A data model to deal with multi-scaled networks

Mainguenaud, Michel; Simatic, X.T.

doi:10.1016/0198-9715(92)90009-g

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…Даній моделі присвячені також статті [838,849,850]. Аналогічна ідея запропонована в мультимасштабних мережах (multi -scaled networks) [841].…”

Section: графові бази данихunclassified

60 Years of Databases (part four)

Reznichenko¹

2022

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The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emer- gence formation and rapid development, the era of relational databases, extended relational data- bases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relation-al databases, the results of E. Codd’s scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardiza- tion, and transaction management are revealed. The extended relational databases phase is devot- ed to describing temporal, spatial, deductive, ac- tive, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the former Soviet Union.

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Section: графові бази данихunclassified

60 Years of Databases (part four)

Reznichenko¹

2022

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“…With implicit schema such as RDF [19][20][21], GGL [22][23][24][25], Simatic-XT [26], and Object Exchange Model (OEM) [27]. Implicit schema is integrated in the database as a root of the data structure.…”

Section: Graph Databasesmentioning

confidence: 99%

NLA-Bit: A Basic Structure for Storing Big Data with Complexity O(1)

Ivanova

2021

BDCC

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This paper introduces a novel approach for storing Resource Description Framework (RDF) data based on the possibilities of Natural Language Addressing (NLA) and on a special NLA basic structure for storing Big Data, called “NLA-bit”, which is aimed to support middle-size or large distributed RDF triple or quadruple stores with time complexity O(1). The main idea of NLA is to use letter codes as coordinates (addresses) for data storing. This avoids indexing and provides high-speed direct access to the data with time complexity O(1). NLA-bit is a structured set of all RDF instances with the same “Subject”. An example based on a document system, where every document is stored as NLA-bit, which contains all data connected to it by metadata links, is discussed. The NLA-bits open up a wide field for research and practical implementations in the field of large databases with dynamic semi-structured data (Big Data). Important advantages of the approach are as follow: (1) The reduction of the amount of occupied memory due to the complete absence of additional indexes, absolute addresses, pointers, and additional files; (2) reduction of processing time due to the complete lack of demand—the data are stored/extracted to/from a direct address.

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“…The idea of merging graph theory and object-orientative was first published in 1992 [5], focusing on the graph's structure instead of the data stored in graph elements.…”

Section: Related Workmentioning

confidence: 99%

A statically typed query language for property graphs

Tausch

Philippsen

Adersberger³

2011

Proceedings of the 15th Symposium on International Database Engineering &Amp; Applications - IDEAS '11

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Applications that work on network-oriented data often use property graph models. Although their graph data is represented by an object-oriented model, current approaches cannot define statically typed vertex and edge sets. Thus, custom graph operations use untyped input and output sets and cannot exploit crucial concepts like polymorphism. Not only do illegal calling contexts or arguments result in runtime errors or unexpected query results, but also the resulting code tends to be error prone, unclear, and thus hard to maintain. To solve these problems, we extend the property graph model with typed graph classes and open it up to polymorphism. Our approach is an internal domain specific language for graph traversals based on the object-oriented and functional programming language Scala. A case study emphasizes the usability of our framework.

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A data model to deal with multi-scaled networks

Cited by 15 publications

References 5 publications

60 Years of Databases (part four)

60 Years of Databases (part four)

NLA-Bit: A Basic Structure for Storing Big Data with Complexity O(1)

A statically typed query language for property graphs

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