Abstract
Now-a-days we are moving towards digitization and making all our devices producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, MongoDB, Redis. Bigdata such as geospatial data requires geospatial analytics in applications such as tourism, marketing, rural development. Spark framework provides operators for storing and processing distributed data. Our earlier work proposed “GeoRediSpark” to integrate Redis with Spark. Redis is a key-value store that uses in-memory store, hence integrating Redis with Spark can extend the real-time processing of geospatial data. The paper investigated on storage and retrieval of Redis built in geospatial queries and added two new geospatial operators namely GeoWithin and GeoIntersect to enhance the capabilities of Redis. Hashed indexing is used to improve the processing performance. Comparison on Redis metrics on three benchmark datasets is made in this paper. Hashset is used to display geographic data. Output of geospatial queries is visualized in specific to type of place and nature of query using Tableau.
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
References
Sukumar P (2017) How Spark and Redis help derive geographical insights about customers. https://build.hoteltonight.com/how-spark-and-redis-help-derive-geographical-insights-about-customers-be7e32c1f479
SriHarsha R (2017) Magellan: Geospatial processing made easy. http://magellan.ghost.io/magellan-geospatial-processing-made-easy/
Nativ S (2017) Building a large scale recommendation engine with Spark and Redis-ML. https://databricks.com/session/building-a-large-scale-recommendation-engine-with-spark-and-redis-ml
Cihan B (2016) Machine learning on steroids with the new Redis-ML module. https://redislabs.com/blog/machine-learning-steroids-new-redis-ml-module/
Hagedorn S, Götze P, Sattler K-U (2017) The Stark framework for spatial temporal data analytics on Spark. In: Proceedings of 20th international conference on extending database technology (EDBT), pp 123–142
Tang M, Yu Y, Aref WG, Mahmood AR, Malluhi QM, Ouzzani M (2016) In-memory distributed spatial query processing and optimization, pp 1–15. http://merlintang.github.io/paper/memory-distributed-spatial.pdf
Tang M, Yu Y, Malluhi QM, Ouzzani M, Aref WG (2016) Location Spark: a distributed in memory data management system for big spatial data. Proc VLDB Endowment 9(13):1565–1568
Hendawi AM, Ali M, Mokbel MF (2017) Panda∗: a generic and scalable framework for predictive spatio-temporal queries. GeoInformatica 21(2):175–208
Putri FK, Song G, Kwon J, Rao P (2017) DISPAQ: distributed profitable-area query from big taxi trip data. Sensors 17(10):2201, 1–42
Hegde V, Aswathi TS, Sidharth R (2016) Student residential distance calculation using Haversine formulation and visualization through Googlemap for admission analysis. In: Proceedings of IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–5
Li L, Taniar D, Indrawan-Santiago M, Shao Z (2017) Surrounding join query processing in spatial databases. Proceedings of ADC 2017, pp 17–28. Springer International Publishing
Vasavi S, Priyanka GVN, Anu Gokhale A (2019) Framework for visualization of geospatial query processing by integrating Redis with Spark, pp 1–19, IJMSTR, vol 6, issue 1 (in press)
Places in India (2018) http://www.latlong.net/country/india-102.html. Accessed 1 June 2018
Geospatial Analytics in Magellan (2018) https://raw.githubusercontent.com/dima42/uber-gps-analysis/master/gpsdata/all.tsv. Accessed 1 June 2018
Spatial dataset (2018) http://www.cs.utah.edu/~lifeifei/research/tpq/cal.cnode. Accessed 1 June 2018
Branagan C, Crosby P (2013) Understanding the top 5 Redis performance metrics. Datadog Inc, pp 1–22
Wang Y (2018) Vecstra: an efficient and scalable geo-spatial in-memory cache. In: Proceedings of the VLDB 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Priyanka, G.V.N., Vasavi, S., Anu Gokhale, A. (2020). Evaluation of Performance Metrics in GeoRediSpark Framework for GeoSpatial Query Processing. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-24322-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24321-0
Online ISBN: 978-3-030-24322-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)