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Scalable Community Detection for Complex Data graphs via Hyperbolic Network Embedding and graph databases

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第2期8卷 1269-1282页

作者： Tsitseklis, Konstantinos Krommyda, Maria Karyotis, Vasileios Kantere, Verena Papavassiliou, Symeon Natl Tech Univ Athens Sch Elect & Comp Engn Inst Commun & Comp Syst ICCS Zografos 15780 Greece Ionian Univ Dept Informat Corfu 49132 Greece

Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding.

关键词： databases Resource description framework Data visualization Feature extraction Image edge detection Task analysis Europe Community detection feature-rich information graph data graph databases hyperbolic network embedding scalable clustering

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Static analysis of navigational XPath over graph databases

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INFORMATION PROCESSING LETTERS 2016年第7期116卷 467-474页

作者： Kostylev, Egor V. Reutter, Juan L. Vrgoc, Domagoj Univ Oxford Oxford OX1 2JD England PUC Chile Santiago Chile Ctr Semant Web Res Santiago Chile

Most query languages for graph databases rely on exploring the topological properties of the data by using paths. However, many applications require more complex patterns to be matched against the graph to obtain desired results. For this reason a version of the standard XML query language XPath has been adapted to work over graphs. In this paper we study static analysis aspects of this language, concentrating on problems such as containment, equivalence and satisfiability. We show that for the full language all of the problems are undecidable. By restricting the language we then obtain several natural fragments whose complexity ranges from PSPACE-complete to EXPTIME-complete. (C) 2016 Elsevier B.V. All rights reserved.

关键词： databases graph databases Query languages Static analysis

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Modeling XACML Security Policies Using graph databases

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IT PROFESSIONAL 2017年第6期19卷 52-57页

作者： Paniagua Diez, Fidel Vasu, Amrutha Chikkanayakanahalli Suarez Touceda, Diego Sierra Camara, Jose Maria Univ Carlos III Madrid Evalues Lab IT Secur Evaluat Madrid Spain Northeastern Univ Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Boston MA 02115 USA

XACML policies can be presented in a graph data structure, but while these solutions increase performance, they also drastically decrease functionality. To address this, the authors' approach models and stores XAC... 详细信息

关键词： Authorisation Data Structures graph Theory XML Modeling XACML Security Policies graph Database Policies Increases Performance Problems Representing XACML Policies graph Data Structure Solutions Increase Performance Modeling Storing XACML Policies Access Control Language XML Computer Security graphical Models databases Access Control Computer Languages Security Policies Access Control XACML graph databases

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Query Languages for graph databases

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SIGMOD RECORD 2012年第1期41卷 50-60页

作者： Wood, Peter T. Univ London Dept Comp Sci & Informat Syst London WC1E 7HU England

Query languages for graph databases started to be investigated some 25 years ago. With much current data, such as linked data on the Web and social network data, being graph-structured, there has been a recent resurgence in interest in graph query languages. We provide a brief survey of many of the graph query languages that have been proposed, focussing on the core functionality provided in these languages. We also consider issues such as expressive power and the computational complexity of query evaluation.

关键词： graph databases

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Compact and efficient representation of general graph databases

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KNOWLEDGE AND INFORMATION SYSTEMS 2019年第3期60卷 1479-1510页

作者： Alvarez-Garcia, Sandra Freire, Borja Ladra, Susana Pedreira, Oscar Indra La Coruna Spain Enxenio SL R&D Projects Related Bioinformat La Coruna Spain Univ A Coruna Database Lab CITIC Campus Elvina La Coruna 15071 Spain

In this paper, we propose a compact data structure to store labeled attributed graphs based on the k(2)-tree, which is a very compact data structure designed to represent a simple directed graph. The idea we propose can be seen as an extension of the k(2)-tree to support property graphs. In addition to the static approach, we also propose a dynamic version of the storage representation, which allows flexible schemas and insertion or deletion of data. We provide an implementation of a basic set of operations, which can be combined to form complex queries over these graphs with attributes. We evaluate the performance of our proposal with existing graph database systems and prove that our compact attributed graph representation obtains also competitive time results.

关键词： Compression graph databases Property graphs Attributed graphs Compact data structures Dynamic graphs

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graph databases: A survey

Graph databases: A survey

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2015 International Conference on Computing, Communication and Automation, ICCCA 2015

作者： Kumar Kaliyar, Rohit Computer Science and Engineering Shiv Nadar University Greater Noida India

ISBN: (纸本)9781479988907

In the era of big data, data analytics, business intelligence database management plays a vital role from technical business management and research point of view. Over many decades, database management has been a topic of active research. There are different type of database management system have been proposed over a period of time but Relational Database Management System (RDBMS) is the one which has been most popularly used in academic research as well as industrial setup[1]. In recent years, graph databases regained interest among the researchers for certain obvious reasons. One of the most important reasons for such an interest in a graph database is because of the inherent property of graphs as a graph structure. graphs are present everywhere in the data structure, which represents the strong connectivity within the data. Most of the graph database models are defined in which data-structure for schema and instances are modeled as graph or generalization of a graph. In such graph database models, data manipulations are expressed by graph-oriented operations and type constructors [9]. Now days, most of the real world applications can be modeled as a graph and one of the best real world examples is social or biological network. This paper gives an overview of the different type of graph databases, applications, and comparison between their models based on some properties. © 2015 IEEE.

关键词： graph databases

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graph databases for designing high-performance speech recognition grammars 12

Graph databases for designing high-performance speech recogn...

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12th International Conference on Computational Semantics, IWCS 2017

作者： Di Maro, Maria Valentino, Marco Riccio, Anna Origlia, Antonio Università degli Studi di Napoli Federico II Università degli Studi di Napoli ‘L’Orientale’ Università degli Studi di Padova

The present paper reports on the advantages of using graph databases in the development of dynamic language models in Spoken Language Understanding applications, such as spoken dialogue systems. First of all, we introduce Neo4J graph databases and, specifically, MultiWordNet-Extended, a graph representing linguistic knowledge. After this first overview, we show how information included in graphs can be used in speech recognition grammars to automatically extend a generic rule structure. This can be the case of linguistic elements, such as synonyms, hypernyms, meronyms and phonological neighbours, which are semantically or structurally related to each other in our mental lexicon. In all the AI based approaches depending on a training process using large and representative corpora, the probability to correctly predict the creativity a speaker can perform in using language and posing questions is lower than expected. Trying to capture most of the possible words and expressions a speaker could use is extremely necessary, but even an empirical, finite collection of cases could not be enough. For this reason, the use of our tool appears as an appealing solution, capable of including many pieces of information. In addition, we used the proposed tool to develop a spoken dialogue system for museums and the preliminary results are shown and discussed in this paper. © IWCS 2017. All rights reserved.

关键词： graph databases

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Demystifying graph databases: Analysis and Taxonomy of Data Organization, System Designs, and graph Queries

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ACM COMPUTING SURVEYS 2024年第2期56卷 1-40页

作者： Besta, Maciej Gerstenberger, Robert Peter, Emanuel Fischer, Marc Podstawski, Michal Barthels, Claude Alonso, Gustavo Hoefler, Torsten Swiss Fed Inst Technol Inst Comp Platforms OAT S 17Andreasstr 5 CH-8092 Zurich Switzerland Swiss Fed Inst Technol Inst Comp Platforms Dep Comp Sci STF J 420Stampfenbachstr 114 CH-8092 Zurich Switzerland PRODYNA Schweiz AG Grosspeteranlage 29 CH-4052 Basel Switzerland Future Proc Bojkowska 37a PL-44100 Gliwice Poland

Numerous irregular graph datasets, for example social networks or web graphs, may contain even trillions of edges. Often, their structure changes over time and they have domain-specific rich data associated with vertices and edges. graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size and irregularity of such datasets, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., document stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., Resource Description Framework or Labeled Property graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and Atomicity, Consistency, Isolation, Durability). Fifty-one graph database systems are presented and compared, including Neo4j, OrientDB, and Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we outline future research and engineering challenges related to graph databases.

关键词： graphs graph databases NoSQL Stores graph Database Management Systems graph Models Data Layout graph Queries graph Transactions graph Representations RDF Labeled Property graph Triple Stores Key-Value Stores RDBMS Wide-Column Stores Document Stores

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Multi-Dimensional Event Data in graph databases

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JOURNAL ON DATA SEMANTICS 2021年第1-2期10卷 109-141页

作者： Esser, Stefan Fahland, Dirk INFORM GmbH Aachen Germany Eindhoven Univ Technol Eindhoven Netherlands

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as "directly/eventually-follows," it does not support querying multi-dimensional event data of multiple related entities. Relational databases allow storing multi-dimensional event data, but existing query languages do not support querying for sequences or paths of events in terms of temporal relations. In this paper, we propose a general data model for multi-dimensional event data based on labeled property graphs that allows storing structural and temporal relations in a single, integrated graph-based data structure in a systematic way. We provide semantics for all concepts of our data model, and generic queries for modeling event data over multiple entities that interact synchronously and asynchronously. The queries allow for efficiently converting large real-life event data sets into our data model, and we provide 5 converted data sets for further research. We show that typical and advanced queries for retrieving and aggregating such multi-dimensional event data can be formulated and executed efficiently in the existing query language Cypher, giving rise to several new research questions. Specifically, aggregation queries on our data model enable process mining over multiple inter-related entities using off-the-shelf technology.

关键词： Process mining Event log Multi-dimensional processes Querying Labeled property graphs graph databases

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graph databases in electronic communications network: assessment based on query execution time 11

Graph databases in electronic communications network: assess...

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11th International Scientific Conference "Information Technology and Implementation", IT and I 2024

作者： Herasymenko, Oksana Ivanytska, Anna Taras Shevchenko National University ofKyiv 60 Volodymyrska Street Kyiv01033 Ukraine

graph databases are a powerful data structure that can be applied to solve a variety of problems. They are widely used in electronic communications network due to the need for effective management of complex network structures, where traditional relational databases do not always provide sufficient performance and flexibility. There are many graph mapping algorithms. Each of them has its own characteristics and best areas of use. In this paper, Neo4j, Memgraph and ArangoDB graph databases are considered and compared for query performance in an experiment on the same data set by measuring query execution time. Also, a usability estimation is given for Neo4j, Memgraph and ArangoDB, which based on number of GitHub users, number of image downloads, support ability, deployment ability and supported programming languages. © 2024 Copyright for this paper by its authors.

关键词： graph databases

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