Explorative Analysis of Heterogeneous, Unstructured, and Uncertain Data: A Computer Science Perspective on Biodiversity Research

We outline a blueprint for the development of new computer science approaches for the
management and analysis of big data problems for biodiversity science. Such problems
are characterized by a combination of different data sources each of which owns at
least one of the typical characteristics of big data (volume, variety, velocity, or veracity).
For these problems, we envision a solution that covers different aspects of integrating
data sources and algorithms for their analysis on one of the following three layers:
At the data layer, there are various data archives of heterogeneous, unstructured,
and uncertain data. At the functional layer, the data are analyzed for each archive
individually. At the meta-layer, multiple functional archives are combined for complex
analysis.

@inproceedings{2014:10,
   author    = "C. Beckstein and S. B{\"o}cker and M. Bogdan and
                H. Bruelheide, H. M. B{\"u}cker and J. Denzler and
                P. Dittrich and I. Grosse and A. Hinneburg and
                B. K{\"o}nig-Ries and F. L{\"o}ffler and M. Marz and
                M. M{\"u}ller-Hannemann and M. Winter and W. Zimmermann",
   title     = "Explorative Analysis of Heterogeneous, Unstructured, and Uncertain Data:
                A Computer Science Perspective on Biodiversity Research",
   booktitle = "Proceedings of the 3rd International Conference on Data Management Technologies and
                Applications, DATA 2014, Vienna, Austria, August 29--31, 2014",
   editor    = "M. Helfert and A. Holzinger and O. Belo and C. Francalanci",
   year      = 2014,
   pages     = "251--257",
   publisher = "SCITEPRESS",
   abstract  = "We outline a blueprint for the development of new computer science approaches for the
                management and analysis of big data problems for biodiversity science. Such problems
                are characterized by a combination of different data sources each of which owns at
                least one of the typical characteristics of big data (volume, variety, velocity, or veracity).
                For these problems, we envision a solution that covers different aspects of integrating
                data sources and algorithms for their analysis on one of the following three layers:
                At the data layer, there are various data archives of heterogeneous, unstructured,
                and uncertain data. At the functional layer, the data are analyzed for each archive
                individually. At the meta-layer, multiple functional archives are combined for complex
                analysis.",
}