{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:47:31Z","timestamp":1740170851378,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T00:00:00Z","timestamp":1540252800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T00:00:00Z","timestamp":1540252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Adapt Syst Model"],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A self-adaptive system adapts itself to changes in a dynamic environment. The core in self-adaptive systems is making an adaptation decision. The current practice focuses on a single layer of decision making using either a local knowledge base or a shared knowledge base shared by multiple units through a network. While the use of a local knowledge base is efficient, it suffers from its limited maturity. A shared knowledge base can address the maturity problem, but it is inefficient in adaptation due to communication overheads. In this work, we present a three-phase decision making approach for self-adaptive systems to improve precision while being competitive for efficiency. The approach consists of three phases for making a decision. The first phase uses the local knowledge base of the self-adaptive unit to identify an object. If the object cannot be identified locally, the unit sends a request to shared knowledge bases through web services in the second and third phases. The approach makes use of B-kNN for object identification and web services for accessing shared knowledge bases. We conducted quantitative validation in terms of accuracy, precision, recall, and F-measure using a set of scenarios. The results show that 99.58% of accuracy, 94.01% of recall, 94.04% of precision, and 94.01% of F-measure can be achieved. We also conducted comparative analysis by comparing the presented approach with the traditional approach and the cloud-based approach. The results show that the presented approach improves 45% in object identification with an increase of 0.66 s over the traditional approach and the same performance in object identification with a decrease of 0.95 s over the cloud-based approach.<\/jats:p>","DOI":"10.1186\/s40294-018-0059-1","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T20:45:40Z","timestamp":1540327540000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A three-phase decision making approach for self-adaptive systems using web services"],"prefix":"10.1186","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4501-8821","authenticated-orcid":false,"given":"Dhrgam","family":"AL-Kafaf","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dae-Kyoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lunjin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,23]]},"reference":[{"issue":"9","key":"59_CR1","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1088\/0954-3899\/25\/9\/308","volume":"25","author":"M Bleicher","year":"1999","unstructured":"Bleicher M, Zabrodin E, Spieles C, Bass SA, Ernst C, Soff S, Bravina L, Belkacem M, Weber H, St\u00f6cker H, Greiner W (1999) Relativistic hadron-hadron collisions in the ultra-relativistic quantum molecular dynamics model. 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