International Workshop on Situation Recognition by Mining Temporal Information 
SIREMTI2017 co-located with NetSys2017
March 2017 in Göttingen, Germany.

How can we harvest temporal information from our environment, including the web, and create solutions to different situation recognition problems, i.e. small and big emergency or disaster situations of any kind? Relying on the omnipresent information and data available - are we able to create intelligent and assistive approaches that could support us in our everyday life? What kind of  approaches would it be?

Based on the huge amount of available web data - news, texts, messages, - and data from web-connected devices, we should be able to derive relevant information to our advantage and most probably foresee any kind of situation including epidemic or disaster situation. 

There are different methods and algorithms that enable the processing and mining of temporal information from the available temporal data. This workshop focuses on these methods and aims at merging research works relevant to mining temporal information from any kind of data with a special focus on following methods:

- (public) health data models
- (public) health data mining
public health data systems
- trend mining
- trend detection
- topic mining and mining of users posts
- intelligent situation recognition
- data stream processing
- data mining on streaming data
- temporal data mining
- geo-data mining
- spatio-temporal information retrieval
- data-based situation recognition
- wireless data mining
- signal detection and analysis
- complex event processing
- semantic complex event processing
- ontology-based methods for situation recognition
- rule-based methods for analysis of data streams

We are interested in bringing together researchers focusing on harvesting temporal information from different data, including web data (with a focus on public health data) -  news, texts, messages - data of different kind, spatio-temporal data, geo-data and mobile data, sensor data from any environmental sensors, data from ambient RF signals with the goal of situation recognition. Also, we welcome research works describing relevant use cases or reporting on any applied solutions to the problems of situation recognition with the goal of assistance to the users and respect to users' privacy.

Expected participants:
Researchers from computer science and engineering are welcome.