Advertisement

GeoInformatica

, Volume 21, Issue 2, pp 263–291 | Cite as

Distributed processing of big mobility data as spatio-temporal data streams

  • Zdravko GalićEmail author
  • Emir Mešković
  • Dario Osmanović
Article

Abstract

Recent rapid development of wireless communication, mobile computing, global navigation satellite systems (GNSS), and spatially enabled sensors are leading to an exponential growth of available mobility data produced continuously at high speed. Due to these advancements, a new class of monitoring applications has come to the focus, including real-time intelligent transportation systems, traffic monitoring and mobile objects tracking. These new information flow processing (IFP) application domains need to process huge volume of mobility data arriving in the form of continuous data streams from mobile objects. IFP applications are pushing traditional database technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. Mobility data, i.e. real-time, transient, time-varying sequences of spatio-temporal data items, generated by embedded positioning sensors demonstrates at least two Big Data core features: volume and velocity. Existing distributed data stream management systems (DSMS), real-time computing systems (RTCS) and their processing models are dominantly based on relational paradigm and continuous operator model. Thus, they have rudimentary spatio-temporal capabilities, provide expensive fault recovery requiring either hot replication or long recovery times, and do not handle faults and slow nodes. The framework proposed in this paper is a cornerstone towards efficient real-time managing and monitoring of mobile objects through distributed spatio-temporal streams processing on large clusters. A prototype implementation is rooted in a new stream processing model that overcomes the challenges of current distributed stream processing models and enable seamless integration with batch and interactive processing like MapReduce.

Keywords

Big data Data stream architectures GeoStreaming Mobility data Parallel processing Real-time distributed Spatio-temporal data streams 

Notes

Acknowledgments

The authors would like to thank Mirta Baranović, Damir Kalpić and anonymous reviewers for their helpful and constructive comments that helped us to improve the paper.

References

  1. 1.
    Aitchison A (2012) Pro spatial with SQL server 2012. Apress Media LLC, New YorkCrossRefGoogle Scholar
  2. 2.
    Akidau T, Bradshaw R, Chambers C, Chernyak S, Fernández-Moctezuma R, Lax R, McVeety S, Mills D, Perry F, Schmidt E, Whittle S (2015) The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. PVLDB 8(12):1792–1803. http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf Google Scholar
  3. 3.
    Alexandrov A, Bergmann R, Ewen S, Freytag J, Hueske F, Heise A, Kao O, Leich M, Leser U, Markl V, Naumann F, Peters M, Rheinländer A, Sax MJ, Schelter S, Höger M, Tzoumas K, Warneke D (2014) The Stratosphere platform for big data analytics. VLDB J 23(6):939–964. doi: 10.1007/s00778-014-0357-y CrossRefGoogle Scholar
  4. 4.
    Ali MH, Gerea C, Raman BS, Sezgin B, Tarnavski T, Verona T, Wang P, Zabback P, Kirilov A, Ananthanarayan A, Lu M, Raizman A, Krishnan R, Schindlauer R, Grabs T, Bjeletich S, Chandramouli B, Goldstein J, Bhat S, Li Y, Nicola V D, Wang X, Maier D, Santos I, Nano O, Grell S (2009) Microsoft CEP server and online behavioral targeting. PVLDB 2(2):1558–1561Google Scholar
  5. 5.
    Ali MH, Chandramouli B, Raman BS, Katibah E (2010) Spatio-temporal stream processing in Microsoft StreamInsight. IEEE Data Eng Bull 33(2):69–74Google Scholar
  6. 6.
    de Almeida VT, Güting RH, Behr T (2006) Querying moving objects in SECONDO. In: Mobile Data Management, pp 47–51Google Scholar
  7. 7.
    Apache Foundation (2016a) Apache Flink . http://flink.apache.org
  8. 8.
    Apache Foundation (2016b) Apache Hadoop. http://hadoop.apache.org
  9. 9.
    Apache Foundation (2016c) Apache Hive. http://hive.apache.org
  10. 10.
    Apache Foundation (2016d) Apache Samza. http://samza.apache.org
  11. 11.
    (2016e) Apache Spark. http://spark.apache.org/
  12. 12.
    Apache Foundation (2016f) Apache Spark Streaming. http://spark.apache.org/streaming
  13. 13.
    Apache Foundation (2016g) Apache Storm. http://storm.apache.org
  14. 14.
    Apache Foundation (2016h) Flink DataStream API Programming Guide. https://ci.apache.org/projects/flink/flink-docs-release-0.10/apis/streaming_guide.html
  15. 15.
    Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Popa L, Abiteboul S, Kolaitis P G (eds) PODS, ACM, pp 1–16Google Scholar
  16. 16.
    Balazinska M, Balakrishnan H, Madden S, Stonebraker M (2008) Fault-tolerance in the Borealis distributed stream processing system. ACM Trans Database Syst 33(1):3:1–3:44CrossRefGoogle Scholar
  17. 17.
    Bettini C, Dyreson CE, Evans WS, Snodgrass RT, Wang XS (1997) A glossary of time granularity concepts. In: Temporal Databases, Dagstuhl, pp 406–413Google Scholar
  18. 18.
    Biem A, Bouillet E, Feng H, Ranganathan A, Riabov A, Verscheure O, Koutsopoulos HN, Moran C (2010) IBM InfoSphere Streams for scalable, real-time, intelligent transportation services. In: Elmagarmid AK, Agrawal D (eds) Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6-10, 2010, ACM. doi: 10.1145/1807167.1807291, pp 1093–1104
  19. 19.
    California Center for Innovative Transportation (2015) The Mobile Millennium Project. http://traffic.berkeley.edu
  20. 20.
    Chandramouli B, Goldstein J, Barnett M, DeLine R, Platt JC, Terwilliger JF, Wernsing J (2014) Trill: A high-performance incremental query processor for diverse analytics. PVLDB 8(4):401–412. http://www.vldb.org/pvldb/vol8/p401-chandramouli.pdf Google Scholar
  21. 21.
    Chandy KM, Lamport L (1985) Distributed snapshots: Determining global states of distributed systems. ACM Trans Comput Syst 3(1):63–75. doi: 10.1145/214451.214456 CrossRefGoogle Scholar
  22. 22.
    Chen CX (2008) Spatio-temporal query languages. In: Shekhar S, Xiong H (eds) Encyclopedia of GIS. Springer, Berlin, pp 1125–1128Google Scholar
  23. 23.
    Commonwealth Computer Research Inc (2016) GeoMesa. http://www.geomesa.org
  24. 24.
    Condie T, Conway N, Alvaro P, Hellerstein JM, Elmeleegy K, Sears R (2010) MapReduce online. In: NSDI, USENIX Association, pp 313–328Google Scholar
  25. 25.
    Dean J, Ghemawat S (2004) MapReduce: Simplified data processing on large clusters. In: OSDI, USENIX Association, pp 137–150Google Scholar
  26. 26.
    Ebbers M, Abdel-Gayed A, Budhi V, Dolot F, Kamat V, Picone R, Trevelin J (2013) Addressing Data Volume, Velocity, and Variety with IBM InfoSphere Streams 3.0. IBMGoogle Scholar
  27. 27.
    Eldawy A, Mokbel MF (2013) A demonstration of SpatialHadoop: An efficient MapReduce framework for spatial data. PVLDB 6(12):1230–1233Google Scholar
  28. 28.
    Eldawy A, Elganainy M, Bakeer A, Abdelmotaleb A, Mokbel M (2015) Sphinx: Distributed execution of interactive sql queries on big spatial data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, New York, NY, USA, GIS ’15, pp 78:1–78:4 doi: 10.1145/2820783.2820869
  29. 29.
    Esper Tech Inc (2016) EsperTech. http://www.espertech.com/products/
  30. 30.
    Fox A, Eichelberger C, Hughes J, Lyon S (2013) Spatio-temporal indexing in non-relational distributed databases. In: Proceedings of the 2013 IEEE International Conference on Big Data, 6-9 October 2013, Santa Clara, CA, USA, IEEE, pp 291–299. doi: 10.1109/BigData.2013.6691586
  31. 31.
    Franklin MJ, Krishnamurthy S, Conway N, Li A, Russakovsky A, Thombre N (2009) Continuous analytics: Rethinking query processing in a network-effect world. In: CIDR. www.crdrdb.org
  32. 32.
    Galić Z, Mešković E, Križanović K, Baranović M (2012) OCEANUS: a spatio-temporal data stream system prototype. In: Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming, ACM, New York, NY, USA, IWGS ’12, pp 109–115. doi: 10.1145/2442968.2442982
  33. 33.
    Galić Z, Baranović M, Križanović K, Mešković E (2014) Geospatial data streams: Formal framework and implementation. Data Knowl Eng 91:1–16CrossRefGoogle Scholar
  34. 34.
    Golab L, Özsu M T (2010) Data stream management. Synthesis lectures on data management morgan claypool publishers, San Rafael, CAGoogle Scholar
  35. 35.
    Güting RH (1993) Second-order signature: A tool for specifying data models, query processing, and optimization. In: Buneman P, Jajodia S (eds) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993,ACM Press, pp 277–286. doi: 10.1145/170035.170079
  36. 36.
    Güting R H, Schneider M (2005) Moving objects databases. Morgan Kaufmann, San FranciscoGoogle Scholar
  37. 37.
    Güting R H, Böhlen M H, Erwig M, Jensen C S, Lorentzos N A, Schneider M, Vazirgiannis M (2000) A foundation for representing and quering moving objects. ACM Trans Database Syst 25(1):1– 42CrossRefGoogle Scholar
  38. 38.
    Güting RH, Behr T, Düngten C (2013) Trajectory databases. In: Mobility data – modeling, management, and understanding. Cambridge University Press, New York, pp 42–61Google Scholar
  39. 39.
    Hortonworks (2016) Magellan: Geospatial Analytics on Spark. http://hortonworks.com/blog/magellan-geospatial-analytics-in-spark
  40. 40.
    Hu X, Lin TY, Raghavan VV, Wah BW, Baeza-Yates RA, Fox G, Shahabi C, Smith M, Yang Q, Ghani R, Fan W, Lempel R, Nambiar R (eds.) (2013) In: Proceedings of the 2013 IEEE International Conference on Big Data, 6-9 October 2013, Santa Clara, CA, USA, IEEE . http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6679357
  41. 41.
    Huang Y, Zhang C (2008) New data types and operations to support geo-streams. In: Cova T J, Miller H J, Beard K, Frank A U, Goodchild M F (eds) GIScience, Springer, Lecture Notes in Computer Science, vol 5266, pp 106–118Google Scholar
  42. 42.
    Hunter T, Das T, Zaharia M, Abbeel P, Bayen A M (2013) Large-scale estimation in cyberphysical systems using streaming data: a case study with arterial traffic estimation. IEEE T Automation Science and Engineering 10(4):884–898CrossRefGoogle Scholar
  43. 43.
    Information Management Lab – University of Piraeus (2016) HERMES. http://hermes-mod.java.net
  44. 44.
    ISO 19107:2003 (2003) Geographic information – Spatial schemaGoogle Scholar
  45. 45.
    ISO 19108:2002 (2002) Geographic information – Temporal schemaGoogle Scholar
  46. 46.
    ISO 19141:2008 (2008) Geographic information – Schema for moving featuresGoogle Scholar
  47. 47.
    ISO/IEC 13249-3:2011 (2011) Information technology – Database languages – SQL multimedia and application packages – Part 3: SpatialGoogle Scholar
  48. 48.
    Jiang J, Bao H, Chang EY, Li Y (2012) MOIST: A scalable and parallel moving object indexer with school tracking. PVLDB 5(12):1838–1849. http://vldb.org/pvldb/vol5/p1838_junchenjiang_vldb2012.pdf Google Scholar
  49. 49.
    Kazemitabar SJ, Kashani FB, McLeod D (2011) Geostreaming in cloud. In: Ali MH, Hoel EG, Kashani FB (eds) Proceedings of the 2011 ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2011, November 1, 2011, Chicago, IL, USA, ACM, pp 3–9. doi: 10.1145/2064959.2064962
  50. 50.
    Kornacker M, Behm A, Bittorf V, Bobrovytsky T, Ching C, Choi A, Erickson J, Grund M, Hecht D, Jacobs M, Joshi I, Kuff L, Kumar D, Leblang A, Li N, Pandis I, Robinson H, Rorke D, Rus S, Russell J, Tsirogiannis D, Wanderman-Milne S, Yoder M (2015) Impala: A modern, open-source SQL engine for Hadoop. In: CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 4-7, 2015, Online Proceedings, http://www.cidrdb.org/cidr2015/Papers/CIDR15_Paper28.pdf
  51. 51.
    Koubarakis M, Sellis TK, Frank AU, Grumbach S, Güting RH, Jensen CS, Lorentzos NA, Manolopoulos Y, Nardelli E, Pernici B, Schek HJ, Scholl M, Theodoulidis B, Tryfona N (2003) Spatio-Temporal Databases: The CHOROCHRONOS Approach, Lecture Notes in Computer Science, vol 2520, SpringerGoogle Scholar
  52. 52.
    Krämer J, Seeger B (2009) Semantics and implementation of continuous sliding window queries over data streams. ACM Trans Database Syst 34(1)Google Scholar
  53. 53.
    Law YN, Wang H, Zaniolo C (2011) Relational languages and data models for continuous queries on sequences and data streams. ACM Trans Database Syst 36(2):8:1–8:32CrossRefGoogle Scholar
  54. 54.
    Loeckx J, Ehrich HD, Wolf M (1996) Specification of Abstract Data Types. John Wiley & Sons and B. G. TeubnerGoogle Scholar
  55. 55.
    Lu J, Güting RH (2013) Parallel SECONDO: Practical and efficient mobility data processing in the cloud. In: Proceedings of the 2013 IEEE International Conference on Big Data, 6-9 October 2013, Santa Clara, CA, USA, IEEE, pp 17–25. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6679357
  56. 56.
    Ma Q, Yang B, Qian W, Zhou A (2009) Query processing of massive trajectory data based on MapReduce. In: Meng X, Wang H, Chen Y (eds) Proceedings of the First International CIKM Workshop on Cloud Data Management, CloudDb 2009, Hong Kong, China, November 2, 2009, ACM, pp 9–16. doi: 10.1145/1651263.1651266
  57. 57.
    Mahmood AR, Aly AM, Qadah T, Rezig EK, Daghistani A, Madkour A, Abdelhamid AS, Hassan MS, Aref WG, Basalamah S (2015) Tornado: A distributed spatio-textual stream processing system. PVLDB 8 (12):2020–2031. http://www.vldb.org/pvldb/vol8/p2020-mahmood.pdf Google Scholar
  58. 58.
    Davis M (2016) JTS Topology Suite. http://tsusiatsoftware.net/jts/main.html
  59. 59.
    Meehan J, Tatbul N, Zdonik S, Aslantas C, Çetintemel U, Du J, Kraska T, Madden S, Maier D, Pavlo A, Stonebraker M, Tufte K, Wang H (2015) S-store: Streaming meets transaction processing. PVLDB 8(13):2134–2145. http://www.vldb.org/pvldb/vol8/p2134-meehan.pdf Google Scholar
  60. 60.
    Miller J, Raymond M, Archer J, Adem S, Hansel L, Konda S, Luti M, Zhao Y, Teredesai A, Ali M H (2011) An extensibility approach for spatio-temporal stream processing using Microsoft StreamInsight. In: Pfoser D, Tao Y, Mouratidis K, Nascimento M A, Mokbel M F, Shekhar S, Huang Y (eds) SSTD, Springer, Lecture Notes in Computer Science, vol 6849, pp 496–501Google Scholar
  61. 61.
    Mokbel MF, Xiong X, Hammad MA, Aref WG (2005) Continuous query processing of spatio-temporal data streams in PLACE. GeoInformatica 9(4):343–365CrossRefGoogle Scholar
  62. 62.
    Murray C (2014) Oracle Spatial and Graph Developer’s Guide. OracleGoogle Scholar
  63. 63.
    Murray DG, McSherry F, Isaacs R, Isard M, Barham P, Abadi M (2013) Naiad: a timely dataflow system. In: Kaminsky M, Dahlin M (eds) ACM SIGOPS 24th Symposium on Operating Systems Principles, SOSP ’13, Farmington, PA, USA, November 3-6, 2013, pp 439–455. ACM. doi: 10.1145/2517349.2522738
  64. 64.
    Nidzwetzki JK, Güting RH (2015) Distributed SECONDO: A highly available and scalable system for spatial data processing. In: Claramunt C, Schneider M, Wong RC, Xiong L, Loh W, Shahabi C, Li K (eds) Advances in Spatial and Temporal Databases - 14th International Symposium, SSTD 2015, Hong Kong, China, August 26-28, 2015. Proceedings, Springer, Lecture Notes in Computer Science, vol 9239, pp 491–496. doi: 10.1007/978-3-319-22363-6_28
  65. 65.
    Obe R, Hsu L, Ramsey P (2012) PostGIS in Action Manning Publications, Greenwich, CTGoogle Scholar
  66. 66.
    Oracle (2015) Oracle Fusion Middleware – Developing Applications for Oracle CQL Data Cartridges, 12c Release 1 (12.2.1). Oracle CorporationGoogle Scholar
  67. 67.
    Patroumpas K, Sellis TK (2004) Managing trajectories of moving objects as data streams. In: Sander J, Nascimento M A (eds) STDBM, pp 41–48Google Scholar
  68. 68.
    Patroumpas K, Sellis TK (2011) Maintaining consistent results of continuous queries under diverse window specifications. Inf Syst 36(1):42–61CrossRefGoogle Scholar
  69. 69.
    Patroumpas K, Sellis TK (2012) Event processing and real-time monitoring over streaming traffic data. In: Martino SD, Peron A, Tezuka T (eds), vol 7236. W2GIS, Springer, Lecture Notes in Computer Science, pp 116–133Google Scholar
  70. 70.
    Qian Z, He Y, Su C, Wu Z, Zhu H, Zhang T, Zhou L, Yu Y, Zhang Z (2013) TimeStream: reliable stream computation in the cloud. In: Hanzálek Z, Härtig H, Castro M, Kaashoek MF (eds) EuroSys, ACM, pp 1–14Google Scholar
  71. 71.
    SAP (2016) SAP HANA Data Streaming. http://help.sap.com/hana_options_sds
  72. 72.
    Sarwat M (2015) Interactive and scalable exploration of big spatial data - A data management perspective. In: Jensen CS, Xie X, Zadorozhny V, Madria S, Pitoura E, Zheng B, Chow C (eds) 16th IEEE International Conference on Mobile Data Management, MDM 2015, Pittsburgh, PA, USA, June 15-18, 2015 - Volume 1, IEEE, pp 263–270. doi: 10.1109/MDM.2015.67
  73. 73.
    Schneider M (1997) Spatial data types for database systems, finite resolution geometry for geographic information systems, Lecture Notes in Computer Science, vol 1288. Springer, BerlinGoogle Scholar
  74. 74.
    Schneider M (2009) Spatial and spatio-temporal data models and languages. In: Liu L, Özsu MT (eds) Encyclopedia of Database Systems, Springer US, pp 2681–2685, pp 2681–2685. doi: 10.1007/978-0-387-39940-9_360
  75. 75.
    Shekhar S, Chawla S (2003) Spatial databases - a tour prentice hall. Upper Saddle River, NJGoogle Scholar
  76. 76.
    (2008). In: Shekhar S, Xiong H (eds) Encyclopedia of GIS. Springer, BerlinGoogle Scholar
  77. 77.
    Stonebraker M, Çetintemel U, Zdonik S B (2005) The 8 requirements of real-time stream processing. SIGMOD Record 34(4):42–47CrossRefGoogle Scholar
  78. 78.
    Tan H, Luo W, Ni LM (2012) CloST: a Hadoop-based storage system for big spatio-temporal data analytics. In: Chen X, Lebanon G, Wang H, Zaki MJ (eds) 21st ACM International Conference on Information and Knowledge Management, CIKM’12, Maui, HI, USA, October 29 - November 02, 2012, ACM, pp 2139–2143. doi: 10.1145/2396761.2398589
  79. 79.
    TIBCO (2016) TIBCO StreamBase. http://www.streambase.com
  80. 80.
    Xiong X, Mokbel MF, Aref WG (2008) Spatio-temporal database. In: Shekhar S, Xiong H (eds) Encyclopedia of GIS. Springer, Berlin, pp 1114–1115Google Scholar
  81. 81.
    Yu J, Wu J, Sarwat M (2015) GeoSpark: A cluster computing framework for processing large-scale spatial data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, New York, NY, USA, GIS ’15, pp 70:1–70:4. doi: 10.1145/2820783.2820860
  82. 82.
    Zheng Y, Chen Y, Li Q, Xie X, Ma W (2010a) Understanding transportation modes based on GPS data for web applications. TWEB 4(1). doi: 10.1145/1658373.1658374
  83. 83.
    Zheng Y, Xie X, Ma W (2010b) GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39. http://sites.computer.org/debull/A10june/geolife.pdf

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computing, Department of Applied ComputingUniversity of ZagrebZagrebCroatia
  2. 2.Faculty of Electrical EngineeringUniversity of TuzlaTuzlaBosnia and Herzegovina

Personalised recommendations