MUSE 2014 - 5th International Workshop on Mining Ubiquitous and Social Environments (MUSE)

The emergence of ubiquitous computing has started to create new environments consisting of small, heterogeneous, and distributed devices that foster the social interaction of users in several dimensions. Similarly, the upcoming social web also integrates the user interactions in social networking environments.
In typical ubiquitous settings, the mining system can be implemented inside the small devices and sometimes on central servers, for real-time applications, similar to common mining approaches. However, the characteristics of ubiquitous and social mining in general are quite different from the current mainstream data mining and machine learning. Unlike in traditional data mining scenarios, data does not emerge from a small number of (heterogeneous) data sources, but potentially from hundreds to millions of different sources. Often there is only minimal coordination and thus these sources can overlap or diverge in many possible ways. Steps into this new and exciting application area are the analysis of this new data, the adaptation of well known data mining and machine learning algorithms and finally the development of new algorithms.
Mining big data in ubiquitous and social environments is an emerging area of research focusing on advanced systems for data mining in such distributed and network-organized systems. Therefore, for this workshop, we aim to attract researchers from all over the world working in the field of data mining and machine learning with a special focus on analyzing big data in ubiquitous and social environments.
The goal of this workshop is to promote an interdisciplinary forum for researchers working in the fields of ubiquitous computing, mobile sensing, social web, Web 2.0, and social networks which are interested in utilizing data mining in a ubiquitous setting. The workshop seeks for contributions adopting state-of-the-art mining algorithms on ubiquitous social data. Papers combining aspects of the two fields are especially welcome. In short, we want to accelerate the process of identifying the power of advanced data mining operating on data collected in ubiquitous and social environments, as well as the process of advancing data mining through lessons learned in analyzing these new data.
Topics of Interest
The topics of the workshop are split roughly into four areas which include, but are not limited to the following topics:
Ubiquitous Mining:
Analysis of data from sensors and mobile devices
Resource-aware algorithms for distributed mining
Scalable and distributed classification, prediction, and clustering algorithms
Activity recognition
Mining continuous streams and ubiquitous data
Online methods for mining temporal, spatial and spatio-temporal data
Combining data from different sources
Mining Social Data:
Analysis of social networks and social media
Mining techniques for social networks and social media
Algorithms for inferring semantics and meaning from social data
Privacy and security issues in social data
How social data can be used to mine and create collective intelligence
Individual and group behavior in social media and social networks
Social networks for the collaboration of large communities
Modeling social behavior
Novel techniques for mining big data from social media
Dynamics and evolution patterns of social networks
Ubiquitous and Social Mining
Personalization and recommendation
User models and predicting user behavior
User profiling in ubiquitous and social environments
Network analysis of social systems
Discovering social structures and communities
Mobility mining
Link prediction
Analysis of data from crowd-sourcing approaches
Applications:
Discovering misuse and fraud
Usage and presentation interfaces for mining and data collection
Analysis of social and ubiquitous games
Privacy challenges in ubiquitous and social applications
Recommenders in ubiquitous and social environments
Applications of any of the above methods and technologies
We also encourage submissions which relate research results from other areas to the workshop topics.
Springer Book: As in the previous years, it is planned to publish revised selected papers as a volume in the Springer LNCS/LNAI series.
Workshop Organizers
Martin Atzmueller, Knowledge and Data Engineering Group, Kassel University, Germany
( atzmueller-AT-cs.uni-kassel.de )
Christoph Scholz, Knowledge and Data Engineering Group, Kassel University, Germany
( scholz-AT-cs.uni-kassel.de )
Submission and Proceedings
We invite two types of submissions for this workshop:
Technical papers in any of the topics of interest of the workshop (but not limited to them)
Short position papers in any of the topics of interest of the workshop (but not limited to them)
Submitted papers will be peer-reviewed and selected on the basis of these reviews. Accepted papers will be presented at the workshop.
Format requirements for submissions of papers are:
Maximum 16 pages, including title page and bibliography for technical papers.
Maximum 8 pages, including title page and bibliography for short position papers.