| dc.description.abstract | Previous studies of spatial skyline queries based on the surrounding
environment posed a challenge in handling skyline searches that supported mobile
users. This study introduces a method which allows users to search for spatial
objects dynamically. Online streaming data services are currently available to
support user movements. With this condition, streaming data requires a longer
execution time in processing. This work examines the effectiveness and efficiency
of the Apache-Spark framework in developing spatial skyline queries based on the
surrounding environment algorithm for data streaming. Further implementation of
this algorithm on mobile devices can provide better location access for users.
Comparative analysis of processing time was carried by comparing single
processing, parallel distributed computing, and cluster computing algorithms with
various measurement evaluation parameters. Result of the test on "number of
types in surrounding environment" parameter shows that the execution time of
parallel and cluster computing is faster than single computing, 63.694 and 72.772
times faster, respectively. Test on the "raw data size" parameter indicates that
parallel computing execution time is 46.553 times faster than single computing,
while cluster computing is 74.187 times faster than single computing. In the "main
facility search radius" parameter, cluster computing is 45.224 times faster than
single computing, and parallel computing is 77.022 times faster than single
computing. Lastly, the "number grid" parameter indicates that the execution time
for both parallel and cluster computing is faster than single computing, 157.944
and 276.251 times faster, respectively. | id |
