{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:31:40Z","timestamp":1780090300139,"version":"3.54.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031304415","type":"print"},{"value":"9783031304422","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30442-2_22","type":"book-chapter","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T10:02:09Z","timestamp":1682589729000},"page":"291-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["High Performance Dataframes from\u00a0Parallel Processing Patterns"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3076-0011","authenticated-orcid":false,"given":"Niranda","family":"Perera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Supun","family":"Kamburugamuve","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chathura","family":"Widanage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vibhatha","family":"Abeykoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet","family":"Uyar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiying","family":"Shan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasara","family":"Maithree","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damitha","family":"Lenadora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thejaka Amila","family":"Kanewala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey","family":"Fox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"22_CR1","unstructured":"MPI: A Message-Passing Interface Standard Version 3.0 (2012). http:\/\/mpi-forum.org\/docs\/mpi-3.0\/mpi30-report.pdf. Technical Report"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Abeykoon, V., et al.: Streaming machine learning algorithms with big data systems. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5661\u20135666. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9006337"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Abeykoon, V., et al.: Hptmt parallel operators for high performance data science & data engineering. arXiv preprint arXiv:2108.06001 (2021)","DOI":"10.3389\/fdata.2021.756041"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Abeykoon, V., et al.: Data engineering for HPC with python. In: 2020 IEEE\/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC), pp. 13\u201321. IEEE (2020)","DOI":"10.1109\/PyHPC51966.2020.00007"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Babuji, Y.N., et al.: Parsl: scalable parallel scripting in python. In: IWSG (2018)","DOI":"10.1145\/3332186.3332231"},{"key":"22_CR6","unstructured":"CylonData: cylon (2021). https:\/\/github.com\/cylondata\/cylon"},{"key":"22_CR7","unstructured":"CylonData: cylon experiments (2021). https:\/\/github.com\/cylondata\/cylon_experiments"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Fox, G., et al.: Solving problems on concurrent processors, vol. 1: general techniques and regular problems. Comput. Phys. 3(1), 83\u201384 (1989)","DOI":"10.1063\/1.4822815"},{"key":"22_CR9","unstructured":"Gao, H., Sakharnykh, N.: Scaling joins to a thousand GPUs. In: 12th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS@ VLDB (2021)"},{"issue":"1","key":"22_CR10","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1177\/1094342017712976","volume":"32","author":"S Kamburugamuve","year":"2018","unstructured":"Kamburugamuve, S., Wickramasinghe, P., Ekanayake, S., Fox, G.C.: Anatomy of machine learning algorithm implementations in MPI, Spark, and Flink. Int. J. High Perform. Comput. Appl. 32(1), 61\u201373 (2018)","journal-title":"Int. J. High Perform. Comput. Appl."},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Kamburugamuve, S., et al.: Hptmt: operator-based architecture for scalable high-performance data-intensive frameworks. In: 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), pp. 228\u2013239. IEEE (2021)","DOI":"10.1109\/CLOUD53861.2021.00036"},{"issue":"10","key":"22_CR12","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1016\/0167-8191(93)90019-H","volume":"19","author":"X Li","year":"1993","unstructured":"Li, X., Lu, P., Schaeffer, J., Shillington, J., Wong, P.S., Shi, H.: On the versatility of parallel sorting by regular sampling. Parallel Comput. 19(10), 1079\u20131103 (1993)","journal-title":"Parallel Comput."},{"key":"22_CR13","unstructured":"Mattson, T., Sanders, B., Massingill, B.: Patterns for parallel programming (2004)"},{"issue":"9","key":"22_CR14","first-page":"1","volume":"14","author":"W McKinney","year":"2011","unstructured":"McKinney, W., et al.: pandas: a foundational python library for data analysis and statistics. Python High Perform. Sci. Comput. 14(9), 1\u20139 (2011)","journal-title":"Python High Perform. Sci. Comput."},{"key":"22_CR15","unstructured":"Modin: modin scalability issues (2021). https:\/\/github.com\/modin-project\/modin\/issues"},{"key":"22_CR16","unstructured":"Moritz, P., et al.: Ray: a distributed framework for emerging $$\\{$$AI$$\\}$$ applications. In: 13th $$\\{$$USENIX$$\\}$$ Symposium on Operating Systems Design and Implementation ($$\\{$$OSDI$$\\}$$ 18), pp. 561\u2013577 (2018)"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Perera, N., et al.: A fast, scalable, universal approach for distributed data reductions. In: International Workshop on Big Data Reduction, IEEE Big Data (2020)","DOI":"10.1109\/BigData50022.2020.9378124"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Petersohn, D., et al.: Towards scalable dataframe systems. arXiv preprint arXiv:2001.00888 (2020)","DOI":"10.14778\/3407790.3407807"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Rocklin, M.: Dask: parallel computation with blocked algorithms and task scheduling. In: Proceedings of the 14th Python in Science Conference, 130\u2013136. Citeseer (2015)","DOI":"10.25080\/Majora-7b98e3ed-013"},{"issue":"8","key":"22_CR20","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/79173.79181","volume":"33","author":"LG Valiant","year":"1990","unstructured":"Valiant, L.G.: A bridging model for parallel computation. Commun. ACM 33(8), 103\u2013111 (1990)","journal-title":"Commun. ACM"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Wickramasinghe, P., et al.: Twister2: tset high-performance iterative dataflow. In: 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD &IS), pp. 55\u201360. IEEE (2019)","DOI":"10.1109\/HPBDIS.2019.8735495"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Widanage, C., et al.: High performance data engineering everywhere. In: 2020 IEEE International Conference on Smart Data Services (SMDS), pp. 122\u2013132. IEEE (2020)","DOI":"10.1109\/SMDS49396.2020.00022"},{"issue":"11","key":"22_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2934664","volume":"59","author":"M Zaharia","year":"2016","unstructured":"Zaharia, M., et al.: apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56\u201365 (2016)","journal-title":"Commun. ACM"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Kamil, A., Driscoll, M.B., Shan, H., Yelick, K.: UPC++: a PGAS extension for c++. In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, pp. 1105\u20131114. IEEE (2014)","DOI":"10.1109\/IPDPS.2014.115"}],"container-title":["Lecture Notes in Computer Science","Parallel Processing and Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30442-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T10:04:22Z","timestamp":1682589862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30442-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031304415","9783031304422"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30442-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"28 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPAM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Processing and Applied Mathematics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gdansk","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppam2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppam.edu.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"132","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}