Stage-oe-small.jpg

Data-driven Business as a Cognitive Computing Challenge: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
(Die Seite wurde neu angelegt: „{{Veranstaltung |Titel DE=Data-driven Business as a Cognitive Computing Challenge |Titel EN=Data-driven Business as a Cognitive Computing Challenge |Beschreibung …“)
 
K (Textersetzung - „Forschungsgruppe=Wissensmanagement“ durch „Forschungsgruppe=Web Science und Wissensmanagement“)
 
(Eine dazwischenliegende Version von einem anderen Benutzer wird nicht angezeigt)
Zeile 7: Zeile 7:
 
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.
 
 
|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  
Zeile 13: Zeile 12:
 
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.
 
 
|Veranstaltungsart=Kolloquium Angewandte Informatik
 
|Veranstaltungsart=Kolloquium Angewandte Informatik
 
|Start=2015/02/03 11:30:00
 
|Start=2015/02/03 11:30:00
Zeile 21: Zeile 19:
 
|Vortragender=Prof. Dr. Stefanie Lindstädt
 
|Vortragender=Prof. Dr. Stefanie Lindstädt
 
|Eingeladen durch=Rudi Studer
 
|Eingeladen durch=Rudi Studer
|Forschungsgruppe=Wissensmanagement
+
|PDF=3 2 Lindstaedt.pdf
 +
|Forschungsgruppe=Web Science und Wissensmanagement
 
|In News anzeigen=True
 
|In News anzeigen=True
 
}}
 
}}

Aktuelle Version vom 27. November 2015, 21:44 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) Web Science und Wissensmanagement
Information: Media:3 2 Lindstaedt.pdf