BAUSPACE

Abstract

The Internet of Things (IoT) is driving the next economic revolution where the main actors are both the data volume and the immediacy. However, the IoT world is increasingly generating vast amounts of data classified as a dark data, since most of them are generated but never analysed. Therefore, efficient big data analysis in IoT infrastructure is becoming mandatory to transform this data deluge into meaningful information. Even after enabling this analysis, the quantitative information provided by traditional hard sensors is not enough to deal with some scenarios where human observations are required. These observations could be targeted through sensors, where people's opinion in social networks, posts, news or comments may be analyzed to create dynamic observation resources. Combining both sources-of-information (devices and humans) automatically would provide a very powerful tool that could represent a step forward in the data understanding science. However, the development of soft sensors implies the use of many services for crawling text sources and mashup Web-based content, storage it, understanding the language or inferring information, just to mention a few. Therefore, a novel cloud-based distributed system is mandatory to be able to develop such frameworks. In this paper we introduce a work-in-progress for a distributed and modular framework to develop soft sensors in a scalable manner and transparently to the cloud provider.