{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T09:28:12Z","timestamp":1769333292444,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOLIT(The Ministry of Land, Infrastructure and Transport), Korea","award":["18NSIP-B081011-05"],"award-info":[{"award-number":["18NSIP-B081011-05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The conventional extracting\u2013transforming\u2013loading (ETL) system is typically operated on a single machine not capable of handling huge volumes of geospatial big data. To deal with the considerable amount of big data in the ETL process, we propose D_ELT (delayed extracting\u2013loading \u2013transforming) by utilizing MapReduce-based parallelization. Among various kinds of big data, we concentrate on geospatial big data generated via sensors using Internet of Things (IoT) technology. In the IoT environment, update latency for sensor big data is typically short and old data are not worth further analysis, so the speed of data preparation is even more significant. We conducted several experiments measuring the overall performance of D_ELT and compared it with both traditional ETL and extracting\u2013loading\u2013 transforming (ELT) systems, using different sizes of data and complexity levels for analysis. The experimental results show that D_ELT outperforms the other two approaches, ETL and ELT. In addition, the larger the amount of data or the higher the complexity of the analysis, the greater the parallelization effect of transform in D_ELT, leading to better performance over the traditional ETL and ELT approaches.<\/jats:p>","DOI":"10.3390\/ijgi8110475","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T04:41:27Z","timestamp":1571978487000},"page":"475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Junghee","family":"Jo","sequence":"first","affiliation":[{"name":"Busan National University of Education, Busan 46241, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang-Woo","family":"Lee","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2015.10.012","article-title":"Geospatial big data handling theory and methods: A review and research challenges","volume":"115","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","unstructured":"Morais, C.D. (2018, April 04). Where Is the Phrase \u201c80% of Data is Geographic?\u201d. Available online: http:\/\/www.gislounge.com\/80-percent-data-is-geographic."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jeansoulin, R. (2016). Review of forty years of technological changes in geomatics toward the big data paradigm. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5090155"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, Z., Liu, Q., Deng, M., and Xu, F. (2017, January 10\u201312). Handling multiple testing in local statistics of spatial association by controlling the false discovery rate: A comparative analysis. Proceedings of the IEEE 2nd International Conference 2017Big data Analysis (ICBDA), Beijing, China.","DOI":"10.1109\/ICBDA.2017.8078722"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, P., Di, L., Du, Q., and Wang, L. (2018). Remote Sensing Big data: Theory, Methods and Applications. Remote Sens., 10.","DOI":"10.3390\/rs10050711"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, P., and Shi, W. (2018). Measuring the Spatial Relationship Information of Multi-Layered Vector Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030088"},{"key":"ref_7","unstructured":"White, T. (2012). Hadoop: The Definitive Guide, O\u2019Reilly Media, Inc.. [3rd ed.]."},{"key":"ref_8","unstructured":"Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., and Stoica, I. (2010). Spark: Cluster Computing with Working Sets, HotCloud."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Eldawy, A. (2014, January 22). SpatialHadoop: Towards flexible and scalable spatial processing using MapReduce. Proceedings of the SIGMOD PhD symposium 2014, Snowbird, UT, USA.","DOI":"10.1145\/2602622.2602625"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, J., Wu, J., and Sarwat, M. (2015, January 3\u20136). Geospark: A cluster computing framework for processing large-scale spatial data. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA.","DOI":"10.1145\/2820783.2820860"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jo, J., and Lee, K.W. (2018). High-Performance Geospatial Big data Processing System Based on MapReduce. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100399"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sabtu, A., Azmi, N.F.M., Sjarif, N.N.A., Ismail, S.A., Yusop, O.M., Sarkan, H., and Chuprat, S. (2017, January 16\u201317). The challenges of extract, transform and loading (ETL) system implementation for near real-time environment. Proceedings of the 2017 International Conference on Research and Innovation in Information Systems (ICRIIS) 2017, Langkawi, Malaysia.","DOI":"10.1109\/ICRIIS.2017.8002467"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.datak.2017.08.003","article-title":"A Fine Grained Distribution Approach for ETL Processes in Big data Environments","volume":"111","author":"Bala","year":"2017","journal-title":"Data Knowl. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, X., Thomsen, C., and Pedersen, T.B. (2013). ETLMR: A highly scalable dimensional ETL framework based on MapReduce. Transactions on Large-Scale Data-and Knowledge-Centered Systems VIII, Springer.","DOI":"10.1007\/978-3-642-37574-3_1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Misra, S., Saha, S.K., and Mazumdar, C. (2013, January 16\u201318). Performance Comparison of Hadoop Based Tools with Commercial ETL Tools-A Case Study. Proceedings of the International Conference on Big Data Analytics, Mysore, India.","DOI":"10.1007\/978-3-319-03689-2_12"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bala, M., Boussaid, O., and Alimazighi, Z. (2014, January 10\u201313). P-ETL: Parallel-ETL based on the MapReduce paradigm. Proceedings of the 2014 IEEE\/ACS 11th International Conference on Computer Systems and Applications (AICCSA), Doha, Qatar.","DOI":"10.1109\/AICCSA.2014.7073177"},{"key":"ref_17","unstructured":"(2019, September 23). Marmot from GitHub. Available online: https:\/\/github.com\/kwlee0220\/marmot.server.dist."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Trujillo, J., and Lujan-Mora, S. (2003). A UML based approach for modeling ETL processes in data warehouses. Conceptual Modeling\u2014ER 2003, Proceedings of the International Conference on Conceptual Modeling, Chicago, IL, USA, 13\u201316 October 2003, Springer.","DOI":"10.1007\/978-3-540-39648-2_25"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"El Akkaoui, Z., and Zimanyi, E. (2009). Defining ETL worfklows using BPMN and BPEL. DOLAP \u201809, Proceedings of the ACM Twelfth International Workshop on Data Warehousing and OLAP, Hong Kong, China, 6 November 2009, ACM.","DOI":"10.1145\/1651291.1651299"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Thomsen, C., and Bach Pedersen, T. (2009). pygrametl: A powerful programming framework for extract-transform-load programmers. DOLAP \u201909, Proceedings of the ACM Twelfth International Workshop on Data Warehousing and OLAP, Hong Kong, China, 6 November 2009, ACM.","DOI":"10.1145\/1651291.1651301"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zheng, L., Sun, M., Luo, Y., Song, X., Yang, C., Hu, F., and Yu, M. (2018). Utilizing MapReduce to Improve Probe-Car Track Data Mining. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070287"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yao, X., Mokbel, M., Ye, S., Li, G., Alarabi, L., Eldawy, A., Zhao, Z., Zhao, L., and Zhu, D. (2018). LandQv2: A MapReduce-based system for processing arable land quality big data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070271"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.14778\/1687553.1687576","article-title":"MAD skills: New analysis practices for big data","volume":"2","author":"Cohen","year":"2009","journal-title":"Proc. VLDB Endow."},{"key":"ref_24","unstructured":"Devi, P.S., Rao, V.V., and Raghavender, K. (2014, January 2\u20134). Emerging Technology Big data-Hadoop over Datawarehousing ETL. Proceedings of the International Conference (IRF), Pretoria, South Africa."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.datak.2017.01.001","article-title":"Big data technologies and management: What conceptual modeling can do","volume":"108","author":"Storey","year":"2017","journal-title":"Data Knowl. Eng."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/475\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:29:01Z","timestamp":1760189341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/475"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,24]]},"references-count":25,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["ijgi8110475"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8110475","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,24]]}}}