Titelaufnahme

Titel
Technical, social and economic implications of Machine Learning in Iot / von Aleksandar Bogoevski
VerfasserBogoevski, Aleksandar
Begutachter / BegutachterinKöszegi, Sabine Theresia
ErschienenWien, 2017
Umfang69 Seiten
HochschulschriftTechnische Universität Wien, Masterarbeit, 2017
Anmerkung
Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprueft
SpracheEnglisch
DokumenttypMasterarbeit
Schlagwörter (DE)cyber physical systems / internet of things / IoT driven business models / business model innovation / IoT state of the art technologies
Schlagwörter (EN)cyber physical systems / internet of things / IoT driven business models / business model innovation / IoT state of the art technologies
URNurn:nbn:at:at-ubtuw:1-101356 Persistent Identifier (URN)
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
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Technical, social and economic implications of Machine Learning in Iot [1.12 mb]
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Zusammenfassung (Englisch)

'Data is the new oil, it's valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, or chemicals to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.' Clive Humby, Mathematician and architect of Tesco's Clubcard, 2006 In today's world data is gathered almost everywhere and for every purpose, especially as more and more devices are connected, which act as a data source in different fields. But what to do with this data? What benefits can result of gaining data? And what new possibilities are coming up driven by digitalization? How important is analytics in the Internet of Things? This thesis aims to analyze the importance of analytics in the Internet of Things when it comes to creating new business models in the industrial environment and provide a state - of - the - art review on what has been done so far in this domain.^ ^Therefore, the value chain of IoT is analyzed where different factors like the amount of data, the processing capabilities and the variety of data play an important role when it comes to creating value out of gained data from all different types of sources deployed in the field. New technologies like predictive analytics, edge computing and different access technologies are unleashing tremendous possibilities for enterprises of all sizes to change industries they are acting in and even disrupt these industries just by gaining new insights in customer behavior, better utilizing their assets or by introducing new business models to the world which are enabled through the Internet of Things and especially through analytics. It is all about gaining value out of collected data in order to remain competitive and to be able to sustain over time.^ Businesses of all sizes should analyze their possibilities and benefits in deploying different types of analytics and determine their possibilities in deploying new business models to meet expectations rising out of the new society where possessing goods is not that much of importance anymore, but therefore even more important to provide exactly the desired service at the right time at the right quality level in order to make sure that customer expectations are met and bind customers even closer to a business than it has been possible ever before.