<i>NetCube</i>: a comprehensive network traffic analysis model based on multidimensional OLAP data cube

Park, Jooyoung; Yu, Jaehak; Park, Jun-Sang; Kim, Myung-Sup

doi:10.1002/nem.1818

Cited by 12 publications

(5 citation statements)

References 22 publications

(51 reference statements)

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“…Most of the network devices like switches, routers, multilayer switches and firewalls support this protocol for scanning and monitoring the determined network. The version v2c and SNMP [9], [10], [11] The Local probe group has showed that there are two important alarm notifications. The first sensor was Disk free and his status was in down state.…”

Section: Methodsmentioning

confidence: 99%

Using a Specialized Software for Comprehensive Monitoring the Suspicious States in Computer Networks

Boyanov¹

2014

JSAR

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In this paper a comprehensive scanning and monitoring the suspicious states in determined computer networks is made. Most of the computer and network problems with many vulnerabilities are connected. Therefore it is advisable to scan the entire computer network in order to detect weaknesses and accordingly be taken precautions Thanks to the many malicious users will not be able to penetrate in the relevant computer network and to gain access to computer resources.

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Section: Methodsmentioning

confidence: 99%

Using a Specialized Software for Comprehensive Monitoring the Suspicious States in Computer Networks

Boyanov¹

2014

JSAR

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“…In the context of road traffic, our literature review revealed the existence of two works proposed by Bauzer-Medeiros et al [24] and Park et al [25], respectively. In the first work, the authors proposed a multidimensional model for the treatment of spatiotemporal data collected by hundreds of sensors placed on the main highways of a city.…”

Section: Spatial Data Warehouses and Spatial Multidimensional Modelsmentioning

confidence: 96%

Cyber-Physical Spatial Decision Support System for Road Traffic Management

Jabeur¹,

Haddad²,

Boulkrouche³

2016

JUSPN

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Nowadays, most of growing cities in the world are witnessing an unprecedented increase in road traffic congestion because of population mobility and sporadic events like accidents and natural disasters. As these congestions generally result in substantial casualties and economic losses, tremendous investments are being spent on efficient solutions for road traffic management. Abundant works have proposed solutions to help road traffic stakeholders in making decisions about ongoing events at the individual and collective levels. However, not enough success is yet achieved when it comes to collecting, processing, and delivering the right data, from the right location, at the right time to the right user. We argue in this paper that the divide should be effectively closed between a real world where road traffic and its related events are happening and a world where decisions are being taken. To this end, we propose to use the emergent technologies of Cyber Physical Systems along with multi-agent system mechanisms for additional autonomy, flexibility, and control of the different aspects of the highly dynamic and uncertain field of road traffic management. Within this scope, we propose an architecture of a Cyber Physical Spatial Decision Support System (CPSDSS) through which we explain how various road traffic challenges could be monitored and controlled

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“…Dimensional data modeling is one of the foundational techniques of modern data warehousing [9]. It has been extensively applied to a wide range of domains involving data analysis and decision support, due to its inherent ability to structure data for quick access and summary [10,11,12]. View materialization is a common methodology used on top of these dimensional data schemes for speeding up query execution.…”

Section: Related Workmentioning

confidence: 99%

A Workload-Driven Approach for View Selection in Large Dimensional Datasets

et al. 2020

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The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of readintensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads.

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NetCube: a comprehensive network traffic analysis model based on multidimensional OLAP data cube

Cited by 12 publications

References 22 publications

Using a Specialized Software for Comprehensive Monitoring the Suspicious States in Computer Networks

Using a Specialized Software for Comprehensive Monitoring the Suspicious States in Computer Networks

Cyber-Physical Spatial Decision Support System for Road Traffic Management

A Workload-Driven Approach for View Selection in Large Dimensional Datasets

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