Aug 24, 2014
New York City
Submission Deadline: Jun 7, 2014
Notification Due: Jul 1, 2014
Final Version Due: Jul 8, 2014
Workshop Twitter hashtag: #CrowdSens2014
According to research conducted by the International Data Corporation (IDC), the size of the ‘digital universe’ in 2010 (i.e., the amount of information which is stored digitally) surpassed one Zettabyte (ZB) for the first time in history and it now stands at about 1.8 ZB. This massive expansion in the size of the amount of information appears to be exceeding Moore’s Law. It is also estimated that about 70% of this information is generated by individuals. The ubiquitous availability of computing technology, in particular smartphones, tablets, laptops and other easily portable devices, and the adoption of social networking sites, make it possible to be connected and continuously contribute to this massively distributed information publishing process.
By doing so, users are (unconsciously) acting as social sensors, whose sensor readings are their manually generated data. People document their daily life experiences, report on their physical locations and social interactions with others, express opinions and provide diverse observations on both the physical world (sights, sounds, smells, feelings, etc.) and the online world (news, music, events, etc.). Such massive amounts of ubiquitous social sensors, if wisely utilized, can provide new forms of valuable information that are currently not available by any traditional data collection methods including real physical sensors, and can be used to enhance decision making processes.
It has been shown over and over that reports on real world events, such as the Japan’s Earthquake and Tsunami, the Arab Spring uprisings, and the England’s riots happened in 2011, are much faster propagated within the network of social sensors (e.g. on Twitter) than they are processed by traditional means (e.g. seismic sensor reading analysis, police emergency reports, news media coverage). In these cases, human observers can be exploited to interpret and enrich such integrated sensor-derived information. As an example, both journalists and opinion makers now make increasing usage of massive data collected from social sensors in order to study public opinions, and discover new perspectives of daily stories. As another example, within a smart city scenario, social sensors can contribute important information about the daily city life through various channels, such as social media, SMS, and reports to the city operation center. Such social sensors can enrich the existing information currently collected by the city physical sensors (e.g. traffic and camera sensors), helping to reduce uncertainty, and leading to a better envision and comprehension of the magnitude of potential problems and situations.
Effective mining, analyzing, fusing, and exploiting information sourced from multimodal physical and social sensor data sources is still an open and exciting challenge. Many factors here add to the complexity of the problem, including the real-time element of the data processing; the heterogeneity of the sources, from physical sensors data to posts on social media; and the ubiquitous and noisy nature of the human-sensor generated information, which can be written in an informal style, duplicated, incomplete or even incorrect.
The 2nd International Workshop on Multimodal Crowd Sensing (CrowdSens 2014) will provide an open forum for researchers from various domains such as data mining, data management, information retrieval, and semantic web, for discussing the above challenges.
Topics of interest
Inspired by this year’s KDD conference focus on “Data Mining for Social Good“, the 2nd International Workshop on Multimodal Crowd Sensing (CrowdSens’2014) aims to gather researchers and practitioners around the topic of “Harnessing crowd sensors for social good.” The main goal of the workshop is therefore to explore how analyzing, fusing and exploiting information from multimodal physical and social sensor (people) data sources can help tackle societal challenges including citizen empowerment, environment protection, direct democracy, education, ageing and well being, smart city living environment, disaster management, etc.
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)
Haggai Roitman, IBM Research – Haifa, Israel
Miriam Fernandez, Knowledge Media Institute, UK
Iván Cantador, Universidad Autónoma de Madrid, Spain
We invite two main types of contributions: full papers (6-8 papers) pages) and short papers (2-4 pages). Both types of contributions could be new research ideas, position statements, critiques of existing approaches, or experiment reports.
Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. Each paper will be reviewed by at least three independent referees.
Manuscripts should be submitted electronically, in PDF format and formatted using the ACM camera-ready templates available at:http://www.acm.org/sigs/publications/proceedings-templates
All submissions will be done electronically via the CrowdSens 2014 Web submission system:
Accepted papers will be published in online proceedings.
At least one author of each accepted paper must register for the conference. Information about registration will be provided at KDD 2014 Web page:
Papers submission: June 7th, 2014
Notification of acceptance: July 1st, 2014
Camera Ready: July 8th, 2014
Workshop: August 24th, 2014
Main conference: August 24-27, 2014
Late submissions will be rejected without further consideration.
Queries regarding paper submissions should be sent to the workshop co-chair: Haggai Roitman (email@example.com)