{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:54:08Z","timestamp":1768474448100,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.<\/jats:p>","DOI":"10.3390\/a14050149","type":"journal-article","created":{"date-parts":[[2021,5,9]],"date-time":"2021-05-09T21:32:46Z","timestamp":1620595966000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Query Rewriting for Incremental Continuous Query Evaluation in HIFUN"],"prefix":"10.3390","volume":"14","author":[{"given":"Petros","family":"Zervoudakis","sequence":"first","affiliation":[{"name":"Institute of Computer Science, FORTH-ICS, 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9917-4486","authenticated-orcid":false,"given":"Haridimos","family":"Kondylakis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, FORTH-ICS, 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Spyratos","sequence":"additional","affiliation":[{"name":"Laboratoire de Recherche en Informatique, Universit\u00e9 de Paris-Sud, 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0863-8266","authenticated-orcid":false,"given":"Dimitris","family":"Plexousakis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, FORTH-ICS, 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.comcom.2020.10.001","article-title":"FoT-Stream: A Fog platform for data stream analytics in IoT","volume":"164","author":"Mello","year":"2020","journal-title":"Comput. 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