Semantic++ Electronic Commerce Architecture and Models in Cloud

2019 ◽  
pp. 787-811
Author(s):  
Guigang Zhang ◽  
Chao Li ◽  
Yong Zhang ◽  
Chunxiao Xing ◽  
Sixin Xue ◽  
...  

Electronic commerce is playing a more and more important role in today's commercial activities. In this chapter, the authors propose a kind of new electronic commerce architecture in the cloud and give two kinds of new electronic commerce models. This chapter opens the discussion of why we need to design a new architecture in the cloud environment. Firstly, the authors have a discussion about the semantic++ computing. After the discussion, they give the architecture that can satisfy the requirements in the cloud. This architecture mainly includes five technologies, which are the massive EC data storage technology in the cloud, the massive EC data processing technology in the cloud, the EC security management technology in the cloud, OLAP technology for EC in the cloud, and active EC technology in the cloud. Then, the authors propose two kinds of semantic++ electronic commerce models based on big data. These two models are the new electronic commerce models. The first model is semantic++ electronic commerce Q/A (Questions/Answers) model and another is the active semantic++ electronic commerce model. These two models are all based on big data. Finally, the authors conclude this chapter and give future work.

Author(s):  
Guigang Zhang ◽  
Chao Li ◽  
Yong Zhang ◽  
Chunxiao Xing ◽  
Sixin Xue ◽  
...  

Electronic commerce is playing a more and more important role in today's commercial activities. In this chapter, the authors propose a kind of new electronic commerce architecture in the cloud and give two kinds of new electronic commerce models. This chapter opens the discussion of why we need to design a new architecture in the cloud environment. Firstly, the authors have a discussion about the semantic++ computing. After the discussion, they give the architecture that can satisfy the requirements in the cloud. This architecture mainly includes five technologies, which are the massive EC data storage technology in the cloud, the massive EC data processing technology in the cloud, the EC security management technology in the cloud, OLAP technology for EC in the cloud, and active EC technology in the cloud. Then, the authors propose two kinds of semantic++ electronic commerce models based on big data. These two models are the new electronic commerce models. The first model is semantic++ electronic commerce Q/A (Questions/Answers) model and another is the active semantic++ electronic commerce model. These two models are all based on big data. Finally, the authors conclude this chapter and give future work.


2012 ◽  
Vol 10 (4) ◽  
pp. 42-56
Author(s):  
Guigang Zhang ◽  
Chao Li ◽  
Sixin Xue ◽  
Yuenan Liu ◽  
Yong Zhang ◽  
...  

In this paper, the authors propose a new electronic commerce architecture in the cloud that satisfies the requirements of the cloud. This architecture includes five technologies, which are the massive EC data storage technology in the cloud, the massive EC data processing technology in the cloud, the EC security management technology in the cloud, OLAP technology for EC in the cloud, and active EC technology in the cloud. Finally, a detailed discussion of future trends for EC in the cloud environment is presented in this paper.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Yuanyuan Zhang ◽  
Yan Yan

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.


Author(s):  
Abou_el_ela Abdou Hussein

Day by day advanced web technologies have led to tremendous growth amount of daily data generated volumes. This mountain of huge and spread data sets leads to phenomenon that called big data which is a collection of massive, heterogeneous, unstructured, enormous and complex data sets. Big Data life cycle could be represented as, Collecting (capture), storing, distribute, manipulating, interpreting, analyzing, investigate and visualizing big data. Traditional techniques as Relational Database Management System (RDBMS) couldn’t handle big data because it has its own limitations, so Advancement in computing architecture is required to handle both the data storage requisites and the weighty processing needed to analyze huge volumes and variety of data economically. There are many technologies manipulating a big data, one of them is hadoop. Hadoop could be understand as an open source spread data processing that is one of the prominent and well known solutions to overcome handling big data problem. Apache Hadoop was based on Google File System and Map Reduce programming paradigm. Through this paper we dived to search for all big data characteristics starting from first three V's that have been extended during time through researches to be more than fifty six V's and making comparisons between researchers to reach to best representation and the precise clarification of all big data V’s characteristics. We highlight the challenges that face big data processing and how to overcome these challenges using Hadoop and its use in processing big data sets as a solution for resolving various problems in a distributed cloud based environment. This paper mainly focuses on different components of hadoop like Hive, Pig, and Hbase, etc. Also we institutes absolute description of Hadoop Pros and cons and improvements to face hadoop problems by choosing proposed Cost-efficient Scheduler Algorithm for heterogeneous Hadoop system.


2021 ◽  
Author(s):  
Kovtsur Maxim ◽  
Kistruga Anton ◽  
Mikhailova Anastasiya ◽  
Potemkin Pavel ◽  
Volkogonov Vladimir

Author(s):  
Ganesh Chandra Deka

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.


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