BigGIS is a new generation of GIS that supports decision making in multiple scenarios which require processing of large and heterogeneous data sets.
The novelty lies in an integrated analytical approach to spatio-temporal data, that are unstructured and from unreliable sources. The system provides predictive, prescriptive and visual tool integrated in a common analytical pipeline.
- The project is evaluated on three scenarios:
- “Smart city” (urban heat islands, particulate matter)
- “Environmental management” (health threatening animals and plants)
- “Disaster control, civil protection” (air pollution, toxic chemicals)
Please visit the project site for more information as well as some interactive demos.
Current GIS solution are mostly tackling big data related requirements in terms of data volume or data velocity. In the era of cloud computing, leveraging cloud-based resources is a widely adopted pattern. In addition, with the advent of big data analytics, performing massively parallel analytical tasks on large-scale data at rest or data in motion is as well becoming a feasible approach shaping the design of today’s GIS. Although scaling out enables GIS to tackle the aforementioned big data induced requirements, there are still two major open issues. Firstly, dealing with varying data types across multiple data sources (variety) lead to data and schema heterogeneity, e.g., to describe locations such as addresses, relative spatial relationships or different coordinates reference systems. Secondly, modeling the inherent uncertainties in data (veracity), e.g., real-world noise and erroneous values due to the nature of the data collecting process. Both being crucial tasks in data management and analytics that directly affect the information retrieval and decision-making quality and moreover the generated knowledge on human-side (value). By leveraging the the continuous refinement model, we present a holistic approach that explicitly deals with all big data dimensions. By integrating the user in the process, computers can learn from the cognitive and perceptive skills of human analysis to create hidden connections between data and the problem domain. This helps to decrease the noise and uncertainty and allows to build up trust in the analysis results on user side which will eventually lead to an increasing likelihood of relevant findings and generated knowledge.
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