Graph Databases: Their Power and Limitations

Pokorný, Jaroslav

doi:10.1007/978-3-319-24369-6_5

Cited by 61 publications

(41 citation statements)

References 11 publications

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“…They usually o®er a support for complex search expressions, full-text search, ranking and grouping of search results, geospatial search and distributed search for high scalability. More generally, NoSQL databases include also graph databases 8 and others, e.g. XML and RDF ones.…”

Section: Categories Of Nosql Databasesmentioning

confidence: 99%

Integration of Relational and NoSQL Databases

Pokorný

2019

Vietnam J. Comp. Sci.

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The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.

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Section: Categories Of Nosql Databasesmentioning

confidence: 99%

Integration of Relational and NoSQL Databases

Pokorný

2019

Vietnam J. Comp. Sci.

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“…Different approaches for structuring information in graph databases were considered for representing knowledge in our conversational agent [22]. One of them, the labeled-property graph model (used, for example, by the Neo4j Database Management System [20]) consists of adding properties to nodes or relationships to specify different metadata in a keyvalue fashion.…”

Section: Proceedings Of Rochi 2020mentioning

confidence: 99%

Conversational Agent in Romanian for Storing User Information in a Knowledge Graph

Boroghina¹,

Corlatescu

Dascălu

2020

RoCHI - International Conference on Human-Computer Interaction

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Information surrounds us and keeping track of relevant details can be challenging. Although there are multiple applications to take notes, organize ideas, or set reminders, existing solutions are semantic-agnostic and rely on the user to manually search for desired information by keywords. We propose a novel method to help people store and retrieve such details with ease in Romanian language. Our conversational agent built on top of the RASA framework is capable to extract relevant information from the user's utterances, store them in a persistent knowledge graph, and ultimately, access them when requested. A set of specific intents regarding locations, timestamps, and properties were created and learned by the agent using manually built examples. In addition, an interaction logic based on a knowledge graph was added to enable the storage and retrieval of information, based on the identified semantic components from the input sentences. The performed tests showed a good accuracy for intent detection, and promising results for the sentence parser. Our conversational agent is accessible as a web application which can process text or speech inputs, and responds with a textual representation of the user's memorized facts.

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“…In this section, we focus on basics of graph querying (for more details see [11]). Its simplest type uses the index-free adjacency.…”

Section: Graph Queryingmentioning

confidence: 99%

Functional querying in graph databases

Pokorný

2017

Vietnam J Comput Sci

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The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual and data level. The notions of graph conceptual schema and graph database schema are considered. The notion of a typed attribute is used as a basic structure both on the conceptual and database level. As a formal approach to declarative graph database querying a version of typed lambda calculus is used. This approach allows to use a logic necessary for querying, arithmetic as well as aggregation function. Another advantage is the ability to deal with relations and graphs in one integrated environment.

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Graph Databases: Their Power and Limitations

Cited by 61 publications

References 11 publications

Integration of Relational and NoSQL Databases

Integration of Relational and NoSQL Databases

Conversational Agent in Romanian for Storing User Information in a Knowledge Graph

Functional querying in graph databases

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