scholarly journals E-Commerce Performance with Cloud Computing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1007-1009
Keyword(s):  
Cloud Computing ◽  
Electronic Commerce ◽  
Web Of Things ◽  
Business Experience ◽  
Online Business ◽  
Exchange Data ◽  
The Web

Electronic Commerce or E-Commerce is a term for a business that incorporates the trading of information over the web. It covers an extent of different kind of business from buyer based retail goals, trough closeout or music site, to business exchanges trading items and ventures between undertakings. The web of things is the arrangement of a physical inquiry embedded with devices, programming, sensors and framework accessibility that engages to assemble and exchange data. This paper will illuminate how IOT is executed in online business experience nearby the delineation of Amazon dash an IOT advancement made by Amazon

scholarly journals Data Duplication in Cloud for Optimal Performance and Security

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1156-1158
Keyword(s):  
Electronic Commerce ◽  
Optimal Performance ◽  
Web Of Things ◽  
Business Experience ◽  
Online Business ◽  
Exchange Data ◽  
The Web

Electronic Commerce or E-Commerce is a term for a business that incorporates the trading of information over the web. It covers an extent of different kind of business from buyer based retail goals, trough closeout or music site, to business exchanges trading items and ventures between undertakings. The web of things is the arrangement of a physical inquiry embedded with devices, programming, sensors and framework accessibility that engages to assemble and exchange data. This paper will illuminate how IOT is executed in online business experience nearby the delineation of Amazon dash an IOT advancement made by Amazon.

Providing a Smart Industrial Environment with the Web of Things and Cloud Computing

2016 ◽  
Author(s):  
Marlon C. Domenech ◽  
Leonardo P. Rauta ◽  
Marcelo Dornbusch Lopes ◽  
Paulo H. Da Silva ◽  
Rodrigo C. Da Silva ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Industrial Environment ◽  
Web Of Things ◽  
The Web

scholarly journals Ontology-Defined Middleware for Internet of Things Architectures

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1163 ◽  
Author(s):  
Víctor Caballero ◽  
Sergi Valbuena ◽  
David Vernet ◽  
Agustín Zaballos
Keyword(s):  
Internet Of Things ◽  
Data Exchange ◽  
The Internet ◽  
Semantic Web Technologies ◽  
Web Of Things ◽  
Web Technologies ◽  
Massive Number ◽  
The Internet Of Things ◽  
Exchange Data ◽  
The Web

The Internet of Things scenario is composed of an amalgamation of physical devices. Those physical devices are heterogeneous in their nature both in terms of communication protocols and in data exchange formats. The Web of Things emerged as a homogenization layer that uses well-established web technologies and semantic web technologies to exchange data. Therefore, the Web of Things enables such physical devices to the web, they become Web Things. Given such a massive number of services and processes that the Internet of Things/Web of Things enables, it has become almost mandatory to describe their properties and characteristics. Several web ontologies and description frameworks are devoted to that purpose. Ontologies such as SOSA/SSN or OWL-S describe the Web Things and their procedures to sense or actuate. For example, OWL-S complements SOSA/SSN in describing the procedures used for sensing/actuating. It is, however, not its scope to be specific enough to enable a computer program to interpret and execute the defined flow of control. In this work, it is our goal to investigate how we can model those procedures using web ontologies in a manner that allows us to directly deploy the procedure implementation. A prototype implementation of the results of our research is implemented along with an analysis of several use cases to show the generality of our proposal.

A Study for VM Placement Schemes in Cloud

2017 ◽  
Vol 7 (8) ◽  
pp. 208
Author(s):  
Ramandeep Kaur ◽  
Navpreet Kaur
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Task Scheduling ◽  
Specific Time ◽  
Due Date ◽  
Vm Placement ◽  
Time Task ◽  
Wide Range ◽  
The Web

The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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