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 1st International Workshop on Multimodal Crowd Sensing (CrowdSens 2012) will provide an open forum for researchers from various domains such as data management, data mining, information retrieval, and semantic web, for discussing the above challenges.