Szczegóły publikacji

Opis bibliograficzny

Associative representation and processing of databases using DASNG and AVB+trees for efficient data access / Adrian HORZYK // W: Knowledge Discovery, Knowledge Engineering and Knowledge Management : 9th international joint conference, IC3K 2017 : Funchal, Madeira - Portugal, 1–3 November, 2017 : revised selected papers / eds.  Fred A., [et al.]. — Cham : Springer International Publishing, 2019. — (Communications in Computer and Information Science ; ISSN 1865-0929 ; 976). — ISBN: 978-3-030-15639-8; e-ISBN: 978-3-030-15640-4. — S. 242-267. — Bibliogr. s. [267], Abstr.


Autor


Słowa kluczowe

AVB-treesassociative database transformationbig data representation and processingdeep associative semantic neuronal graphsassociative graph data structuresAVB+treesdeep neural network architectures

Dane bibliometryczne

ID BaDAP121863
Data dodania do BaDAP2019-05-28
DOI10.1007/978-3-030-15640-4_13
Rok publikacji2019
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaSpringer
Konferencja9th International joint Conference on Knowledge discovery, Knowledge engineering and Knowledge management
Czasopismo/seriaCommunications in Computer and Information Science

Abstract

Today, we have to cope with a great amount of data – BIG data problems. The main issues concerned about BIG data are sparing representation, time efficiency of data access and processing, as well as data mining and knowledge discovery. When dealing with the big amount of data, time is crucial. The most of time for data processing in the contemporary computer science is lost for a various search operation to access appropriate data. This paper presents how data collected in relational databases can be transformed into the associative neuronal graph structures, and how searching operations can be accelerated thanks to the use of aggregation and association of the stored data. To achieve an extraordinary efficiency in data access, this paper introduces new AVB+trees which together with Deep Associative Semantic Neuronal Graphs which can typically allow for constant time access to the stored data. The presented solution allows representing horizontal and vertical relations between data and stored objects, expanding possibilities of relational databases and replacing various search operations by the specific graph structure. Another contribution is the expansion of the aggregation of the duplicates to all data tables which contain the same attributes. In such a way, the presented associative structures simplify and speed up all searching operations in comparison to the classic solutions. © Springer Nature Switzerland AG 2019.

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Deep associative semantic neural graphs for knowledge representation and fast data exploration / Adrian HORZYK // W: IC3K 2017 [Dokument elektroniczny] : proceedings of the 9th International joint Conference on Knowledge discovery, Knowledge engineering and Knowledge management : Funchal, Madeira - Portugal, 1–3 November, 2017. Vol. 2, KEOD / eds. David Aveiro, Jan Dietz, Joaquim Filipe. — Wersja do Windows. — Dane tekstowe. — [Portugal] : SCITEPRESS - Science and Technology Publications, cop. [2017]. — e-ISBN: 978-989-758-272-1. — S. 67-79. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: http://www.scitepress.org/DigitalLibrary/PublicationsDetail.a... [2017-12-04]. — Bibliogr. s. 78–79, Abstr. — Dostęp po zalogowaniu
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Associative graph data structures with an efficient access via AVB+trees / Adrian HORZYK // W: 2018 11th International conference on Human System Interaction (HSI) [Dokument elektroniczny] : July 4–6 2018, Gdańsk : conference proceedings / eds. Adam Bujnowski, Mariusz Kaczmarek, Jacek Rumiński. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, 2018. — Dod. USB ISBN 978-1-5386-5023-3. — ISBN: 978-1-5386-5025-7; e-ISBN: 978-1-5386-5024-0. — S. 169–175. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 175, Abstr.