Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2017

Ginthör Robert, Lamb Reinhold, Koinegg Johann

Green Big Data - der Rohstoff Daten in der Energie- und Abfallwirtschaft

Green Tech Cluster GmbH, 2017

Buch
Daten stellen den Rohstoff und die Basis für viele Unternehmen und deren künftigen wirtschaftlichen Erfolg in der Industrie dar. Diese Radar-Ausgabe knüpft inhaltlich an die veröffentlichten Radar-Ausgaben „Dienstleistungsinnovationen“ und „Digitalisierte Maschinen und Anlagen“ an und beleuchtet die technischen Möglichkeiten und zukünftigen Entwicklungen von Data-driven Business im Kontext der Green Tech Industries. Basierend auf der fortschreitenden Digitalisierung nimmt das Angebotan strukturierten und unstrukturierten Daten in den unterschiedlichen Bereichen der Wirtschaft rasant zu. In diesem Kontext gilt es sowohl interne als auch externe Daten unterschiedlichen Ursprungs zentral zu erfassen, zu validieren, miteinander zu kombinieren, auszuwerten sowie daraus neue Erkenntnisse und Anwendungen für ein Data DrivenBusiness zu generieren.
2017

Meixner Britta, Kaiser Rene, Jäger Joscha, Ooi Wei Tsang, Kosch Harald

"INTERACTIVE MEDIA: TECHNOLOGY AND EXPERIENCE" Springer Multimedia Tools and Applications (MTAP) Journal

Springer Multimedia Tools and Applications (MTAP), Springer, Springer US, 2017

Buch
(journal special issue)
2017

Lindstaedt Stefanie , Czech Paul, Fessl Angela

Theory of Knowledge Management

A Lifecycle Approach to Knowledge Excellence in the Biopharmaceutical Industry, Nuala Calnan, Martin J Lipa, Paige E. Kane, Jose C. Menezes, CRC Press, 2017

Buch
A Lifecycle Approach to Knowledge Excellence various industries and use cases. Through their cognitive computing-based approach, which combines the strength of man and the machine, they are setting standards within both the local and the international research community. With their expertise in the field of knowledge management they are describing the basic approaches in this chapter.
2017

Breitfuß Gert, Kaiser Rene, Kern Roman, Kowald Dominik, Lex Elisabeth, Pammer-Schindler Viktoria, Veas Eduardo Enrique

i-Know Workshops 2017

CEUR Workshop Proceedings for i-know 2017 conference, CEUR , CEUR, Graz, Austria, 2017

Buch
Proceedings of the Workshop Papers of i-Know 2017, co-located with International Conference on Knowledge Technologies and Data-Driven Business 2017 (i-Know 2017), Graz, Austria, October 11-12, 2017.
2017

Kowald Dominik

Modeling Activation Processes in Human Memory for Tag Recommendations: Using Models from Human Memory Theory to Implement Recommender Systems for Social Tagging and Microblogging Environment

Suedwestdeutscher Verlag für Hochschulschriften, TU Graz, Suedwestdeutscher Verlag für Hochschulschrifte, Graz, 2017

Buch
Social tagging systems enable users to collaboratively assign freely chosen keywords(i.e., tags) to resources (e.g., Web links). In order to support users in finding descrip-tive tags, tag recommendation algorithms have been proposed. One issue of currentstate-of-the-art tag recommendation algorithms is that they are often designed ina purely data-driven way and thus, lack a thorough understanding of the cognitiveprocesses that play a role when people assign tags to resources. A prominent exam-ple is the activation equation of the cognitive architecture ACT-R, which formalizesactivation processes in human memory to determine if a specific memory unit (e.g.,a word or tag) will be needed in a specific context. It is the aim of this thesis toinvestigate if a cognitive-inspired approach, which models activation processes inhuman memory, can improve tag recommendations.For this, the relation between activation processes in human memory and usagepractices of tags is studied, which reveals that (i) past usage frequency, (ii) recency,and (iii) semantic context cues are important factors when people reuse tags. Basedon this, a cognitive-inspired tag recommendation approach termed BLLAC+MPrisdeveloped based on the activation equation of ACT-R. An extensive evaluation usingsix real-world folksonomy datasets shows that BLLAC+MProutperforms currentstate-of-the-art tag recommendation algorithms with respect to various evaluationmetrics. Finally, BLLAC+MPris utilized for hashtag recommendations in Twitter todemonstrate its generalizability in related areas of tag-based recommender systems.The findings of this thesis demonstrate that activation processes in human memorycan be utilized to improve not only social tag recommendations but also hashtagrecommendations. This opens up a number of possible research strands for futurework, such as the design of cognitive-inspired resource recommender systems
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