Topics of interest

CrowdSens workshop is an opportunity to bring together researchers and practitioners to report on current work and experiments, and to discuss new challenges and opportunities w.r.t. the paradigm described above. Topics of interest include, but are not limited to:

  • Data acquisition methods for crowd sensing

    • Physical world crowd data capture
    • Multimedia crowd data capture (e.g. SMS, MMS, CDRs, transcripts)
    • Real-time data acquisition methods
    • Massive scale social sensor monitoring and crawling
    • Predictive models for social data acquisition
    • Scheduling, prioritization and sampling methods
  • Data models for crowd sensing

    • Social sensor event models
    • Social sensor data representation
    • Social sensor context representation
    • Spatio-temporal models for crowd sensing
    • Multimodal data models for crowd sensing
    • Semantic models for crowd sensing
    • Uncertainty models for incomplete and noisy social sensors data
    • Trust and authorization models for crowd sensing
    • Privacy in crowd sensing
  • Novel data processing, analysis, and classification methods

    • Data cleansing for crowd sensing (e.g. real-time duplicates detection)
    • Feature extraction, Entity analytics and novel NLP methods
    • Context extraction and prediction using multimodal sources
    • Uncertainty estimation and predictive analytics
    • Data mining methods under incomplete and noisy data (e.g. online clustering, categorization, classification)
    • Opinion mining, sentiment analysis methods for crowd sensing
    • Trends, bursts, anomalies and outliers detection over large scale social sensor data
    • Network analysis, information propagation and influence detection methods for crowd sensing
    • Crowd behavioural analysis and prediction
    • Real-time community detection and analysis
    • Social stream processing methods (e.g. top-k querying, filtering, sampling)
  • Event detection, fusion, and summarization methods

    • Event detection methods (under uncertainty, incomplete or noisy settings)
    • Event story detection
    • Detection of developing events
    • Event uncertainty estimation
    • Event time and location estimation
    • Methods for event data delivery
    • Methods for event data reporting, summarization or visualization
    • Pattern recognition methods
    • Multimodal data fusion methods
  • Evaluation methods for crowd-sensing

    • Quality metrics and key performance indicators for crowd sensing
    • Benchmarks and evaluation methodologies for crowd sensing
  • Applications of crowd sensing

    • News mining from social sensors (e.g. emerging story detection)
    • Infotainment (e.g. event discovery and recommendation)
    • Disaster management (e.g. weather monitoring, disaster prediction)
    • Public safety (e.g. prediction of developing situation and sentiments)
    • Public health (e.g. epidemic monitoring, infectious disease outbreak detection)
    • Transportation (e.g. prediction of traffic loads, detection of hazards)
    • Finance (e.g. market monitoring)
    • Cyber security (e.g. Counter terrorism, dark web monitoring)
    • Government and Politics (e.g. Voice of Citizen, opinion mining)
    • Retail and consumer products (e.g. Voice of Customer, demand sensing)

1st International Workshop on Multimodal Crowd Sensing (CrowdSens 2012)
21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
29thOctober-2nd November 2012 | Maui, Hawaii, USA