Modern business applications predominantly rely on web technology, enabling software vendors to efficiently provide them as a service, removing some of the complexity of the traditional release and update process. To increasing web application accuracy and speed user process crawler model. Cutting edge business applications transcendently depend on web innovation, empowering programming sellers to give proficiently them as an administration, uprooting a portion of the multifaceted nature of the customary discharge and overhaul process. While this encourages shorter, more productive and successive discharge cycles, it obliges persistent testing. Having knowledge into application conduct through unequivocal models can to a great extent bolster improvement, testing and support. Model-based testing permits effective test creation taking into account a depiction of the states the application can be in and the moves between these states. As determining conduct models that are sufficiently exact to be executable by a test computerization device is a hard assignment, an option is to concentrate them from running applications.
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Pub Date : 1900-01-01DOI: 10.34293/sijash.v7i3.1374
A. Thangamuthu
Over the past few decades, Internet technology has shaped almost everybody's life. Business banks set up internet workstations and provide customer information requirements, internet payment fund diversion financial services, credit, investment, etc. An online payment system is a means for conducting economic transactions based on the Internet.This allows a seller to accept payments over the web or other internet connections, such as direct network connections between retail stores and their suppliers— a common way of keeping inventories just in time. Online payment systems are greatly expanding a company's scope and selling potential.Usually, online payment services are operated by third-party firms like PayPal, Google or Click2Pay. Such companies make a profit by taking a small portion of each transaction, or by signing contracts with institutions that require a large number of transactions. Without the ability to make online payments, a large Internet retailer, like Amazon.com, could not exist. Online payment systems have expanded the playing field between big and small companies, as each of them can adopt the same payment methods when they sign up with third party payment processors.
{"title":"A Survey on Various Online Payment and Billing Techniques","authors":"A. Thangamuthu","doi":"10.34293/sijash.v7i3.1374","DOIUrl":"https://doi.org/10.34293/sijash.v7i3.1374","url":null,"abstract":"Over the past few decades, Internet technology has shaped almost everybody's life. Business banks set up internet workstations and provide customer information requirements, internet payment fund diversion financial services, credit, investment, etc. An online payment system is a means for conducting economic transactions based on the Internet.This allows a seller to accept payments over the web or other internet connections, such as direct network connections between retail stores and their suppliers— a common way of keeping inventories just in time. Online payment systems are greatly expanding a company's scope and selling potential.Usually, online payment services are operated by third-party firms like PayPal, Google or Click2Pay. Such companies make a profit by taking a small portion of each transaction, or by signing contracts with institutions that require a large number of transactions. Without the ability to make online payments, a large Internet retailer, like Amazon.com, could not exist. Online payment systems have expanded the playing field between big and small companies, as each of them can adopt the same payment methods when they sign up with third party payment processors.","PeriodicalId":377883,"journal":{"name":"Data mining and knowledge engineering","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121494344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
{"title":"Data Mining Concepts and Techniques","authors":"S. Gnanapriya, R. Suganya, G. Devi, M. S. Kumar","doi":"10.5860/choice.49-3305","DOIUrl":"https://doi.org/10.5860/choice.49-3305","url":null,"abstract":"Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an \"interesting\" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.","PeriodicalId":377883,"journal":{"name":"Data mining and knowledge engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116696275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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