Navigating Diverse Datasets in the Face of Uncertainty
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Alvarez-Ayllon, AlejandroDate
2023-09-11Department
Ingeniería InformáticaAbstract
When exploring big volumes of data, one of the challenging aspects is their diversity
of origin. Multiple files that have not yet been ingested into a database system may
contain information of interest to a researcher, who must curate, understand and sieve
their content before being able to extract knowledge.
Performance is one of the greatest difficulties in exploring these datasets. On the
one hand, examining non-indexed, unprocessed files can be inefficient. On the other
hand, any processing before its understanding introduces latency and potentially un-
necessary work if the chosen schema matches poorly the data. We have surveyed the
state-of-the-art and, fortunately, there exist multiple proposal of solutions to handle
data in-situ performantly.
Another major difficulty is matching files from multiple origins since their schema
and layout may not be compatible or properly documented. Most surveyed solutions
overlook this problem, especially for numeric, uncertain data, as is typical in fields
like astronomy.
The main objective of our research is to assist data scientists during the exploration
of unprocessed, numerical, raw data distributed across multiple files based solely on
its intrinsic distribution.
In this thesis, we first introduce the concept of Equally-Distributed Dependencies,
which provides the foundations to match this kind of dataset. We propose PresQ,
a novel algorithm that finds quasi-cliques on hypergraphs based on their expected
statistical properties. The probabilistic approach of PresQ can be successfully exploited to mine EDD between diverse datasets when the underlying populations can
be assumed to be the same.
Finally, we propose a two-sample statistical test based on Self-Organizing Maps
(SOM). This method can outperform, in terms of power, other classifier-based two-
sample tests, being in some cases comparable to kernel-based methods, with the
advantage of being interpretable.
Both PresQ and the SOM-based statistical test can provide insights that drive
serendipitous discoveries.
Subjects
hypergraph; data exploration; data analysisCollections
- Tesis [599]
- Tesis Ing. Inf. [25]