Abstract
Selecting the appropriate data management infrastructure is still a hard task for the designers of mobile applications with large volumes of data. Considering NoSQL needs for such applications, this paper demonstrates how the physical implementation of the database may impact query performance. Specifically, we consider the needs of mobile users that involve constant spatial data traffic, such as querying for points of interest, map visualization, zooming and panning, routing and location tracking. We define a workload and process such queries over three types of databases: relational, document-based and graph-based. Our evaluation shows that a fair comparison requires specific workloads for each mobile feature, but that is not possible using the industry’s standard benchmark tools. Overall, the paper shows that physical design must evolve to take advantage of the performance of NoSQL databases while keeping data consistency and integrity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Foursquare: http://www.foursquare.com.
- 2.
Waze: http://www.waze.com.
- 3.
Using other DBMS requires adapting the database model and queries employed.
- 4.
PostgreSQL: http://www.postgresql.org/.
- 5.
PostGIS: http://postgis.net/.
- 6.
pgRouting: http://pgrouting.org/.
- 7.
MongoDB: http://www.mongodb.org/.
- 8.
Neo4j: http://www.neo4j.org/.
- 9.
Neo4j-Spatial: http://www.neo4j.org/develop/spatial.
- 10.
- 11.
- 12.
GeoJSON: http://www.geojson.org/.
- 13.
GDAL: http://www.gdal.org/.
- 14.
Cisco Visual Networking Index: http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html.
- 15.
NHTS: National Household Travel Survey. Available at http://www.rita.dot.gov/bts/sites/rita.dot. gov.bts/files/subject_areas/national_household_ travel_survey/index.html.
- 16.
Windows Server Resource Monitor: http://msdn.microsoft.com/en-us/library/ms191246.aspx.
- 17.
Java VisualVM: http://visualvm.java.net/.
References
Baptista, C.S., et al.: Using OGC services to interoperate spatial data stored in SQL and NoSQL databases. In: Proceedings of GeoInfo, pp. 61–72. Campos do Jordão, Brazil (2011)
Chen, Y., Raab, F., Katz, R.: From TPC-C to big data benchmarks: a functional workload model. In: Rabl, T., Poess, M., Baru, C., Jacobsen, H.-A. (eds.) WBDB 2012. LNCS, vol. 8163, pp. 28–43. Springer, Heidelberg (2014)
Davis Jr., C.A., Fonseca, F.: Considerations from the development of a local spatial data infrastructure. Inf. Technol. Dev. 12(4), 273–290 (2006)
Do, T.T., Liu, F., Hua, K.A.: When mobile objects’ energy is not so tight: a new perspective on scalability issues of continuous spatial query systems. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 445–458. Springer, Heidelberg (2007)
Ghazal, A., et al.: BigBench: towards an industry standard benchmark for big data analytics. In: Proceedings of SIGMOD, pp. 1197–1208 (2013)
Hora, A.C., Davis Jr., C.A., Moro, M.M.: Mapping network relationships from spatial database schemas to GML documents. J. Inf. Data Manage. 2(1), 67–74 (2011)
Liu, C., Fruin, B.C., Samet, H.: SAC: semantic adaptive caching for spatial mobile applications. In: Proceedings of ACM SIGSPATIAL, pp. 174–183 (2013)
Nascimento, S.M., et al.: The spatial star schema benchmark. In: Proceedings of GeoInfo, pp. 73–84. Campos do Jordão, Brazil (2011)
Niedermayer, J., et al.: Probabilistic nearest neighbor queries on uncertain moving object trajectories. PVLDB 7(3), 205–216 (2014)
Ray, S., Simion, B., Brown, A.D.: Jackpine: a benchmark to evaluate spatial database performance. In: Proceedings of ICDE, pp. 1139–1150 (2011)
Shekhar, S., Evans, M.R., Gunturi, V., Yang, K.S., Cugler, D.C.: Benchmarking spatial big data. In: Rabl, T., Poess, M., Baru, C., Jacobsen, H.-A. (eds.) WBDB 2012. LNCS, vol. 8163, pp. 81–93. Springer, Heidelberg (2014)
Sidlauskas, D., Jensen, C.S.: Spatial joins in main memory: implementation matters!. PVLDB 8(1), 97–100 (2014)
Acknowledgments
This work was funded by CAPES, CNPq and FAPEMIG.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Santos, P.O., Moro, M.M., Davis, C.A. (2015). Comparative Performance Evaluation of Relational and NoSQL Databases for Spatial and Mobile Applications. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-22849-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22848-8
Online ISBN: 978-3-319-22849-5
eBook Packages: Computer ScienceComputer Science (R0)