Ubiquitous Data

@inproceedings{Hotho2010UbiquitousD,
  title={Ubiquitous Data},
  author={Andreas Hotho and Rasmus Ulslev Pedersen and Michael Wurst},
  booktitle={Ubiquitous Knowledge Discovery},
  year={2010}
}
Ubiquitous knowledge discovery systems must be captured from many different perspectives. In earlier chapters, aspects like machine learning, underlying network technologies etc. were described. An essential component, which we shall discuss now, is still missing: Ubiquitous Data. While data themselves are a central part of the knowledge discovery process, in a ubiquitous setting new challenges arise. In this context, the emergence of data itself plays a large role, therefore we label this part… 
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References

SHOWING 1-10 OF 28 REFERENCES
Distributed feature extraction in a p2p setting - a case study
Mobility, Data Mining and Privacy - Geographic Knowledge Discovery
TLDR
This book tightly integrates and relates their findings in 13 chapters covering all related subjects, including the concepts of movement data and knowledge discovery from movement data; privacy-aware geographic knowledge discovery; wireless network and next-generation mobile technologies; trajectory data models, systems and warehouses; privacy and security aspects of technologies and related regulations.
Learning from Data Streams
TLDR
Learning from large datasets may be more effective when using algorithms that place greater emphasis on bias management, and algorithms that process data streams deliver approximate solutions, providing a fast answer using few memory resources.
Aspect-Based Tagging for Collaborative Media Organization
TLDR
This paper allows tags to be grouped into aspects, and shows that introducing aspects does not only help the user to manage large numbers of tags, but also facilitates data mining in various ways.
Network properties of folksonomies
TLDR
It is shown that simple statistical indicators unambiguously spot non-social behavior such as spam, and is introduced a network of tag co-occurrence to investigate some of its statistical properties.
TinyDB: an acquisitional query processing system for sensor networks
TLDR
This work evaluates issues in the context of TinyDB, a distributed query processor for smart sensor devices, and shows how acquisitional techniques can provide significant reductions in power consumption on the authors' sensor devices.
Semantic Web Mining: State of the art and future directions
Principles of Data Mining
TLDR
This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.
Conceptual Structures: Knowledge Architectures for Smart Applications, 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, July 22-27, 2007, Proceedings
TLDR
Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise, an Inferential Approach to the Generation of Referring Expressions, and more.
Mining Association Rules in Folksonomies
TLDR
How association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics is discussed.
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