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Towards a Generalised Semistructured Data Model and Query Language

Lookup NU author(s): Dr Giacomo BergamiORCiD

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Abstract

Although current efforts are all aimed at re-defining new ways to harness old data representations, possibly with new schema features, the challenges still open provide evidence of the need for a "diametrically opposite" approach: in fact, all information generated in real contexts is to be understood lacking of any form of schema, where the schema associated with such data is only determined a posteriori based on either a specific application context, or from some data's facets of interest. This solution should still enable recommendation systems to manipulate the aforementioned data semantically. After providing evidence of these limitations from current literature, we propose a new Generalized Semistructured data Model that makes possible queries expressible in any data representation through a Generalised Semistructured Query Language, both relying upon script v2.0 as a MetaModel language manipulating types as terms as well as allowing structural aggregation functions.

Publication metadata

Author(s): Bergami G, Zegadlo W

Publication type: Article

Publication status: Published

Journal: ACM SIGWEB Newsletter

Year: 2023

Volume: 2023

Issue: Summer

Print publication date: 01/08/2023

Acceptance date: 01/08/2023

ISSN (print): 1931-1745

ISSN (electronic): 1931-1435

Publisher: ACM

URL: https://doi.org/10.1145/3609429.3609433

DOI: 10.1145/3609429.3609433

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