{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:02:02Z","timestamp":1769763722407,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s11227-021-03793-6","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T08:05:46Z","timestamp":1618819546000},"page":"13018-13045","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An efficient query optimization technique in big data using $$\\sigma$$-ANFIS load balancer and CaM-BW optimizer"],"prefix":"10.1007","volume":"77","author":[{"given":"Deepak","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Vijay Kumar","family":"Jha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"3793_CR1","doi-asserted-by":"crossref","unstructured":"Zhang J (2017) Research on big data storage structure and query optimization. In: International Conference on sComputer Systems, Electronics and Control (ICCSEC). IEEE, pp 1508\u20131511","DOI":"10.1109\/ICCSEC.2017.8446959"},{"key":"3793_CR2","doi-asserted-by":"crossref","unstructured":"Juneja A, Das NN (2019) Big data quality framework: pre-processing data in weather monitoring application. In: International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, pp 559\u2013563","DOI":"10.1109\/COMITCon.2019.8862267"},{"key":"3793_CR3","doi-asserted-by":"crossref","unstructured":"Jemal D, Faiz R, Boukorca A, Bellatreche L (2015) MapReduce-DBMS: an integration model for big data management and optimization. In: Database and Expert Systems Applications. Springer, Cham, pp 430\u2013439","DOI":"10.1007\/978-3-319-22852-5_36"},{"key":"3793_CR4","doi-asserted-by":"crossref","unstructured":"Garg V (2015) Optimization of multiple queries for big data with apache Hadoop\/Hive. In: International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp 938\u2013941","DOI":"10.1109\/CICN.2015.184"},{"issue":"3","key":"3793_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s10844-017-0455-6","volume":"49","author":"R Goswami","year":"2017","unstructured":"Goswami R, Bhattacharyya DK, Dutta M (2017) Materialized view selection using evolutionary algorithm for speeding up big data query processing. J Intell Inf Syst 49(3):407\u2013433","journal-title":"J Intell Inf Syst"},{"key":"3793_CR6","doi-asserted-by":"crossref","unstructured":"Ding D, Dong F, Luo J (2014) Multi-Q: multiple queries optimization based on MapReduce in cloud. In: Second International Conference on Advanced Cloud and Big Data. IEEE, pp 100\u2013107","DOI":"10.1109\/CBD.2014.20"},{"key":"3793_CR7","doi-asserted-by":"crossref","unstructured":"Mateen A, Ali K (2017) Optimization strategies through big-data migration in distributed cloud databases. In: IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). IEEE, pp 96\u201399","DOI":"10.1109\/ICPCSI.2017.8391881"},{"issue":"6","key":"3793_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14257\/ijdta.2017.10.6.01","volume":"10","author":"A Bachhav","year":"2017","unstructured":"Bachhav A, Kharat V, Shelar M (2017) Query optimization for databases in cloud environment: a survey. Int J Database Theory Appl 10(6):1\u201312","journal-title":"Int J Database Theory Appl"},{"issue":"8","key":"3793_CR9","doi-asserted-by":"publisher","first-page":"5420","DOI":"10.1007\/s11227-019-02806-9","volume":"75","author":"A Sebaa","year":"2019","unstructured":"Sebaa A, Tari A (2019) Query optimization in cloud environments: challenges, taxonomy, and techniques. J Supercomput 75(8):5420\u20135450","journal-title":"J Supercomput"},{"key":"3793_CR10","doi-asserted-by":"crossref","unstructured":"Sharma M, Singh G, Singh R (2016) Design and analysis of stochastic DSS query optimizers in a distributed database system. Egypt Inf J 17(2):161\u2013173","DOI":"10.1016\/j.eij.2015.10.003"},{"key":"3793_CR11","doi-asserted-by":"crossref","unstructured":"Sahal R, Khafagy MH, Omara FA (2018) Exploiting coarse-grained reused-based opportunities in big data multi-query optimization. J Comput Sci 26:432\u2013452","DOI":"10.1016\/j.jocs.2017.05.023"},{"issue":"3","key":"3793_CR12","doi-asserted-by":"publisher","first-page":"2166","DOI":"10.1016\/j.jpdc.2013.10.003","volume":"74","author":"R Gu","year":"2014","unstructured":"Gu R, Yang X, Yan J, Sun Y, Wang B, Yuan C, Huang Y (2014) SHadoop: Improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters. J Parallel Distrib Comput 74(3):2166\u20132179","journal-title":"J Parallel Distrib Comput"},{"key":"3793_CR13","doi-asserted-by":"crossref","unstructured":"Viswanathan L, Jindal A, Karanasos K (2018) Query and resource optimization: bridging the gap. In: IEEE 34th International Conference on Data Engineering (ICDE). IEEE, pp 1384\u20131387","DOI":"10.1109\/ICDE.2018.00156"},{"key":"3793_CR14","unstructured":"Lou Y, Ye F (2018) Research on data query optimization based on SparkSQL and MongoDB. In: 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, pp 144\u2013147"},{"key":"3793_CR15","doi-asserted-by":"crossref","unstructured":"Ren Z, Yun N, Shi W, Li Y, Wan J, Yu L, Fan X (2018) Characterizing the effectiveness of query optimizer in spark. In: 2018 IEEE World Congress on Services (SERVICES). IEEE, pp s41\u201342","DOI":"10.1109\/SERVICES.2018.00034"},{"issue":"8","key":"3793_CR16","first-page":"148","volume":"5","author":"AR Thangam","year":"2016","unstructured":"Thangam AR, Peter SJ (2016) An extensive survey on various query optimization techniques. Int J Comput Sci Mob Comput 5(8):148\u2013154","journal-title":"Int J Comput Sci Mob Comput"},{"key":"3793_CR17","doi-asserted-by":"crossref","unstructured":"Ragaventhiran J, Kavithadevi MK (2020) Map-optimize-reduce: CAN tree assisted FP-growth algorithm for clusters based FP mining on Hadoop. Future Gen Comput Syst 103:111\u2013122","DOI":"10.1016\/j.future.2019.09.041"},{"key":"3793_CR18","doi-asserted-by":"crossref","unstructured":"Ghomi EJ, Rahmani AM, Qader NN (2017) Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl 88:50\u201371","DOI":"10.1016\/j.jnca.2017.04.007"},{"issue":"11","key":"3793_CR19","first-page":"115","volume":"3","author":"D Kashyap","year":"2014","unstructured":"Kashyap D, Viradiya J (2014) A survey of various load balancing algorithms in cloud computing. Int J Sci Technol Res 3(11):115\u2013119","journal-title":"Int J Sci Technol Res"},{"key":"3793_CR20","doi-asserted-by":"crossref","unstructured":"Kumar D, Jha VK (2020) An improved query optimization process in big data using ACO-GA algorithm and HDFS map reduce technique. Distrib Parallel Databases 1\u201318","DOI":"10.1007\/s10619-020-07285-z"},{"key":"3793_CR21","doi-asserted-by":"crossref","unstructured":"Ge W, Li X, Yuan C, Huang Y (2019) Correlation-aware partitioning for skewed range query optimization. World Wide Web 22(1):125\u2013151","DOI":"10.1007\/s11280-018-0547-4"},{"key":"3793_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu H, Xiao D, Eltabakh MY (2018) Adaptive correlation exploitation in big data query optimization. VLDB J 27(6):873\u2013898","DOI":"10.1007\/s00778-018-0515-8"},{"key":"3793_CR23","doi-asserted-by":"crossref","unstructured":"Jafarinejad M, Amini M (2018) Multi-join query optimization in bucket-based encrypted databases using an enhanced ant colony optimization algorithm. Distrib Parallel Databases 36(2):399\u2013441","DOI":"10.1007\/s10619-018-7220-x"},{"key":"3793_CR24","doi-asserted-by":"crossref","unstructured":"Michiardi P, Carra D, Migliorini S (2020) Cache-based multi-query optimization for data-intensive scalable computing frameworks. Inf Syst Front 1\u201317","DOI":"10.1007\/s10796-020-09995-2"},{"issue":"2","key":"3793_CR25","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s10723-018-9431-9","volume":"16","author":"R Sahal","year":"2018","unstructured":"Sahal R, Nihad M, Khafagy MH, Omara FA (2018) iHOME: index-based join query optimization for limited big data storage. J Grid Comput 16(2):345\u2013380","journal-title":"J Grid Comput"},{"issue":"2","key":"3793_CR26","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TBDATA.2017.2719054","volume":"4","author":"R Sellami","year":"2017","unstructured":"Sellami R, Defude B (2017) Complex queries optimization and evaluation over relational and NoSQL data stores in cloud environments. IEEE Trans Big Data 4(2):217\u2013230","journal-title":"IEEE Trans Big Data"},{"key":"3793_CR27","unstructured":"Sharma M, Singh G, Singh R (2018) Clinical decision support system query optimizer using hybrid firefly and controlled genetic algorithm. J King Saud Univ Comput Inf Sci"},{"key":"3793_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04397-1","author":"D Kleyko","year":"2019","unstructured":"Kleyko D, Rahimi A, Gayler RW, Osipov E (2019) Autoscaling bloom filter: controlling trade-off between true and false positives. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04397-1","journal-title":"Neural Comput Appl"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-03793-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-03793-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-03793-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:40:35Z","timestamp":1635169235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-03793-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":28,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["3793"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-03793-6","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,19]]},"assertion":[{"value":"1 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}