{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:18:37Z","timestamp":1742998717043,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030860431"},{"type":"electronic","value":"9783030860448"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86044-8_20","type":"book-chapter","created":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T08:03:37Z","timestamp":1629878617000},"page":"295-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Software Architectures for Edge Analytics: A Survey"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8107-6204","authenticated-orcid":false,"given":"Marie","family":"Platenius-Mohr","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadil","family":"Abukwaik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Schlake","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Vach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,26]]},"reference":[{"key":"20_CR1","unstructured":"Management of alarm systems for the process industries, ansi\/isa-18.2-2016 (2016)"},{"key":"20_CR2","unstructured":"Amazon Web Services: AWS IoT Greengrass (2021). https:\/\/aws.amazon.com\/greengrass\/"},{"issue":"10","key":"20_CR3","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MC.2017.3641638","volume":"50","author":"G Ananthanarayanan","year":"2017","unstructured":"Ananthanarayanan, G., et al.: Real-time video analytics: the killer app for edge computing. Computer 50(10), 58\u201367 (2017)","journal-title":"Computer"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Bosch, J., Olsson, H.H., Crnkovic, I.: Engineering AI systems: a research agenda. In: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, pp. 1\u201319. IGI Global (2021)","DOI":"10.4018\/978-1-7998-5101-1.ch001"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Cao, H., Wachowicz, M., Cha, S.: Developing an edge computing platform for real-time descriptive analytics. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4546\u20134554. IEEE (2017)","DOI":"10.1109\/BigData.2017.8258497"},{"issue":"9","key":"20_CR6","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.3390\/s19092047","volume":"19","author":"YY Chen","year":"2019","unstructured":"Chen, Y.Y., Lin, Y.H., Kung, C.C., Chung, M.H., Yen, I., et al.: Design and implementation of cloud analytics-assisted smart power meters considering advanced artificial intelligence as edge analytics in demand-side management for smart homes. Sensors 19(9), 2047 (2019)","journal-title":"Sensors"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, B., Papageorgiou, A., Bauer, M.: Geelytics: enabling on-demand edge analytics over scoped data sources. In: International Congress on Big Data (BigData Congress), pp. 101\u2013108. IEEE (2016)","DOI":"10.1109\/BigDataCongress.2016.21"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Cheng, B., Papageorgiou, A., Cirillo, F., Kovacs, E.: Geelytics: geo-distributed edge analytics for large scale IoT systems based on dynamic topology. In: 2nd World Forum on Internet of Things (WF-IoT), pp. 565\u2013570. IEEE (2015)","DOI":"10.1109\/WF-IoT.2015.7389116"},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-319-57639-8_6","volume-title":"Fog Computing in the Internet of Things","author":"A Chowdhery","year":"2018","unstructured":"Chowdhery, A., Levorato, M., Burago, I., Baidya, S.: Urban IoT edge analytics. In: Rahmani, A.M., Liljeberg, P., Preden, J.-S., Jantsch, A. (eds.) Fog Computing in the Internet of Things, pp. 101\u2013120. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-57639-8_6"},{"issue":"1","key":"20_CR10","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MVT.2018.2883777","volume":"14","author":"A Ferdowsi","year":"2019","unstructured":"Ferdowsi, A., Challita, U., Saad, W.: Deep learning for reliable mobile edge analytics in intelligent transportation systems: an overview. IEEE Veh. Technol. Mag. 14(1), 62\u201370 (2019)","journal-title":"IEEE Veh. Technol. Mag."},{"key":"20_CR11","unstructured":"Gartner: Gartner predicts the future of AI technologies (2019). https:\/\/www.gartner.com\/smarterwithgartner\/gartner-predicts-the-future-of-ai-technologies\/"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.jlp.2017.09.001","volume":"50","author":"P Goel","year":"2017","unstructured":"Goel, P., Datta, A., Mannan, M.S.: Industrial alarm systems: challenges and opportunities. J. Loss Prev. Process Ind. 50, 23\u201336 (2017)","journal-title":"J. Loss Prev. Process Ind."},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Gruener, S., Koziolek, H., Rueckert, J.: Towards resilient IoT messaging: an experience report analyzing MQTT brokers. In: 2021 IEEE International Conference on Software Architecture (ICSA). IEEE (2021)","DOI":"10.1109\/ICSA51549.2021.00015"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Harth, N., Anagnostopoulos, C.: Quality-aware aggregation & predictive analytics at the edge. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 17\u201326. IEEE (2017)","DOI":"10.1109\/BigData.2017.8257907"},{"issue":"2","key":"20_CR15","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s12530-017-9190-z","volume":"9","author":"N Harth","year":"2018","unstructured":"Harth, N., Anagnostopoulos, C., Pezaros, D.: Predictive intelligence to the edge: impact on edge analytics. Evol. Syst. 9(2), 95\u2013118 (2018)","journal-title":"Evol. Syst."},{"key":"20_CR16","unstructured":"Internet of Business, Cambridge Innovation Institute: Cisco and IBM team up to drive edge analytics for IoT (2021). https:\/\/internetofbusiness.com\/cisco-ibm-team-drive-edge-analytics\/"},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.future.2019.02.050","volume":"97","author":"WZ Khan","year":"2019","unstructured":"Khan, W.Z., Ahmed, E., Hakak, S., Yaqoob, I., Ahmed, A.: Edge computing: a survey. Futur. Gener. Comput. Syst. 97, 219\u2013235 (2019)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Lujic, I., De Maio, V., Brandic, I.: Efficient edge storage management based on near real-time forecasts. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp. 21\u201330. IEEE (2017)","DOI":"10.1109\/ICFEC.2017.9"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Lujic, I., De Maio, V., Brandic, I.: Adaptive recovery of incomplete datasets for edge analytics. In: 2nd International Conference on Fog and Edge Computing (ICFEC). pp. 1\u201310. IEEE (2018)","DOI":"10.1109\/CFEC.2018.8358726"},{"key":"20_CR20","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-1-4842-4106-6_7","volume-title":"Cognitive Computing Recipes","author":"A Masood","year":"2019","unstructured":"Masood, A., Hashmi, A.: AIOps: predictive analytics & machine learning in operations. In: Cognitive Computing Recipes, pp. 359\u2013382. Apress, Berkeley, CA (2019). https:\/\/doi.org\/10.1007\/978-1-4842-4106-6_7"},{"key":"20_CR21","unstructured":"Microsoft: Azure IoT Edge (2021). https:\/\/azure.microsoft.com\/en-us\/services\/iot-edge\/"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Muccini, H., Vaidhyanathan, K.: Software architecture for ml-based systems: what exists and what lies ahead. arXiv preprint arXiv:2103.07950 (2021)","DOI":"10.1109\/WAIN52551.2021.00026"},{"key":"20_CR23","unstructured":"Ng, A.: Why AI Is the New Electricity (2017). https:\/\/www.gsb.stanford.edu\/insights\/andrew-ng-why-ai-new-electricity"},{"issue":"10","key":"20_CR24","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1515\/auto-2015-0060","volume":"63","author":"O Niggemann","year":"2015","unstructured":"Niggemann, O., Frey, C.: Data-driven anomaly detection in cyber-physical production systems. at-Automatisierungstechnik 63(10), 821\u2013832 (2015)","journal-title":"at-Automatisierungstechnik"},{"key":"20_CR25","unstructured":"Perino, J., Littlefield, M., Murugesan, V.: Living on the edge - edge computing in the new OT ecosystem. LNS Research (2020). https:\/\/resource.stratus.com\/whitepaper\/edge-computing-in-the-new-ot-ecosystem\/"},{"issue":"1","key":"20_CR26","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2017.9","volume":"50","author":"M Satyanarayanan","year":"2017","unstructured":"Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30\u201339 (2017)","journal-title":"Computer"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Tamburri, D.A.: Sustainable MLOps: trends and challenges. In: 22nd Int. Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 17\u201323. IEEE (2020)","DOI":"10.1109\/SYNASC51798.2020.00015"},{"issue":"12","key":"20_CR28","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.3390\/s19122733","volume":"19","author":"A Ukil","year":"2019","unstructured":"Ukil, A., Jara, A.J., Marin, L.: Data-driven automated cardiac health management with robust edge analytics and de-risking. Sensors 19(12), 2733 (2019)","journal-title":"Sensors"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Wen, Z., Bhatotia, P., Chen, R., Lee, M., et al.: ApproxIoT: approximate analytics for edge computing. In: 2018 IEEE 38th Int. Conf. on Distributed Computing Systems (ICDCS), pp. 411\u2013421. IEEE (2018)","DOI":"10.1109\/ICDCS.2018.00048"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Xu, X., Huang, S., Feagan, L., Chen, Y., Qiu, Y., Wang, Y.: EAaaS: edge analytics as a service. In: International Conference on Web Services (ICWS 2017), pp. 349\u2013356. IEEE (2017)","DOI":"10.1109\/ICWS.2017.130"},{"key":"20_CR31","doi-asserted-by":"publisher","first-page":"6900","DOI":"10.1109\/ACCESS.2017.2778504","volume":"6","author":"W Yu","year":"2017","unstructured":"Yu, W., et al.: A survey on the edge computing for the internet of things. IEEE Access 6, 6900\u20136919 (2017)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Software Architecture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86044-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T08:09:26Z","timestamp":1629878966000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86044-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030860431","9783030860448"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86044-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Software Architecture","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecsa2021","order":10,"name":"conference_id","label":"Conference ID","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":"68","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":"16","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":"5","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":"24% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}