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Performance Evaluation of Trajectory Queries on Multiprocessor and Cluster
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we evaluate the performance of trajectory queries that are handled by Cassandra, MongoDB,  and  PostgreSQL.  The  evaluation  is  conducted  on  a  multiprocessor  and  a  cluster. Telecommunication companies collect a lot of data from their mobile users. These data must be analysed in order to support business decisions, such  as  infrastructure  planning.  The  optimal choice of hardware platform and database can be different from a query to another. We use data collected  from  Telenor  Sverige,  a  telecommunication  company  that  operates  in  Sweden.  These data are collected every five minutes for an entire  week  in  a  medium  sized  city.  The  execution time  results  show  that  Cassandra  performs  much  better  than  MongoDB  and  PostgreSQL  for queries  that  do  not  have  spatial  features.  Statio’s  Cassandra  Lucene  index  incorporates  a geospatial  index  into  Cassandra,  thus  making  Cassandra  to  perform  similarly  as  MongoDB  to handle  spatial  queries.  In  four  use  cases,  namely, distance  query,  k-nearest  neigbhor  query, range   query,   and   region   query,   Cassandra   performs   much   better   than   MongoDB   and PostgreSQL for two cases, namely range query and region query. The scalability is also good for these two use cases.

Place, publisher, year, edition, pages
2016. Vol. 6
Keywords [en]
Databases evaluation, Trajectory queries, Multiprocessor and cluster, NoSQL database, Cassandra, MongoDB, PostgreSQL
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-15757OAI: oai:DiVA.org:bth-15757DiVA, id: diva2:1173850
Conference
Third International Conference on Data Mining and Database (DMDB 2016), vienna, austria
Funder
Knowledge Foundation, 20140032Available from: 2018-01-14 Created: 2018-01-14 Last updated: 2018-01-19Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf