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            	{"first" : "M. Alireza",	"last" : "Hakamian"},
            	{"first" : "Denis",	"last" : "Zahariev"},
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            	{"first" : "Valeria",	"last" : "Cardellini"},
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            	{"first" : "Petr",	"last" : "Tuma"}
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            	{"first" : "Sebastian",	"last" : "Frank"},
            	{"first" : "M. Alireza",	"last" : "Hakamian"},
            	{"first" : "Denis",	"last" : "Zahariev"},
            	{"first" : "André",	"last" : "van Hoorn"}
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            	{"first" : "Marco",	"last" : "Vieira"},
            	{"first" : "Valeria",	"last" : "Cardellini"},
            	{"first" : "Antinisca Di",	"last" : "Marco"},
            	{"first" : "Petr",	"last" : "Tuma"}
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            "axiomatic","data-analytics","design","machine-learning","manufacturing","myown","specification"
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         "label" : "AD4ML: Axiomatic Design to Specify Machine Learning Solutions for Manufacturing",
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         "author": [ 
            "A. G. Villanueva Zacarias","R. Ghabri","P. Reimann"
         ],
         "authors": [
         	
            	{"first" : "A. G.",	"last" : "Villanueva Zacarias"},
            	{"first" : "R.",	"last" : "Ghabri"},
            	{"first" : "P.",	"last" : "Reimann"}
         ],
         "pages": "148-155","abstract": "Machine learning is increasingly adopted in manufacturing use cases, e.g., for fault detection in a production line. Each new use case requires developing its own machine learning (ML) solution. A ML solution integrates different software components to read, process, and analyze all use case data, as well as to finally generate the output that domain experts need for their decision-making. The process to design a system specification for a ML solution is not straight-forward. It entails two types of complexity: (1) The technical complexity of selecting combinations of ML algorithms and software components that suit a use case; (2) the organizational complexity of integrating different requirements from a multidisciplinary team of, e.g., domain experts, data scientists, and IT specialists. In this paper, we propose several adaptations to Axiomatic Design in order to design ML solution specifications that handle these complexities. We call this Axiomatic Design for Machine Learning (AD4ML). We apply AD4ML to specify a ML solution for a fault detection use case and discuss to what extent our approach conquers the above-mentioned complexities. We also discuss how AD4ML facilitates the agile design of ML solutions.",
         "doi" : "10.1109/IRI49571.2020.00029",
         
         "bibtexKey": "9191629"

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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/23824a9778eb687fae1a89272266bd129/hermann",         
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         "interHash" : "7f08b6efcfcf08ea26872619b6fd8f67",
         "label" : "SYBL: An Extensible Language for Controlling Elasticity in Cloud Applications",
         "user" : "hermann",
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         "date" : "2018-06-26 09:23:23",
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         "booktitle": "2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing",
         "year": "2013", 
         "url": "", 
         
         "author": [ 
            "G. Copil","D. Moldovan","H. L. Truong","S. Dustdar"
         ],
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            	{"first" : "G.",	"last" : "Copil"},
            	{"first" : "D.",	"last" : "Moldovan"},
            	{"first" : "H. L.",	"last" : "Truong"},
            	{"first" : "S.",	"last" : "Dustdar"}
         ],
         "pages": "112-119",
         "doi" : "10.1109/CCGrid.2013.42",
         
         "bibtexKey": "copil2013extensible"

      }
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      {
         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/20ebfbc4b77ffed3431bd7c5c0e16e345/isw-bibliothek",         
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            "CNC","Companion","Device","HMI","ISW","MTConnect","OPC","PLCopen","Specification","UA","xkt"
         ],
         
         "intraHash" : "0ebfbc4b77ffed3431bd7c5c0e16e345",
         "interHash" : "3e73e3209e9d4e4071311a1b8970c153",
         "label" : "OPC UA - eine Positionsbestimmung",
         "user" : "isw-bibliothek",
         "description" : "",
         "date" : "2016-03-03 09:53:12",
         "changeDate" : "2016-03-14 15:28:20",
         "count" : 1,
         "pub-type": "article",
         "journal": "Computer&Automation",
         "year": "2013", 
         "url": "http://www.computer-automation.de/steuerungsebene/bedienen-beobachten/artikel/97031/", 
         
         "author": [ 
            "Matthias Keinert"
         ],
         "authors": [
         	
            	{"first" : "Matthias",	"last" : "Keinert"}
         ],
         "number": "5-2013","pages": "46-49","abstract": "Mit der OPC UA Technologie steht ein industrieller Kommunikationsmechanismuszur Verfügung, der es erlaubt Systeme von der Steuerungs- bis zur Leit- undBetriebsebene miteinander zu vernetzen. OPC UA zeichnet sich durch integrierteSicherheitsmechanismen, Plattformunabhängigkeit und Skalierbarkeit aus. Um dieAustauschbarkeit von Produkten mit integrierter OPC UA Technologie weiter zuerhöhen, müssen entsprechende Informationsmodelle standardisiert werden. Sowürde beispielsweise der Einsatz einer einzigen HMI für CNC-Steuerungenverschiedener Hersteller zu einem wirklichen Mehrwert führen und die Akzeptanzund Verbreitung von OPC UA weiter fördern.",
         "__markedentry" : "[xtl:6]",
         
         "bibtexKey": "Keinert2013a"

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