DOI: https://doi.org/10.14710/jtsiskom.2022.14268

Spatial Skyline Query Based on Surrounding Environment Untuk Data Streaming Menggunakan Apache-Spark

1Program Studi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Indonesia

2Department of Agricultural Industrial Technology, Bogor Agricultural University, Kampus IPB, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kota Bogor, Jawa Barat 16680, Indonesia

3Department of Computer Science, Bogor Agricultural University, Kampus IPB, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kota Bogor, Jawa Barat 16680, Indonesia

4 Department of Informatic, Universitas Nasional, Jl. Sawo Manila No.61, RT.14/RW.7, Pejaten Bar., Kec. Ps. Minggu, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta 12520, Indonesia

View all affiliations
Received: 19 Jul 2021; Published: 30 Apr 2022.
Open Access Copyright (c) 2022 Raden Muhamad Firzatullah
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Previous research on Spatial Skyline Query Based on Surrounding Environment left a challenge in finding skyline objects that support the use of mobile devices. This study introduces a method that allows users to search for spatial objects dynamically. Cloud-based streaming data services are currently available to support the dynamic search of spatial skyline objects. Under these conditions, streaming data requires a longer processing time. This study aims to examine the effectiveness and efficiency of Apache-Spark in developing Spatial Skyline Query Based on Surrounding Environment in processing streaming data. Further implementation of the developed algorithm can provide better location access for users on mobile devices. Comparative analysis of algorithm execution time is performed by comparing algorithm processing on a single processor and cluster computing using various evaluation parameters. The test results on each parameter show that the computation time of the proposed algorithm on a single computation is not as good as the previous algorithm. However, in cluster computing, the proposed algorithm is superior
Fulltext View|Download Email colleagues
Keywords: Skyline Query; Data Streaming; Spatial Object; Apache-Spark; Cluster Computing

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query Modified skyline query to measure priority region for personal protective equipment recipient of COVID-19 health workers More related articles
Most cited articles
People counter on CCTV video using histogram of oriented gradient and Kalman filter methods Pembuatan Aplikasi Terintegrasi, Pendataan Barang di Gudang Berbasis Android Backward Chaining Analysis Model in Detecting Game Addiction Levels in Children Perancangan Sistem Manajemen Restoran dengan Aplikasi Pemesanan Restoran Berbasis Mobile dalam Jaringan Lokal Parameter tuning in KNN for software defect prediction: an empirical analysis More cited articles
  1. Zhiming C, Arefin MS, Morimoto Y. 2013. Skyline Queries for Spatial Objects: A Method for Selecting Spatial Objects Based on Surrounding Environments. ICNC (3): 215-220
  2. Kodama K, Iijima Y, Guo X, Ishikawa Y. 2009. Skyline Queries Based on User Locations and Preferences for Making Location-based Recommendations. LBSN. 9–16
  3. Guo X, Ishikawa Y, Gao Y. 2010. Direction-based Spatial Skylines. ACM International Workshop on Data Engineering for Wire-less and Mobile Access. 73–80
  4. Lin X, Xu J, Hu H. 2013. Range-based Skyline Queries in Mobile Environments. IEEE Transactions on Knowledge and Data Engineering. 25(4):835-849
  5. Agarwal R, Garg D. 2014. Finding Nearest Facility for Multiple Customers using Voronoi Diagram. International Advance Computing Conference (IACC). 641-646
  6. Arefin M, Ma G, Morimoto Y. 2014. A Spatial Skyline Query for a Group of Users. Journal of Software. 9(11): 2938–2947
  7. Annisa, Siddique MA, Zaman A, Morimoto Y. 2015. A Method for Selecting Desirable Unfixed Shape Areas from Integrated Geographic Information System. International Congress on Advanced Applied Informatics. 4:195-200
  8. Annisa, Zaman A, Morimoto Y. 2016. Area Skyline Query for Selecting Good Locations in a Map. Journal of Information Processing. 24(6):946-955
  9. Annisa, Siddique MA, Morimoto Y. 2017. Finding Key Persons on Social Media by Using MapReduce Skyline. International Journal of Networking and Computing. 7(1): 86-360
  10. Bartolini I, Ciaccia P, Patella M. 2006. SaLSa: computing the skyline without scanning the whole sky. ACM. 15(6): 405-414
  11. Borzsonyi S, Kossmann, Stocker, Konrad. 2001. The Skyline Operator. Proceedings 17th International Conference on Data Engineering. 17(10): 421–430
  12. Papadias D, Tao Y, Fu G, Seeger B. 2003. An optimal and progres-sive algorithm for skyline queries. ACM SIGMOD. 467–478
  13. Sharifzadeh M, Shahabi C. 2006. The Spatial Skyline Queries. VLDB (32): 751-762
  14. Zhiming C, Jinhao X, Arefin MS, Morimoto Y. 2012. Real Estate Recommender: Location Query for Selecting Spatial Objects. IEEE: 5-7
  15. Namiot D. 2015. On Big Data Stream Processing. International Journal of Open Information Technologies. 3(8): 48-51
  16. Li Xiaoyang, Wang Yijie, Li Xioling, Wang Yuan. 2014. Parallel Skyline Queries Over Uncertain Data Streams in Cloud Computing Environments. International Journal of Web and Grid Services. 10(1): 24-52
  17. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM. 2013. Time-aware Point of Interest Recommendation. ACM SIGIR. 36: 363-372
  18. Brandt P, Karstensen J, Fu Y. 2017. The Meridional Ageostrophic Transport in The Tropical Atlantic. Ocean Sci (13): 531-549
  19. Zaharia M, Armburst M, Xin RS, Lian C, Huai Y, Liu D, Bradley JK, Meng X, Kaftan T, Franklin MJ, Ghodsi A. 2015. Spark SQL: Relational Data Processing in Spark. ACM SIGMOID. 15: 1383-1394
  20. Tiziano DM, Gabriele M. 2016. Parallel Patterns for Window-Based Stateful Operators on Data Streams: An Algorithmic Skeleton Approach. International Journal of Parallel Programming. 45(2): 382-401
  21. Sarkas N, Das G, Koudas N, Tung AKH. 2008. Categorical Skylines for Data Streaming. ACM SIGMOD. 8: 239-250
  22. Sechin A. 2016. Parallel Computing in Photogrammetry. GIM International. 1: 21-23
  23. Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ, Ghodsi A, Gonzalez J, Shenker S, Stoica I. 2016. Apache-spark: A Unified Engine for Big Data Processing. ACM. 59(11): 56-65
  24. Reyes-Ortiz JL, Oneto L, Anguita D. 2015. Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf. Elsevier. 53: 121-130
  25. Chomicki J, Patella M, Zezula P. 2003. Skyline with Presorting. ICDE. 717-719
  26. Brummelen V, Robert G. 2013. Heavenly Mathematics: The Forgotten Art of Spherical Trigonometry. Princeton University Press. ISBN 9780691148922. 11-10
  27. Battersby SE, Finn MP, Usery EL, Yamamoto KH. 2014. Implications of Web Mercator and Its Use in Online Mapping. Cartographica The International Journal for Geographic Information and Geovisualization. 49(2):85-101
  28. Wilkinson B, Allen M. 2004. Parallel Programming. United States of America: Pearson Education

Last update:

No citation recorded.

Last update: 2024-12-20 00:29:39

No citation recorded.