Data Engineering, 2016,28(4):1047-1060.
    [129] Patroumpas K, Artikis A, Katzouris N, et al. Event recognition for maritime surveillance. In:Proc. of the EDBT. 2015. 629-640.
    [130] Zhang C, Zhou G, Yuan Q, et al. GeoBurst:Real-Time local event detection in geo-tagged tweet streams. In:Proc. of the 39th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2016. 513-522.
    [131] Zhang C, Lei D, Yuan Q, et al. GeoBurst+:Effective and real-time local event detection in geo-tagged tweet streams. ACM Trans. on Intelligent Systems and Technology (TIST), 2018,9(3):34.
    [132] Abdelhaq H, Sengstock C, Gertz M. Eventweet:Online localized event detection from twitter. Proc. of the VLDB Endowment, 2013,6(12):1326-1329.
    [133] Walther M, Kaisser M. Geo-Spatial event detection in the twitter stream. In:Proc. of the European Conf. on Information Retrieval. Berlin, Heidelberg:Springer-Verlag, 2013. 356-367.
    [134] Maurya A, Murray K, Liu Y, et al. Semantic scan:Detecting subtle, spatially localized events in text streams. arXiv Preprint arXiv:1602.04393, 2016.
    [135] Unankard S, Li X, Sharaf MA. Emerging event detection in social networks with location sensitivity. World Wide Web, 2015,18(5):1393-1417.
    [136] Watanabe K, Ochi M, Okabe M, et al. Jasmine:A real-time local-event detection system based on geolocation information propagated to microblogs. In:Proc. of the 20th ACM Int'l Conf. on Information and Knowledge Management. ACM, 2011. 2541-2544.
    [137] Zhang C, Liu L, Lei D, et al. Triovecevent:Embedding-Based online local event detection in geo-tagged tweet streams. In:Proc. of the 23rd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2017. 595-604.
    [138] Feng W, Zhang C, Zhang W, et al. STREAMCUBE:Hierarchical spatio-temporal hashtag clustering for event exploration over the twitter stream. In:Proc. of the 31st IEEE Int'l Conf. on Data Engineering. IEEE, 2015. 1561-1572.
    [139] Yu Y, Chen X. A survey of point-of-interest recommendation in location-based social networks. In:Proc. of the Workshops at the 29th AAAI Conf. on Artificial Intelligence. 2015.
    [140] Liu X, Liu Y, Li X. Exploring the context of locations for personalized location recommendations. In:Proc. of the 25th Int'l Joint Conf. on Artificial Intelligence (IJCAI 2016). 2016. 1188-1194.
    [141] Yin H, Zhou X, Cui B, et al. Adapting to user interest drift for poi recommendation. IEEE Trans. on Knowledge and Data Engineering, 2016,28(10):2566-2581.
    [142] Li H, Ge Y, Zhu H. Point-of-Interest recommendations:Learning potential check-ins from friends. In:Proc. of the 22nd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2016. 975-984.
    [143] Liu Y, Liu C, Liu B, et al. Unified point-of-interest recommendation with temporal interval assessment. In:Proc. of the 22nd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2016. 1015-1024.
    [144] Cheng C, Yang H, King I, et al. A unified point-of-interest recommendation framework in location-based social networks. ACM Trans. on Intelligent Systems and Technology (TIST), 2016,8(1):10.
    [145] Yin H, Zhou X, Shao Y, et al. Joint modeling of user check-in behaviors for point-of-interest recommendation. In:Proc. of the 24th ACM Int'l Conf. on Information and Knowledge Management. ACM, 2015. 1631-1640.
    [146] Chen X, Zeng Y, Cong G, et al. On information coverage for location category based point-of-interest recommendation. In:Proc. of the AAAI. 2015. 37-43.
    [147] Bagci H, Karagoz P. Context-Aware friend recommendation for location based social networks using random walk. In:Proc. of the 25th Int'l Conf. Companion on World Wide Web. Int'l World Wide Web Conferences Steering Committee, 2016. 531-536.
    [148] Wang Z, Liao J, Cao Q, et al. Friendbook:A semantic-based friend recommendation system for social networks. IEEE Trans. on Mobile Computing, 2015,14(3):538-551.
    [149] Quercia D, Lathia N, Calabrese F, et al. Recommending social events from mobile phone location data. In:Proc. of the 2010 IEEE Int'l Conf. on Data Mining. IEEE, 2010. 971-976.
    [150] Zhang W, Wang J. A collective Bayesian Poisson factorization model for cold-start local event recommendation. In:Proc. of the 21st ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2015. 1455-1464.
    [151] Ayhan S, Samet H. Aircraft trajectory prediction made easy with predictive analytics. In:Proc. of the 22nd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2016. 421-434.
    [152] Hai NT, Nguyen HH, Thai-Nghe N. A mobility prediction model for location-based social networks. In:Proc. of the Asian Conf. on Intelligent Information and Database Systems. Berlin, Heidelberg:Springer-Verlag, 2016. 106-115.
    [153] Besse PC, Guillouet B, Loubes JM, et al. Destination prediction by trajectory distribution based model. arXiv Preprint arXiv:1605.03027, 2016.
    [154] Chen M, Liu Y, Yu X. Predicting next locations with object clustering and trajectory clustering. In:Proc. of the Pacific-Asia Conf. on Knowledge Discovery and Data Mining. Springer Int'l Publishing, 2015. 344-356.
    [155] Xue AY, Qi J, Xie X, et al. Solving the data sparsity problem in destination prediction. The VLDB Journal, 2015,24(2):219-243.
    [156] Wang Y, Yuan NJ, Lian D, et al. Regularity and conformity:Location prediction using heterogeneous mobility data. In:Proc. of the 21st ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2015. 1275-1284.
    [157] de Brébisson A, Simon É, Auvolat A, et al. Artificial neural networks applied to taxi destination prediction. arXiv Preprint arXiv:1508.00021, 2015.
    [158] Clements M, Serdyukov P, De Vries AP, et al. Using flickr geotags to predict user travel behavior. In:Proc. of the 33rd Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2010. 851-852.
    [159] Yin H, Hu Z, Zhou X, et al. Discovering interpretable geo-social communities for user behavior prediction. In:Proc. of the 32nd IEEE Int'l Conf. on Data Engineering (ICDE). IEEE, 2016. 942-953.
    [160] Zhao L, Ye J, Chen F, et al. Hierarchical incomplete multi-source feature learning for spatiotemporal event forecasting. In:Proc. of the 22nd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2016. 805-814.
    [161] Zhao L, Chen F, Lu CT, et al. Spatiotemporal event forecasting in social media. In:Proc. of the SDM. 2015,15:963-971.
    [162] Zhao L, Sun Q, Ye J, et al. Multi-Task learning for spatio-temporal event forecasting. In:Proc. of the 21st ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. ACM, 2015. 1503-1512.
    附中文参考文献:
    [3] 刘大有,陈慧灵,齐红,杨博.时空数据挖掘研究进展.计算机研究与发展,2013,50(2):225-239.
    [4] 吉根林,赵斌.面向大数据的时空数据挖掘综述.南京师范大学学报:自然科学版,2014,37(1):1-7.
    [5] 郑宇.城市计算概述.武汉大学学报(信息科学版),2015,40(1):1-13.
    [7] 高强,张凤荔,王瑞锦,周帆.轨迹大数据:数据处理关键技术研究综述.软件学报,2017,28(4):959-992. http://www.jos.org.cn/1000-9825/5143.htm[doi:10.13328/j.cnki.jos.005143]
    [23] 童咏昕,袁野,成雨蓉,陈雷,王国仁.时空众包数据管理技术研究综述.软件学报,2017,28(1):35-58. http://www.jos.org.cn/1000-9825/5140.htm[doi:10.13328/j.cnki.jos.005140]
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

姚迪,张超,黄建辉,陈越新,毕经平.时空数据语义理解:技术与应用.软件学报,2018,29(7):2018-2045

Copy
Share
Article Metrics
  • Abstract:4868
  • PDF: 11468
  • HTML: 5098
  • Cited by: 0
History
  • Received:June 13,2017
  • Revised:March 16,2018
  • Online: April 16,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the first2035071Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063