Szczegóły publikacji
Opis bibliograficzny
Common graph representation of different XBRL taxonomies / Artur BASIURA, Leszek KOTULSKI, Dominik Ziembiński // W: Intelligent Information and Database Systems : 14th Asian Conference, ACIIDS 2022 : Ho Chi Minh City, Vietnam, November 28–30, 2022 : proceedings, Pt. 1 / eds. Ngoc Thanh Nguyen, [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13757. Lecture Notes in Artificial Intelligence). — ISBN: 978-3-031-21742-5; e-ISBN: 978-3-031-21743-2. — S. 507–515. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-12-09. — A. Basiura - dod. afiliacja: BFT24.COM, Lublin
Autorzy (3)
- AGHBasiura Artur
- AGHKotulski Leszek
- Ziembiński Dominik
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 144165 |
|---|---|
| Data dodania do BaDAP | 2022-12-21 |
| DOI | 10.1007/978-3-031-21743-2_40 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Asian Conference on Intelligent Information and Database Systems 2022 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Information nowadays plays a critical role in our lives, and its misinterpretation or lack of data, makes decisions wrong. It is important to systematize it, not only in the local context but globally. Finance is one of the key areas where standardization and normalization are attempted. One of the attempts is the XBRL format which is widely used in finance. However, the problem is the nature of local implementations. There are many different taxonomies that are implemented independently by countries and organizations. Currently there are no attempts to combine them and create a single standard. The paper presents a formal model for storing data in graph structures and the concept of using graph grammar to search financial indicators in big data storage. It provides a basis for the future construction of a common graph representation and thus the accumulation of cross-cutting knowledge.
