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Reference Data Abstraction and Causal Relation based on Algebraic Expressions

Topics: Data Analytics; Data Management for Analytics; Data Mining; Data Science; Data Structures and Data Management Algorithms; Database Architecture and Performance; Evolutionary Computing and Optimization; Information Integration; Information Retrieval; Query Processing and Optimization; Semantic Web Technologies for Data Management; Social Data Analytics

Authors: Susumu Yamasaki and Mariko Sasakura

Affiliation: Department of Computer Science, Okayama University, Tsushima-Naka, Okayama, Japan

Keyword(s): Reference Data, Algebraic Structure, 3-Valued Model Theory.

Abstract: This paper is related to algebraic aspects of referential relations in distributed systems, where the sites as states are assumed to contain pages, and each page as reference data involves links to others as well as its own contents. The links among pages are abstracted into causal relations in terms of algebraic expressions. As an algebra for the representation basis of causal relations, more abstract Heyting algebra (a bounded lattice with Heyting implication) is taken rather than the Boolean algebra with classical implication, where the meanings of negatives are different in the two algebras. A standard form may be obtained from any Heyting algebra expression, which may denote causal relations with Heyting negatives. If the evaluation domain is taken from the 3-valued, then the algebraic expressions are abstract enough to represent referential links of pages in a distributed system, where the link may be interpreted as active, inactive and unknown. There is a critical problem to b e solved in such a framework as theoretical basis. The model theory is relevant to nonmonotonic function or reasoning in AI, with respect to the mapping associated with the causal relations, such that fixed point theory cannot be always routines. This paper presents a method to inductively construct models of algebraic expressions conditioned in accordance to reference data characters. Then we examine the traverse of states with models of algebraic expressions clustering at states, for metatheory regarding searching the reference data in a distributed system. With abstraction from state transitions, an algebraic structure is refined such that operational aspect of traversing may be well formulated. (More)

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Paper citation in several formats:
Yamasaki, S. and Sasakura, M. (2020). Reference Data Abstraction and Causal Relation based on Algebraic Expressions. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-440-4; ISSN 2184-285X, SciTePress, pages 207-214. DOI: 10.5220/0009825602070214

@conference{data20,
author={Susumu Yamasaki. and Mariko Sasakura.},
title={Reference Data Abstraction and Causal Relation based on Algebraic Expressions},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA},
year={2020},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009825602070214},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA
TI - Reference Data Abstraction and Causal Relation based on Algebraic Expressions
SN - 978-989-758-440-4
IS - 2184-285X
AU - Yamasaki, S.
AU - Sasakura, M.
PY - 2020
SP - 207
EP - 214
DO - 10.5220/0009825602070214
PB - SciTePress