The development of open source software has gained popularity. Most of the software projects use diverse sets of programming languages for development. In this work, the Knowledge Discovery in Data (KDD) approach to a...
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ISBN:
(纸本)9781509041718
The development of open source software has gained popularity. Most of the software projects use diverse sets of programming languages for development. In this work, the Knowledge Discovery in Data (KDD) approach to analyze the data of 30,518 open source projects hosted on SourceForge. The process of knowledge discovery is explored by using the association rule mining algorithm to find the programming languages, which are often used together in combination for the development of software project. The group-matrix based visualization technique is further implied to visualize the generated associated group of languages. The generated knowledge base and visualization of associated languages provide current and future developers with insight knowledge of multiple set of programming languages which are used together frequently for the development of open source software projects.
In India, the accreditation process in engineering made the management and performance of learning process should be enforced to achieve standard goals. The part of every engineering profession is the teaching of the ...
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ISBN:
(纸本)9781509012770
In India, the accreditation process in engineering made the management and performance of learning process should be enforced to achieve standard goals. The part of every engineering profession is the teaching of the basics of the computer science. At the international level, it had been showed that it is almost impossible to satisfying the standard goals. It had been established in a lack of comprehensive maps about scientific concepts. It happens because of getting error in mental model. The results that are based on ideas are definitive. This paper allows concluding that complementing the comprehension of students mental model with a concrete motivation that is based on virtual and actual robots.
In "Big Data" research, the data acquired from many sources are fused and analyzed to obtain valuable and sometimes unexpected information. Even though the volumes of data are unprecedented, the data are usu...
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ISBN:
(纸本)9781467383509
In "Big Data" research, the data acquired from many sources are fused and analyzed to obtain valuable and sometimes unexpected information. Even though the volumes of data are unprecedented, the data are usually stored for post-experiment analysis and for sharing among scientists. Typical scenarios implicitly assume that the data are stored and can be re-analyzed later. The reality is, unfortunately, not so ideal because the data may be "non-persistent" and allow only one-time use. We propose to reformulate how big data programs are developed, and introduce the notion of data-carrying programs that are, in a sense, self-validating. By writing these programs in a specially-defined language, and transforming them to store sample data, programs can save enough data to provide high-confidence validation of their results.
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