Abstract
The amount of different environments where data can be exploited have increased partly because of the massive adoption of technologies such as microservices and distributed architectures. Accordingly, approaches to treat data are in constant improvement. An example of this is the Formal Concept Analysis framework that has seen an increase in the methods carried out to increment its capabilities in the mentioned environments. However, on top of the exponential nature of the output that the framework produces, the data stream processing environment still poses challenges regarding the flexibility in the usage of FCA and its extensions. Consequently, several approaches have been proposed to deal with them considering different constraints, such as receiving unsorted elements or unknown attributes. In this work, the notion of flexibly scalable for FCA distributed algorithms consuming data streams is defined. Additionally, the meaning of different scenarios of lattice merge in a particular data stream model is discussed. Finally, a pseudo-algorithm for merging lattices in the case of disjoint objects is presented. The presented work is a preliminary result and, in the future, it is expected to cover the other aspects of the problem with real data for validation.
Partly funded by SNMSF.
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Notes
- 1.
Notice that \(l \le k \text { implies } S_{k,l} = \varnothing \).
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This work has been funded with the help of the French National Agency for Research and Technology (ANRT) and French National Syndicate of Ski Teachers (SNMSF).
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Leutwyler, N., Lezoche, M., Torres, D., Panetto, H. (2023). Towards a Flexible and Scalable Data Stream Algorithm in FCA. In: Ojeda-Aciego, M., Sauerwald, K., Jäschke, R. (eds) Graph-Based Representation and Reasoning. ICCS 2023. Lecture Notes in Computer Science(). Springer, Cham. https://doi.org/10.1007/978-3-031-40960-8_9
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