Ubiquitous Data

  title={Ubiquitous Data},
  author={Andreas Hotho and Rasmus Ulslev Pedersen and Michael Wurst},
  booktitle={Ubiquitous Knowledge Discovery},
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… 
Learning Recurring Concepts from Data Streams in Ubiquitous Environments
This PhD thesis addresses the aforementioned open issues, focusing on learning anytime, anywhere classification models from data streams in ubiquitous environments, where the underlying concepts may change over time, with special emphasis on recurring concepts.
Unlocking Value from Ubiquitous Data
Recent examples from work in the research team on ubiquitous data analytics are presented and a discussion on key questions relating methodologies, tools and frameworks to improve ubiquitous data team effectiveness as well as the potential goals for a ubiquitous data process methodology are opened up.
Context-Aware Collaborative Data Stream Mining in Ubiquitous Devices
This paper motivates and describes a novel Context-aware Collaborative data stream mining system CC-Stream that allows intelligent mining and classification of time-changing data streams on-board ubiquitous devices.
Data stream mining in ubiquitous environments: state‐of‐the‐art and current directions
This article reviews the state‐of‐the‐art techniques in mining data streams for mobile and ubiquitous environments, and identifies the key characteristics of these algorithms and present illustrative applications.
A generic platform for ubiquitous and subjective data
This paper provides an overview of the architecture of Xively, providing an extendable concept of data which allows to enrich existing data points with any kind of additional information, and describes two sensing applications namely AirProbe and WideNoise that were implemented for the platform.
Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media
  • M. Atzmüller
  • Computer Science
    Solving Large Scale Learning Tasks
  • 2016
Recent work on description-oriented community detection, spatio-semantic analysis using local exceptionality detection, and class association rule mining for activity recognition is summarized.
Tag Recommendations for SensorFolkSonomies
It is shown that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but sensory data and impressions, and the scenario requires efficient recommender algorithms that are able to run on a mobile device alone, since Internet connectivity is not always available.
Meeting the Needs of Sophisticated Applications with Ubiquitous Computing Systems
  • D. Adamopoulos
  • Computer Science
    Fourth International Conference on Networking and Services (icns 2008)
  • 2008
The current status of ubiquitous computing is reported, by presenting technologies and applications that are used today, in addition to describing research projects and prototypes that have been developed until today.
Pocket Data Mining


Distributed feature extraction in a p2p setting - a case study
Mobility, Data Mining and Privacy - Geographic Knowledge Discovery
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
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
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
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
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
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
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
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.