{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T21:10:37Z","timestamp":1776373837127,"version":"3.51.2"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Marketing science embraces a wider variety of data types and measurement tools necessary for strategy, research, and applied decision making. Managing the marketing data generated by internet of things (IoT) sensors and actuators is one of the biggest challenges faced by marketing managers when deploying an IoT system. This short note shows how traditional cloud-based IoT systems are challenged by the large scale, heterogeneity, and high latency witnessed in some cloud ecosystems. It introduces researchers to one recent breakthrough, fog computing, an emerging concept that decentralizes applications, strategies, and data analytics into the network itself using a distributed and federated computing model. It transforms centralized cloud to distributed fog by bringing storage and computation closer to the user end. Fog computing is considered a novel marketplace phenomenon which can support AI and management strategies, especially for the design of \u201csmart marketing\u201d.<\/jats:p>","DOI":"10.3389\/frai.2023.1242574","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T09:42:05Z","timestamp":1696412525000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Fog computing: a platform for big-data marketing analytics"],"prefix":"10.3389","volume":"6","author":[{"given":"Jacob","family":"Hornik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matti","family":"Rachamim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergei","family":"Graguer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/978-981-15-2414-1_57","article-title":"\u201cEnergy-efficient resource scheduling in fog computing using SDN framework,\u201d","volume-title":"Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019","author":"Ahmad","year":"2020"},{"key":"B2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/bdcc2020010","article-title":"Fog computing and the internet of things: a review","volume":"2","author":"Atlam","year":"2018","journal-title":"Big Data Cogn. 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