An analytical review of XML association rules mining

Moradi, Mohammad Hassan; Keyvanpour, Mohammad Reza

doi:10.1007/s10462-012-9376-5

Cited by 24 publications

(7 citation statements)

References 29 publications

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“…In Figure 1, a cloud computing communication between network topology model of multi-layer database index node under the environment of mutual equivalence, a model called the structure of P2P network. But the combination of complex network, it is vulnerable to malicious attacks and input error, disk failure, network overload behavior leads to the collapse of the system [4], cause the index performance is not good, need to optimize the topology structure design, construction of multi topology routing index detection protocol. If cloud computing environment of multi-tier database index topology for node set is…”

Section: Topology Routing Detection Protocolmentioning

confidence: 99%

Topology Robust Study in Multiple Cloud Computing Environment

Yuan¹,

Duan²

2017

dtcse

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In order to realize the optimization of multi database environment index of cloud, it needs to construct index topology, and robustness of the proposed control, depth first traversal mechanism in cloud computing environment index design method based on robust topology. Constructing adaptive robust multilayer routing detection protocol, build cloud computing database the index under the load balance topology control node distribution model, through the transmission cost function of intra cluster and inter cluster index, quantitative equation to build cloud computing node topology multi database environment, quantitative analysis of the quantitative recursive equation, realize the accurate positioning of topological node deployment, and uses the depth first traversal mechanism and the minimum hop count routing method to design robust topology structure, improve the ability of cloud computing database multilayer index environment. Experimental results show, this method is used to design index topology, which improves the efficiency of database multi index, improves the timeliness of database access and the accuracy of data retrieval. KEYWORDSCloud computing environment; multi index; topology; robustness INTRODUCTIONSWith the development and application of stochastic network information technology, the main form of cloud computing gradually developed into modern network information computing and processing. Cloud computing is a computing concept in recent years, the basic idea is to use the idle network, distributed in other local resources, such as computer hardware resources, software resources, centralized intelligent scheduling and information processing of network information resources. In the cloud computing grid space, cloud computing grid space resources needed for automatic deployment management, dynamic expansion, according to need, the effective integration of cloud data center resources, cloud computing environment database multi index control, improve the utilization rate of cyber source cloud computing improve access and scheduling capability, cloud storage database, research in cloud computing environment index topology robust control problem, improve access to database and data Scheduling capability [1].

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Section: Topology Routing Detection Protocolmentioning

confidence: 99%

Topology Robust Study in Multiple Cloud Computing Environment

Yuan¹,

Duan²

2017

dtcse

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“…Takama et al [2] have proposed Association Rule Mining based adaptive search engine that uses RDF documents to observe users' information retrieval behavior and retrieve rules from the patterns. Moradi et al [4] have presented analysis on XML-adopted and XML-specific association rule mining. They further classified the mining techniques as semantic based, apriori based etc.…”

Section: Background and Related Workmentioning

confidence: 99%

FP-Tree based Association Rule Mining in Academic Social Networks to Refine RDF Framework and FOAF

SobhaRani¹,

Raju²,

Kumari³

2013

IJCA

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In order to analyze large scale social networks, different strategies are being implemented. The traditional methods of data mining are getting transformed to be suitable to the requirements of the web based information available in different structured and unstructured formats. The process of web mining, a versatile methodology of data mining, involves modified mining techniques applied on documents spread across the World Wide Web. In this proposed work, the process of association rule mining is applied to Academic Social Networks, an offshoot of Social networks. This technique involves processing of profile pages of members stored in FOAF format and retrieving the association rules from the data to identify the strength of relationship amongst researchers. The RDF vocabulary, a standard format of web based identity representation is the building block of this entire framework. Traditional applications of association rule mining is referred in the context of Market Basket analysis, and its application is projected to analyze dense network and derive association patterns amongst members of the academic social network.

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“…In the traditional methods, the intrusion detection methods of blockchain embedded architecture protocol network mainly include spectral feature detection method, wavelet detection method and correlation matching detection method, etc. Combined with signal processing method, the intrusion detection of transmitted information is realized [2]. Traditionally, a neighborhood intrusion detection algorithm based on Hilbert transform is adopted, which collects network traffic in the transmission channel of the blockchain embedded architecture protocol network, builds an intrusion information flow model of the blockchain embedded architecture protocol network, and adopts the joint time-frequency analysis method to detect the neighborhood intrusion feature information by adaptive convolution filtering.…”

Section: Introductionmentioning

confidence: 99%

Embedded architecture protocol network intrusion detection based on deep residual learning

Wang¹

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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Blockchain Embedded Architecture Protocol network is affected by homomorphic disturbance of nodes in routing and forwarding control, and is vulnerable to embedded intrusion. In order to improve the security of the network, an intrusion detection algorithm based on deep residual learning for Blockchain Embedded Architecture Protocol network is proposed. The statistical feature model of blockchain embedded architecture protocol network intrusion is constructed. The edge information fusion technology of association rules is used to detect and filter the intrusion features of blockchain embedded architecture protocol network, and the spectral features of blockchain embedded architecture protocol network data are extracted. The depth residual learning method is used to carry out the adaptive optimization control in the intrusion detection process of blockchain embedded architecture protocol network, and the multi-threshold depth residual fusion method is used to realize the accurate detection of abnormal traffic data and improve the ability to accurately locate and detect intrusions. The simulation results show that this method has a high accuracy probability, good detection performance and improved network security.

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An analytical review of XML association rules mining

Cited by 24 publications

References 29 publications

Topology Robust Study in Multiple Cloud Computing Environment

Topology Robust Study in Multiple Cloud Computing Environment

FP-Tree based Association Rule Mining in Academic Social Networks to Refine RDF Framework and FOAF

Embedded architecture protocol network intrusion detection based on deep residual learning

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