Data-driven Business as a Cognitive Computing Challenge: Unterschied zwischen den Versionen
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evidence-based hypotheses”. We expect that integrating this approach with (big) data analytics will enable humans to use all their cognitive capability to solve significant more complex problems by leveraging | evidence-based hypotheses”. We expect that integrating this approach with (big) data analytics will enable humans to use all their cognitive capability to solve significant more complex problems by leveraging | ||
enormous amounts of data. | enormous amounts of data. | ||
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|Beschreibung EN=Today, big data is primarily viewed as a quantitative phenomenon along the dimensions of volume, velocity and variety. Current research and development in the area focus on advances in data management | |Beschreibung EN=Today, big data is primarily viewed as a quantitative phenomenon along the dimensions of volume, velocity and variety. Current research and development in the area focus on advances in data management | ||
infrastructure and computational performance. Current data-driven business models mostly capitalize on previously untapped data sources. This focus on enabling technologies and early-adopting exploitation is about to change: The veracity dimension of big data emphasizes understanding and trust of human decision makers, who require actionable knowledge for data-driven business. Recent studies suggest big data analytics would hugely benefit from tagging and (semantic) enriching, involving humans in the loop. Promising application domains for big data analytics include entertainment, social media, consumer images and | infrastructure and computational performance. Current data-driven business models mostly capitalize on previously untapped data sources. This focus on enabling technologies and early-adopting exploitation is about to change: The veracity dimension of big data emphasizes understanding and trust of human decision makers, who require actionable knowledge for data-driven business. Recent studies suggest big data analytics would hugely benefit from tagging and (semantic) enriching, involving humans in the loop. Promising application domains for big data analytics include entertainment, social media, consumer images and | ||
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evidence-based hypotheses”. We expect that integrating this approach with (big) data analytics will enable humans to use all their cognitive capability to solve significant more complex problems by leveraging | evidence-based hypotheses”. We expect that integrating this approach with (big) data analytics will enable humans to use all their cognitive capability to solve significant more complex problems by leveraging | ||
enormous amounts of data. | enormous amounts of data. | ||
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|Veranstaltungsart=Kolloquium Angewandte Informatik | |Veranstaltungsart=Kolloquium Angewandte Informatik | ||
|Start=2015/02/03 11:30:00 | |Start=2015/02/03 11:30:00 | ||
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|Vortragender=Prof. Dr. Stefanie Lindstädt | |Vortragender=Prof. Dr. Stefanie Lindstädt | ||
|Eingeladen durch=Rudi Studer | |Eingeladen durch=Rudi Studer | ||
+ | |PDF=3 2 Lindstaedt.pdf | ||
|Forschungsgruppe=Wissensmanagement | |Forschungsgruppe=Wissensmanagement | ||
|In News anzeigen=True | |In News anzeigen=True | ||
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Version vom 28. Januar 2015, 09:04 Uhr
Data-driven Business as a Cognitive Computing Challenge
Veranstaltungsart:
Kolloquium Angewandte Informatik
Today, big data is primarily viewed as a quantitative phenomenon along the dimensions of volume, velocity and variety. Current research and development in the area focus on advances in data management
infrastructure and computational performance. Current data-driven business models mostly capitalize on previously untapped data sources. This focus on enabling technologies and early-adopting exploitation is about to change: The veracity dimension of big data emphasizes understanding and trust of human decision makers, who require actionable knowledge for data-driven business. Recent studies suggest big data analytics would hugely benefit from tagging and (semantic) enriching, involving humans in the loop. Promising application domains for big data analytics include entertainment, social media, consumer images and
medical data, as well as Open Science. Sustainable success in the future data-driven business will be based on balancing the interplay between data, analytics, (human) domain knowledge, human cognition and social interaction, combining data-driven with human-centered approaches. Within the Know-Center, we therefore approach data-driven business as a cognitive computing challenge. Cognitive computing aims at creating systems that “interact naturally with humans, learn from their experiences and generate and evaluate
evidence-based hypotheses”. We expect that integrating this approach with (big) data analytics will enable humans to use all their cognitive capability to solve significant more complex problems by leveraging
enormous amounts of data.
(Prof. Dr. Stefanie Lindstädt)
Start: 03. Februar 2015 um 11:30
Ende: 03. Februar 2015 um 12:30
Im Gebäude 11.40, Raum: 231
Veranstaltung vormerken: (iCal)
Veranstalter: Forschungsgruppe(n) Wissensmanagement
Information: Media:3 2 Lindstaedt.pdf