MUD 2014 : Mining Urban Data - EDBT/ICDT 2014 Workshop
Call For Papers
Mining Urban Data - EDBT/ICDT 2014 Workshop - Deadline: December 14, 2013
* Deadline Extended *
Call for Papers - MUD - Mining Urban Data
*Paper submission: December 14, 2013
*Notification: January 14, 2014
*Camera-ready: January 20, 2014
*Workshop day: March 28, 2014
* A selection of the presented papers will be invited to prepare an extended and revised version for a Special Issue *
* The workshop is organized by the consortium of the EU Project INSIGHT. For more information please visit www.insight-ict.eu
Smart Cities and Urban Data
Recently, the establishment of innovative technologies related to mobile or wearable computing and smart city infrastructure led to the continuous massive generation of heterogeneous data. For example, sensors established in junctions can estimate the volume of traffic at the specific spot. These sensors nowadays form networks that are able to track the vehicle / transport flow of an entire city. Moreover, GPS sensors installed on public transport (e.g. buses) can create similar city pictures since delays can be tracked and utilized to monitor problems around the city. In addition, citizens constantly interact with mobile sensors in their smart-phones or use wearable technologies (e.g. in shoes) that track their activity. At the same time micro-blogging applications like Twitter provide a new stream of textual information that can be utilized to capture events, trends or sentiment. The purpose of the MUD workshop is to discuss the research challenges that arise due to the introduction of such data.
The below topics will be discussed in the context of smart cities:
- Streaming Data - data generated from sensors can only partially/temporarily be stored. Thus, a major requirement is to process and analyse them as they arrive from the sources. Algorithms should be on-line and adaptive.
Big Data - the massive volume of data demands distributed / parallel processing technologies. Other issues include the complexity of the data coming from different sources with different spatial and temporal references or granularity.
- Mobile Data Management - special techniques are required for storing and processing information in mobile environments.
- Heterogeneous Data and Information Fusion - In many smart-city applications, different types of information (GPS, weather, Twitter, traffic data) should be analysed and combined in order to draw conclusions.
- Short text and Multi-lingual data - The main issue in mining micro-blogging data (e.g. Twitter) is that the text is very short, cursorily written and in different languages.
- Event Detection - A very interesting research issue that arises from such data is the identification of real world events (e.g. "traffic jam", "accident", "flood", "concert").
- Noise, Uncertainty and Crowdsourcing - An important difficulty in smart-city related applications is how to eliminate the huge volumes of noise that appear in all types of data. Uncertainty management procedures as well as crowdsourcing techniques might be required in order to aid the data models disambiguate the information.
MUD will focus on presenting novel approaches that target to some of the following applications: a) Traffic Management, b) Public Transport Adjustment, c) Accident Prevention, d) Resource Allocation, e) Energy Efficiency, f) Sentiment Analysis.
MUD will be interesting for researchers and practitioners working in the following research areas: a) Stream Data Mining, b) Big Data Management, c) Mobile Data Management, d) Sensor and Vehicular Networks, e) Text Mining, f) Machine Learning, g) Social Network Analysis, h) Smart Cities, i) Spatial Analysis, j) Visual Analytics.
Please find more information at the website: www.insight-ict.eu/mud/
EDBT/ICDT 2014 web site: WWW.edbticdt2014.gr
* Gennady Andrienko, Fraunhofer IAIS and City University London
* Dimitrios Gunopulos, National & Kapodistrian University of Athens.
* Vana Kalogeraki, Athens University of Economics and Business
* Ioannis Katakis, National & Kapodistrian University of Athens
* Pedro Jose Marron, Universitat Duisburg-Essen
* Katharina Morik, Technische Universitat Dortmund
* Olivier Verscheure, IBM Research