Skip to main content
Erschienen in: Journal of Network and Systems Management 4/2020

27.07.2020

Big Spatial Data Management for the Internet of Things: A Survey

verfasst von: Isam Mashhour Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari

Erschienen in: Journal of Network and Systems Management | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of geo-referenced IoT spatial data that if could be analyzed correctly would unleash important information. This can feed decision support systems for better decision making and strategic planning regarding important aspects of our lives that depend heavily on location-based services. Several spatial data management systems for IoT data in Cloud has recently gained momentum. However, the literature is still missing a comprehensive survey that conceptualize a convenient framework that classify those frameworks under appropriate categories. In this survey paper, we focus on the management of big geospatial data that are generated by IoT data sources. We also define a conceptual framework and match the works of the recent literature with it. We then identify future research frontiers in the field depending on the surveyed works.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Al Jawarneh, I.M., Bellavista, P., Foschini, L., Montanari, R.: Spatial-aware approximate big data stream processing. In: 2019 IEEE global communications conference (GLOBECOM), pp. 1–6 (2019) Al Jawarneh, I.M., Bellavista, P., Foschini, L., Montanari, R.: Spatial-aware approximate big data stream processing. In: 2019 IEEE global communications conference (GLOBECOM), pp. 1–6 (2019)
2.
Zurück zum Zitat Aljawarneh, I.M., Bellavista, P., De Rolt, C. R., Foschini, L.: Dynamic identification of participatory mobile health communities. In: Cloud infrastructures, services, and IoT systems for smart cities, pp. 208–217. Anonymous Springer (2017) Aljawarneh, I.M., Bellavista, P., De Rolt, C. R., Foschini, L.: Dynamic identification of participatory mobile health communities. In: Cloud infrastructures, services, and IoT systems for smart cities, pp. 208–217. Anonymous Springer (2017)
3.
Zurück zum Zitat Sahoo, S.S., Wei, A., Tatsuoka, C., Ghosh, K., Lhatoo, S.D.: Processing neurology clinical data for knowledge discovery: scalable data flows using distributed computing. In: Machine Learning for Health Informatics, pp. 303–318. Anonymous Springer (2016) Sahoo, S.S., Wei, A., Tatsuoka, C., Ghosh, K., Lhatoo, S.D.: Processing neurology clinical data for knowledge discovery: scalable data flows using distributed computing. In: Machine Learning for Health Informatics, pp. 303–318. Anonymous Springer (2016)
4.
Zurück zum Zitat Aji, A., Wang, F., Saltz, J.H.: Towards building a high performance spatial query system for large scale medical imaging data. In: Proceedings of the 20th international conference on advances in geographic information systems, pp. 309–318 (2012) Aji, A., Wang, F., Saltz, J.H.: Towards building a high performance spatial query system for large scale medical imaging data. In: Proceedings of the 20th international conference on advances in geographic information systems, pp. 309–318 (2012)
5.
Zurück zum Zitat Gomes, E., Dantas, M.A., de Macedo, D.D., De Rolt, C., Brocardo, M.L., Foschini, L.: Towards an infrastructure to support big data for a smart city project. In: 2016 IEEE 25th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), pp. 107–112 (2016) Gomes, E., Dantas, M.A., de Macedo, D.D., De Rolt, C., Brocardo, M.L., Foschini, L.: Towards an infrastructure to support big data for a smart city project. In: 2016 IEEE 25th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), pp. 107–112 (2016)
6.
Zurück zum Zitat Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Al Jawarneh, I.M., Zanni, A.: How fog computing can support latency/reliability-sensitive IoT applications: an overview and a taxonomy of state-of-the-art solutions (2019) Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Al Jawarneh, I.M., Zanni, A.: How fog computing can support latency/reliability-sensitive IoT applications: an overview and a taxonomy of state-of-the-art solutions (2019)
7.
Zurück zum Zitat Vatsavai, R.R., Ganguly, A., Chandola, V., Stefanidis, A., Klasky, S., Shekhar, S.: Spatiotemporal data mining in the era of big spatial data: algorithms and applications. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics for big geospatial data, pp. 1–10 (2012) Vatsavai, R.R., Ganguly, A., Chandola, V., Stefanidis, A., Klasky, S., Shekhar, S.: Spatiotemporal data mining in the era of big spatial data: algorithms and applications. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics for big geospatial data, pp. 1–10 (2012)
8.
Zurück zum Zitat Botta, A., De Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst 56, 684–700 (2016)CrossRef Botta, A., De Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst 56, 684–700 (2016)CrossRef
9.
Zurück zum Zitat Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Zanni, A.: A survey on fog computing for the Internet of Things. Pervasive Mob. Comput. 52, 71–99 (2019)CrossRef Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Zanni, A.: A survey on fog computing for the Internet of Things. Pervasive Mob. Comput. 52, 71–99 (2019)CrossRef
10.
Zurück zum Zitat Jones, K.E., Patel, N.G., Levy, M.A., Storeygard, A., Balk, D., Gittleman, J.L., Daszak, P.: Global trends in emerging infectious diseases. Nature 451(7181), 990–993 (2008)CrossRef Jones, K.E., Patel, N.G., Levy, M.A., Storeygard, A., Balk, D., Gittleman, J.L., Daszak, P.: Global trends in emerging infectious diseases. Nature 451(7181), 990–993 (2008)CrossRef
11.
Zurück zum Zitat Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Zanni, A.: A survey on fog computing for the Internet of Things. Pervasive Mob. Comput. 52, 71–99 (2018)CrossRef Bellavista, P., Berrocal, J., Corradi, A., Das, S.K., Foschini, L., Zanni, A.: A survey on fog computing for the Internet of Things. Pervasive Mob. Comput. 52, 71–99 (2018)CrossRef
12.
Zurück zum Zitat Ge, M., Bangui, H., Buhnova, B.: Big data for internet of things: a survey. Future Gener. Comput. Syst. 87, 601–614 (2018)CrossRef Ge, M., Bangui, H., Buhnova, B.: Big data for internet of things: a survey. Future Gener. Comput. Syst. 87, 601–614 (2018)CrossRef
13.
Zurück zum Zitat Siow, E., Tiropanis, T., Hall, W.: Analytics for the internet of things: a survey. ACM Comput. Surv. 51(4), 1–36 (2018)CrossRef Siow, E., Tiropanis, T., Hall, W.: Analytics for the internet of things: a survey. ACM Comput. Surv. 51(4), 1–36 (2018)CrossRef
14.
Zurück zum Zitat Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10(10-10), 95 (2010) Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10(10-10), 95 (2010)
15.
Zurück zum Zitat Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: Msst, pp. 1–10 (2010) Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: Msst, pp. 1–10 (2010)
16.
Zurück zum Zitat Bradshaw, S., Chodorow, K.: Mongodb: the definitive guide: powerful and scalable data storage, 3rd edn. O’Reilly Media Inc, Newton (2018) Bradshaw, S., Chodorow, K.: Mongodb: the definitive guide: powerful and scalable data storage, 3rd edn. O’Reilly Media Inc, Newton (2018)
17.
Zurück zum Zitat Banker, K.: MongoDB in action. Manning Publications Co., Shelter Island (2011) Banker, K.: MongoDB in action. Manning Publications Co., Shelter Island (2011)
18.
Zurück zum Zitat Yu, J., Zhang, Z., Sarwat, M.: Spatial data management in apache spark: the geospark perspective and beyond. GeoInformatica 23(1), 37–78 (2019)CrossRef Yu, J., Zhang, Z., Sarwat, M.: Spatial data management in apache spark: the geospark perspective and beyond. GeoInformatica 23(1), 37–78 (2019)CrossRef
19.
Zurück zum Zitat Khan, R., Khan, S.U., Zaheer, R., Khan, S.: Future internet: the internet of things architecture, possible applications and key challenges. In: 2012 10th international conference on frontiers of information technology, pp. 257–260 (2012) Khan, R., Khan, S.U., Zaheer, R., Khan, S.: Future internet: the internet of things architecture, possible applications and key challenges. In: 2012 10th international conference on frontiers of information technology, pp. 257–260 (2012)
20.
Zurück zum Zitat Tsichritzis, D.C., Lochovsky, F.H.: Hierarchical data-base management: a survey. ACM Comput. Surv. 8(1), 105–123 (1976)MATHCrossRef Tsichritzis, D.C., Lochovsky, F.H.: Hierarchical data-base management: a survey. ACM Comput. Surv. 8(1), 105–123 (1976)MATHCrossRef
21.
Zurück zum Zitat DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: amazon’s highly available key-value store. ACM SIGOPS Oper. Syst. Rev. 41(6), 205–220 (2007)CrossRef DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: amazon’s highly available key-value store. ACM SIGOPS Oper. Syst. Rev. 41(6), 205–220 (2007)CrossRef
22.
Zurück zum Zitat Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. ACM SIGOPS Oper. Syst. Rev. 44(2), 35–40 (2010)CrossRef Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. ACM SIGOPS Oper. Syst. Rev. 44(2), 35–40 (2010)CrossRef
23.
Zurück zum Zitat Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2), 1–26 (2008)CrossRef Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2), 1–26 (2008)CrossRef
24.
Zurück zum Zitat Team, A.H.: Apache hbase reference guide. Apache, Version, vol. 2, (0) (2016) Team, A.H.: Apache hbase reference guide. Apache, Version, vol. 2, (0) (2016)
25.
Zurück zum Zitat Grolinger, K., Higashino, W.A., Tiwari, A., Capretz, M.A.: Data management in cloud environments: NoSQL and NewSQL data stores. J. Cloud Comput. Adv. Syst. Appl. 2(1), 22 (2013)CrossRef Grolinger, K., Higashino, W.A., Tiwari, A., Capretz, M.A.: Data management in cloud environments: NoSQL and NewSQL data stores. J. Cloud Comput. Adv. Syst. Appl. 2(1), 22 (2013)CrossRef
26.
Zurück zum Zitat Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef
27.
Zurück zum Zitat Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Management 23(3), 567–619 (2015)CrossRef Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Management 23(3), 567–619 (2015)CrossRef
28.
Zurück zum Zitat Al Jawarneh, I.M., Bellavista, P., Casimiro, F., Corradi, A, Foschini, L.: Cost-effective strategies for provisioning NoSQL storage services in support for industry 4.0. In: 2018 IEEE symposium on computers and communications (ISCC), pp. 1227 (2018) Al Jawarneh, I.M., Bellavista, P., Casimiro, F., Corradi, A, Foschini, L.: Cost-effective strategies for provisioning NoSQL storage services in support for industry 4.0. In: 2018 IEEE symposium on computers and communications (ISCC), pp. 1227 (2018)
29.
Zurück zum Zitat Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoop gis: a high performance spatial data warehousing system over mapreduce. Proc. VLDB Endowment 6(11), 1009–1020 (2013)CrossRef Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoop gis: a high performance spatial data warehousing system over mapreduce. Proc. VLDB Endowment 6(11), 1009–1020 (2013)CrossRef
30.
Zurück zum Zitat Eldawy, A., Mokbel, M.F.: Spatialhadoop: a mapreduce framework for spatial data. In: 2015 IEEE 31st international conference on data engineering, pp. 1352–1363 (2015) Eldawy, A., Mokbel, M.F.: Spatialhadoop: a mapreduce framework for spatial data. In: 2015 IEEE 31st international conference on data engineering, pp. 1352–1363 (2015)
31.
Zurück zum Zitat You, S., Zhang, J., Gruenwald, L.: Large-scale spatial join query processing in cloud. In: 2015 31st IEEE international conference on data engineering workshops, pp. 34–41 (2015) You, S., Zhang, J., Gruenwald, L.: Large-scale spatial join query processing in cloud. In: 2015 31st IEEE international conference on data engineering workshops, pp. 34–41 (2015)
32.
Zurück zum Zitat Nishimura, S., Das, S., Agrawal, D., El Abbadi, A.: Md-hbase: a scalable multi-dimensional data infrastructure for location aware services. In: in 2011 IEEE 12th international conference on mobile data management, pp. 7–16 (2011) Nishimura, S., Das, S., Agrawal, D., El Abbadi, A.: Md-hbase: a scalable multi-dimensional data infrastructure for location aware services. In: in 2011 IEEE 12th international conference on mobile data management, pp. 7–16 (2011)
33.
Zurück zum Zitat Yu, J., Wu, J., Sarwat, M.: 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, pp. 70 (2015) Yu, J., Wu, J., Sarwat, M.: 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, pp. 70 (2015)
34.
Zurück zum Zitat Tang, M., Yu, Y., Aref, W.G., Mahmood, A.R., Malluhi, Q.M., Ouzzani, M.: Locationspark: in-memory distributed spatial query processing and optimization. In: CoRR, pp. 1–15 (2019) Tang, M., Yu, Y., Aref, W.G., Mahmood, A.R., Malluhi, Q.M., Ouzzani, M.: Locationspark: in-memory distributed spatial query processing and optimization. In: CoRR, pp. 1–15 (2019)
35.
Zurück zum Zitat Eldawy, A., Mokbel, M.F., Alharthi, S., Alzaidy, A., Tarek, K., Ghani, S.: Shahed: a mapreduce-based system for querying and visualizing spatio-temporal satellite data. In: 2015 IEEE 31st international conference on data engineering, pp. 1585–1596 (2015) Eldawy, A., Mokbel, M.F., Alharthi, S., Alzaidy, A., Tarek, K., Ghani, S.: Shahed: a mapreduce-based system for querying and visualizing spatio-temporal satellite data. In: 2015 IEEE 31st international conference on data engineering, pp. 1585–1596 (2015)
36.
Zurück zum Zitat Vo, H., Aji, A., Wang, F.: SATO: a spatial data partitioning framework for scalable query processing. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 545–548 (2014) Vo, H., Aji, A., Wang, F.: SATO: a spatial data partitioning framework for scalable query processing. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 545–548 (2014)
37.
Zurück zum Zitat Bentley, J.L., Friedman, J.H.: Data structures for range searching. ACM Comput. Surv. 11(4), 397–409 (1979)CrossRef Bentley, J.L., Friedman, J.H.: Data structures for range searching. ACM Comput. Surv. 11(4), 397–409 (1979)CrossRef
38.
Zurück zum Zitat Knuth, D.E.: The art of computer programming: sorting and searching, vol. 3, 2nd edn. Addison-Wesley Publishing Company, Redwood City (1998)MATH Knuth, D.E.: The art of computer programming: sorting and searching, vol. 3, 2nd edn. Addison-Wesley Publishing Company, Redwood City (1998)MATH
39.
Zurück zum Zitat Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta Informatica 4(1), 1–9 (1974)MATHCrossRef Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta Informatica 4(1), 1–9 (1974)MATHCrossRef
40.
41.
Zurück zum Zitat Sellis, T.K., Roussopoulos, N., Faloutsos, C.: The R -tree: a dynamic index for multi-dimensional objects. In: Proceedings of the 13th international conference on very large data bases, pp. 507–518 (1987) Sellis, T.K., Roussopoulos, N., Faloutsos, C.: The R -tree: a dynamic index for multi-dimensional objects. In: Proceedings of the 13th international conference on very large data bases, pp. 507–518 (1987)
43.
Zurück zum Zitat Fuchs, H., Kedem, Z.M., Naylor, B.F.: On visible surface generation by a priori tree structures. In: ACM Siggraph computer graphics, pp. 124–133 (1980) Fuchs, H., Kedem, Z.M., Naylor, B.F.: On visible surface generation by a priori tree structures. In: ACM Siggraph computer graphics, pp. 124–133 (1980)
44.
Zurück zum Zitat Leutenegger, S.T., Lopez, M.A., Edgington, J.: STR: a simple and efficient algorithm for R-tree packing. In: Proceedings 13th international conference on data engineering, pp. 497–506 (1997) Leutenegger, S.T., Lopez, M.A., Edgington, J.: STR: a simple and efficient algorithm for R-tree packing. In: Proceedings 13th international conference on data engineering, pp. 497–506 (1997)
45.
Zurück zum Zitat Asano, T., Ranjan, D., Roos, T., Welzl, E., Widmayer, P.: Space-filling curves and their use in the design of geometric data structures. Theor. Comput. Sci. 181(1), 3–15 (1997)MathSciNetMATHCrossRef Asano, T., Ranjan, D., Roos, T., Welzl, E., Widmayer, P.: Space-filling curves and their use in the design of geometric data structures. Theor. Comput. Sci. 181(1), 3–15 (1997)MathSciNetMATHCrossRef
46.
Zurück zum Zitat Aljawarneh, I.M., Bellavista, P., Corradi, A., Montanari, R., Foschini, L., Zanotti, A.: Efficient spark-based framework for big geospatial data query processing and analysis. In: 2017 IEEE symposium on computers and communications (ISCC), pp. 851–856 (2017) Aljawarneh, I.M., Bellavista, P., Corradi, A., Montanari, R., Foschini, L., Zanotti, A.: Efficient spark-based framework for big geospatial data query processing and analysis. In: 2017 IEEE symposium on computers and communications (ISCC), pp. 851–856 (2017)
47.
Zurück zum Zitat Al Jawarneh, I.M., Bellavista, P., Corradi, A., Foschini, L., Montanari, R., Zanotti, A.: In-memory spatial-aware framework for processing proximity-alike queries in big spatial data. In: 2018 IEEE 23rd international workshop on computer aided modeling and design of communication links and networks (CAMAD), pp. 1–6 (2018) Al Jawarneh, I.M., Bellavista, P., Corradi, A., Foschini, L., Montanari, R., Zanotti, A.: In-memory spatial-aware framework for processing proximity-alike queries in big spatial data. In: 2018 IEEE 23rd international workshop on computer aided modeling and design of communication links and networks (CAMAD), pp. 1–6 (2018)
48.
Zurück zum Zitat Aly, A.M., Mahmood, A.R., Hassan, M.S., Aref, W.G., Ouzzani, M., Elmeleegy, H., Qadah, T.: AQWA: adaptive query workload aware partitioning of big spatial data. Proc. VLDB Endowment 8(13), 2062–2073 (2015)CrossRef Aly, A.M., Mahmood, A.R., Hassan, M.S., Aref, W.G., Ouzzani, M., Elmeleegy, H., Qadah, T.: AQWA: adaptive query workload aware partitioning of big spatial data. Proc. VLDB Endowment 8(13), 2062–2073 (2015)CrossRef
49.
Zurück zum Zitat Abdelhamid, A.S., Tang, M., Aly, A.M., Mahmood, A.R., Qadah, T., Aref, W.G., Basalamah, S.: Cruncher: distributed in-memory processing for location-based services. In: 2016 IEEE 32nd international conference on data engineering (ICDE), pp. 1406–1409 (2016) Abdelhamid, A.S., Tang, M., Aly, A.M., Mahmood, A.R., Qadah, T., Aref, W.G., Basalamah, S.: Cruncher: distributed in-memory processing for location-based services. In: 2016 IEEE 32nd international conference on data engineering (ICDE), pp. 1406–1409 (2016)
50.
Zurück zum Zitat Eldawy, A., Alarabi, L., Mokbel, M.F.: Spatial partitioning techniques in SpatialHadoop. Proc. VLDB Endowment 8(12), 1602–1605 (2015)CrossRef Eldawy, A., Alarabi, L., Mokbel, M.F.: Spatial partitioning techniques in SpatialHadoop. Proc. VLDB Endowment 8(12), 1602–1605 (2015)CrossRef
51.
Zurück zum Zitat Amini, S., Gerostathopoulos, I., Prehofer, C.: Big data analytics architecture for real-time traffic control. In: 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS), pp. 710–715 (2017) Amini, S., Gerostathopoulos, I., Prehofer, C.: Big data analytics architecture for real-time traffic control. In: 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS), pp. 710–715 (2017)
52.
Zurück zum Zitat Abdelhaq, H., Gertz, M.: On the locality of keywords in twitter streams. In: Proceedings of the 5th ACM SIGSPATIAL international workshop on geostreaming, pp. 12–20 (2014) Abdelhaq, H., Gertz, M.: On the locality of keywords in twitter streams. In: Proceedings of the 5th ACM SIGSPATIAL international workshop on geostreaming, pp. 12–20 (2014)
53.
Zurück zum Zitat Jacox, E.H., Samet, H.: Spatial join techniques. ACM Trans. Database Syst. 32(1), 7 (2007)CrossRef Jacox, E.H., Samet, H.: Spatial join techniques. ACM Trans. Database Syst. 32(1), 7 (2007)CrossRef
54.
Zurück zum Zitat Kriegel, H., Kröger, P., Sander, J., Zimek, A.: Density-based clustering. Wiley Interdiscip Rev Data Min Knowl Discov 1(3), 231–240 (2011)CrossRef Kriegel, H., Kröger, P., Sander, J., Zimek, A.: Density-based clustering. Wiley Interdiscip Rev Data Min Knowl Discov 1(3), 231–240 (2011)CrossRef
55.
Zurück zum Zitat Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, pp. 226–231 (1996) Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, pp. 226–231 (1996)
56.
Zurück zum Zitat Dai, B., Lin, I.: Efficient map/reduce-based dbscan algorithm with optimized data partition. In: 2012 IEEE fifth international conference on cloud computing, pp. 59–66 (2012) Dai, B., Lin, I.: Efficient map/reduce-based dbscan algorithm with optimized data partition. In: 2012 IEEE fifth international conference on cloud computing, pp. 59–66 (2012)
57.
Zurück zum Zitat He, Y., Tan, H., Luo, W., Feng, S., Fan, J.: MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data. Front. Comput. Sci. 8(1), 83–99 (2014)MathSciNetCrossRef He, Y., Tan, H., Luo, W., Feng, S., Fan, J.: MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data. Front. Comput. Sci. 8(1), 83–99 (2014)MathSciNetCrossRef
58.
59.
Zurück zum Zitat Wang, W., Yang, J., Muntz, R.: PK-tree: a spatial index structure for high dimensional point data. In: Information Organization and Databases Anonymous Springer, pp. 281–293 (2000) Wang, W., Yang, J., Muntz, R.: PK-tree: a spatial index structure for high dimensional point data. In: Information Organization and Databases Anonymous Springer, pp. 281–293 (2000)
60.
Zurück zum Zitat Aji, A., Wang, F.: High performance spatial query processing for large scale scientific data. In: Proceedings of the on SIGMOD/PODS 2012 Ph.D. symposium, pp. 9–14 (2012) Aji, A., Wang, F.: High performance spatial query processing for large scale scientific data. In: Proceedings of the on SIGMOD/PODS 2012 Ph.D. symposium, pp. 9–14 (2012)
61.
Zurück zum Zitat Zhong, Y., Zhu, X., Fang, J.: Elastic and effective spatio-temporal query processing scheme on hadoop. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics for big geospatial data, pp. 33–42 (2012) Zhong, Y., Zhu, X., Fang, J.: Elastic and effective spatio-temporal query processing scheme on hadoop. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics for big geospatial data, pp. 33–42 (2012)
62.
Zurück zum Zitat Hagedorn, S., Gotze, P., Sattler, K.: The STARK framework for spatio-temporal data analytics on spark. Datenbanksysteme Für Business, Technologie Und Web (BTW 2017) (2017) Hagedorn, S., Gotze, P., Sattler, K.: The STARK framework for spatio-temporal data analytics on spark. Datenbanksysteme Für Business, Technologie Und Web (BTW 2017) (2017)
63.
Zurück zum Zitat Giachetta, R.: A framework for processing large scale geospatial and remote sensing data in MapReduce environment. Comput. Graph. 49, 37–46 (2015)CrossRef Giachetta, R.: A framework for processing large scale geospatial and remote sensing data in MapReduce environment. Comput. Graph. 49, 37–46 (2015)CrossRef
64.
Zurück zum Zitat Whitman, R.T., Park, M.B., Ambrose, S.M., Hoel, E.G.: Spatial indexing and analytics on hadoop. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 73–82 (2014) Whitman, R.T., Park, M.B., Ambrose, S.M., Hoel, E.G.: Spatial indexing and analytics on hadoop. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 73–82 (2014)
65.
Zurück zum Zitat Al Naami, K.M., Seker, S., Khan, L.: GISQF: an efficient spatial query processing system. In: 2014 IEEE 7th international conference on cloud computing, pp. 681–688 (2014) Al Naami, K.M., Seker, S., Khan, L.: GISQF: an efficient spatial query processing system. In: 2014 IEEE 7th international conference on cloud computing, pp. 681–688 (2014)
66.
Zurück zum Zitat Fahmy, M.M., Elghandour, I., Nagi M.: CoS-HDFS: Co-locating geo-distributed spatial data in hadoop distributed file system. In: 2016 IEEE/ACM 3rd international conference on big data computing applications and technologies (BDCAT), pp. 123–132 (2016) Fahmy, M.M., Elghandour, I., Nagi M.: CoS-HDFS: Co-locating geo-distributed spatial data in hadoop distributed file system. In: 2016 IEEE/ACM 3rd international conference on big data computing applications and technologies (BDCAT), pp. 123–132 (2016)
67.
Zurück zum Zitat Han, D., Stroulia, E.: Hgrid: a data model for large geospatial data sets in hbase. In: 2013 IEEE sixth international conference on cloud computing, pp. 910–917 (2013) Han, D., Stroulia, E.: Hgrid: a data model for large geospatial data sets in hbase. In: 2013 IEEE sixth international conference on cloud computing, pp. 910–917 (2013)
68.
Zurück zum Zitat Weixin, Z., Zhe, Y., Lin, W., Feilong, W., Chengqi, C.: The non-sql spatial data management model in big data time. In: 2015 IEEE international geoscience and remote sensing symposium (IGARSS), pp. 4506–4509 (2015) Weixin, Z., Zhe, Y., Lin, W., Feilong, W., Chengqi, C.: The non-sql spatial data management model in big data time. In: 2015 IEEE international geoscience and remote sensing symposium (IGARSS), pp. 4506–4509 (2015)
69.
Zurück zum Zitat Li, S., Amin, M.T., Ganti, R., Srivatsa, M., Hu, S., Zhao, Y., Abdelzaher, T.: Stark: optimizing in-memory computing for dynamic dataset collections. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), pp. 103–114 (2017) Li, S., Amin, M.T., Ganti, R., Srivatsa, M., Hu, S., Zhao, Y., Abdelzaher, T.: Stark: optimizing in-memory computing for dynamic dataset collections. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), pp. 103–114 (2017)
70.
Zurück zum Zitat Zheng, K., Gu, D., Fang, F., Zhang, M., Zheng, K., Li, Q.: Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency. Cluster Comput. 20(4), 2833–2844 (2017)CrossRef Zheng, K., Gu, D., Fang, F., Zhang, M., Zheng, K., Li, Q.: Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency. Cluster Comput. 20(4), 2833–2844 (2017)CrossRef
71.
Zurück zum Zitat Brinkhoff, T., Kriegel, H., Schneider, R., Seeger, B.: Multi-step processing of spatial joins. ACM 23(2), 197–208 (1994) Brinkhoff, T., Kriegel, H., Schneider, R., Seeger, B.: Multi-step processing of spatial joins. ACM 23(2), 197–208 (1994)
72.
Zurück zum Zitat Sriharsha, R.: Magellan: geospatial analytics on spark. Retrieved May, vol. 1, pp. 2018 (2015) Sriharsha, R.: Magellan: geospatial analytics on spark. Retrieved May, vol. 1, pp. 2018 (2015)
73.
Zurück zum Zitat Baig, F., Vo, H., Kurc, T., Saltz, J., Wang, F.: Sparkgis: resource aware efficient in-memory spatial query processing. In: Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 1–10 (2017) Baig, F., Vo, H., Kurc, T., Saltz, J., Wang, F.: Sparkgis: resource aware efficient in-memory spatial query processing. In: Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 1–10 (2017)
74.
Zurück zum Zitat Xie, D., Li, F., Yao, B., Li, G., Zhou, L., Guo, M.: Simba: efficient in-memory spatial analytics. In: Proceedings of the 2016 international conference on management of data, pp. 1071–1085 (2016) Xie, D., Li, F., Yao, B., Li, G., Zhou, L., Guo, M.: Simba: efficient in-memory spatial analytics. In: Proceedings of the 2016 international conference on management of data, pp. 1071–1085 (2016)
75.
Zurück zum Zitat Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the third ACM symposium on cloud computing, pp. 7 (2012) Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the third ACM symposium on cloud computing, pp. 7 (2012)
76.
Zurück zum Zitat Delimitrou, C., Kozyrakis, C.: Quasar: resource-efficient and QoS-aware cluster management. In: ACM SIGARCH computer architecture news, pp. 127–144 (2014) Delimitrou, C., Kozyrakis, C.: Quasar: resource-efficient and QoS-aware cluster management. In: ACM SIGARCH computer architecture news, pp. 127–144 (2014)
Metadaten
Titel
Big Spatial Data Management for the Internet of Things: A Survey
verfasst von
Isam Mashhour Al Jawarneh
Paolo Bellavista
Antonio Corradi
Luca Foschini
Rebecca Montanari
Publikationsdatum
27.07.2020
Verlag
Springer US
Erschienen in
Journal of Network and Systems Management / Ausgabe 4/2020
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI
https://doi.org/10.1007/s10922-020-09549-6

Weitere Artikel der Ausgabe 4/2020

Journal of Network and Systems Management 4/2020 Zur Ausgabe