Space of Data through the Lens of Multilevel Graph
It addresses data representation challenges for researchers in data analysis, though it appears incremental by building on existing dataspace definitions.
The paper tackles the complexity of dataspaces by introducing a multilevel graph data structure with contraction and expansion operations, validated empirically on unstructured data like dream reports.
This work seeks to tackle the inherent complexity of dataspaces by introducing a novel data structure that can represent datasets across multiple levels of abstraction, ranging from local to global. We propose the concept of a multilevel graph, which is equipped with two fundamental operations: contraction and expansion of its topology. This multilevel graph is specifically designed to fulfil the requirements for incremental abstraction and flexibility, as outlined in existing definitions of dataspaces. Furthermore, we provide a comprehensive suite of methods for manipulating this graph structure, establishing a robust framework for data analysis. While its effectiveness has been empirically validated for unstructured data, its application to structured data is also inherently viable. Preliminary results are presented through a real-world scenario based on a collection of dream reports.