IQmulus Project (2012-2016)

At a Glance

At a Glance

IQmulus integrates the latest research results in data processing and visualization into a Cloud-based platform for solving important real-life challenges in geospatial applications. New and emerging data acquisition techniques provide fast and efficient means for multi-dimensional spatial data collection. Such raw data is generally big, and includes point clouds and digital images, often enriched with other sensor and thematic data. IQmulus providing a platform to process massive amounts of geospatial big data and serving useful knowledge in a form of processing algorithms developed by the project. The platform is scalable in processing and storage capacity, and capable of handling the four Big Data aspects of variety, volume, velocity and analytics.

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Showcases

Showcases

Initially altogether 139 geo-related User Stories (short natural language sentences of the intended functionality of a software system) have been analyzed, filtered and prioritized. The outcome of this process was then used to define initial ‘Showcases’, i.e., basic sets of requirements to drive the early development of the infrastructure, prototypes and basic services in the first phase of the project.

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User related activities

User related activities

The project’s User Group (organized in WP8) provides critically important input to the key objectives of the project concerning user involvement and partnership building.

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Development work

Development work

IQmulus aims at the development and integration of an infrastructure and platform that will support critical decision-making processing in geospatial applications by setting the following research objectives:

Develop a system for the fusion and integration of very large, highly heterogeneous spatial data sets of different provenance, integrating processing and visualisation. 

  • Developing a system for the fusion and integration of very large, highly heterogeneous spatial data sets such as n-dimensional grid and non-grid coverages and point clouds of different provenance, including processing and visualisation;
  • Creating a model for hierarchic, domain-specific languages with which algorithms for processing of coverages and point clouds;
  • Building a spatial data processing middleware that abstracts from different parallel and distributed processing infrastructures;
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