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Kallio A.,Center for Science Ltd. | Puolamaki K.,Aalto University | Fortelius M.,University of Helsinki | Mannila H.,HIIT Helsinki Institute for Information Technology
Palaeontologia Electronica | Year: 2011

Correlation between occurrences of taxa is a fundamental concept in the analysis of presence-absence data. Such correlations can result from ecologically relevant processes, such as existence and evolution of species communities. Correlations are typically quantified by some sort of similarity index based on co-occurrence counts. We argue that the individual values of a similarity index are not useful as such: rather, we have to be able to estimate the statistical significance of the index value. Secondly, we argue that before computing the correlations one has to carefully select what is the underlying base set of locations for which the co-occurrence counts, similarity indices, and their significance is computed. We demonstrate base set selection with synthetic examples and conclude with an analysis of real data from a large database of fossil land mammals. © Paleontological Society March 2011.


Hinkka M.,Aalto University | Lehto T.,Aalto University | Heljanko K.,Aalto University | Heljanko K.,HIIT Helsinki Institute for Information Technology
Proceedings - 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016 | Year: 2016

Performing Process Mining by analyzing event logs generated by various systems is a very computation and I/O intensive task. Distributed computing and Big Data processing frameworks make it possible to distribute all kinds of computation tasks to multiple computers instead of performing the whole task in a single computer. This paper assesses whether contemporary structured query language (SQL) supporting Big Data processing frameworks are mature enough to be efficiently used to distribute computation of two central Process Mining tasks to two dissimilar clusters of computers providing BPM as a service in the cloud. Tests are performed by using a novel automatic testing framework detailed in this paper and its supporting materials. As a result, an assessment is made on how well selected Big Data processing frameworks manage to process and to parallelize the analysis work required by Process Mining tasks. © 2016 IEEE.

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