A Survey on Graph Database Management Techniques for Huge Unstructured Data

Patil, Ninad; Kiran, P.Prudhvi; Patel, Karan

doi:10.11591/ijece.v8i2.pp1140-1149

Cited by 9 publications

(2 citation statements)

References 30 publications

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“… Big data analytics can be used to compute the load of each switch of the SDN topology from the huge monitoring data. Distributed graph databases such as neo4j, Titan, and ArangoDB [36], [37] can store the traffic data for each switch in the SDN network. The traffic data is retrieved from the SDN controller through Application Programming Interface (API) and stored in a graph database.…”

Section: B Big Data Assists Sdn In Traffic Monitoringmentioning

confidence: 99%

A Review on Utilizing Big Data Techniques for Performance Improvement in Software Defined Networking

2023

IJCCCE

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Traditional network abilities have a drastic shortage in the current networking world. Software-Defined Networking (SDN) is a revival development in the networking domain that provides separation of control and data planes, enlarges the data plane granularity, and simplifies the network devices. All these factors accelerate and automate the evolution of new services. However, when the SDN network topology becomes large, it poses new challenges in security, traffic management, and scalability due to the vast amounts of traffic data generated and the need for additional controllers to manage the significant number of networking devices. On the other hand, big data has become an attractive trend that can enhance network performance in general, specifically SDN. Both SDN and big data have gained great attraction from industry and academia. Traditionally, these two subjects have been studied separately in most of the preceding works. However, big data can impact the design and implementation of SDN thoroughly. This paper presents how big data can support SDN in various aspects, including intrusion detection, traffic monitoring, and controller scalability and resiliency. We suggest several approaches toward deeper cooperation between big data and SDN. Index Term – SDN, big data, Spark, graph database, traffic engineering, intrusion detection.

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Section: B Big Data Assists Sdn In Traffic Monitoringmentioning

confidence: 99%

A Review on Utilizing Big Data Techniques for Performance Improvement in Software Defined Networking

2023

IJCCCE

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“…In general, graphs consist of nodes or vertices, and the links between them are considered edges or relationships [6]. They are weighed as a powerful tool to envision and analyze highly connected data.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Graph Techniques Applied in Software Defined Networking

2023

IJCCCE

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Modelling computer networks in general, particularly Software Defined Networking (SDN) as a graph, is beneficial in network planning and design, configuration management, traffic analysis, and security. According to the dynamic nature of SDN, it needs a fast response due to the rapid changes in the network state. The SDN network topology can be modelled as a graph and stored in a graph database, and the traffic load of each switch is stored in the created graph. Consequently, a graph processing framework can be used to process the stored traffic data, and the results are utilized in traffic engineering to assist the SDN controller in network management. This paper provides a comprehensive literature survey involving graph techniques applied to SDN. Then, a summary of graph algorithms is presented. In addition, an overview of graph databases and graph processing frameworks is displayed. Finally, a model is suggested to integrate the graph database and graph processing framework in SDN traffic analysis. Index Terms— Graph algorithms, Graph database, Graph processing frameworks, Network topology modelling, SDN, Traffic Analysis.

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Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios

Vargas-Solar¹,

Zechinelli-Martini²,

Espinosa-Oviedo³

2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

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We argue for the need of a new generation of data science solutions that can democratize recent advances in data engineering and artificial intelligence for non-technical users from various disciplines, enabling them to unlock the full potential of these solutions. To do so, we adopt an approach whereby computational creativity and conversational computing are combined to guide non-specialists intuitively to explore and extract knowledge from data collections. The paper introduces MATILDA, a creativity-based data science design platform, showing how it can support the design process of data science pipelines guided by human and computational creativity.

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A Survey on Graph Database Management Techniques for Huge Unstructured Data

Cited by 9 publications

References 30 publications

A Review on Utilizing Big Data Techniques for Performance Improvement in Software Defined Networking

A Review on Utilizing Big Data Techniques for Performance Improvement in Software Defined Networking

A Survey of Graph Techniques Applied in Software Defined Networking

Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios

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