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RISS 인기검색어
- 검색키워드
- 저자 : Pankajdeep Kaur (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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An Ontology Based Text Analytics on Social Media
Pankajdeep Kaur,Pallavi Sharma,Nikhil Vohra 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.5
The amount of digital information that is created and used is progressively rising along with the growth of sophisticated hardware and software. In addition, real-world data come in a diversity of forms and can be tremendously bulky. This has augmented the need for powerful algorithms that can deduce and dig out appealing facts and useful information from these data. Text Mining (TM), which is a very complex process; has been successfully used for this purpose. Text mining alternately referred to as text data mining, more or less equivalent to text analytics, can be defined as the process of extracting high-quality information from text. Text mining involves the process of structuring the input data, deriving patterns within the structured data and lastly interpretation and revelation of the output. This paper provides outline on text analytics and social media analytics. At the end, this paper presents our proposed work based on ontology framework to cope up with excessive social media textual data.
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An Ontology Based E-Learning System
Pankajdeep Kaur,Pallavi Sharma,Nikhil Vohra 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.5
Based upon several new technologies that have been developed such as semantic web, SPARQL language and ontology engineering, this paper proposes platform architecture for e-learning. It is an e-learning management system with metadata. This system consists of ontology for the e-learning process, such as teaching methods, learning styles, learning activities and course syllabus. It helps students, administrative staff and teachers to set up and maintain the course data and go through the learning content. This system architecture will be capable of gaining user adaptability, performance scalability and concept reusability. It has ability to act in an intelligent manner by evaluating the academics initially and then provide personalized suggestions to the academics indicating their weaknesses and strengths.
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Virtual Machine Migration in Cloud Computing
Pankajdeep Kaur,Anita Rani 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.5
Cloud computing is the delivers the computing services over the internet. Cloud services help individuals and organization to use data that are managed by third parties or another person at remote locations. Virtual Machine (VM) is an emulation of a particular computer system. In cloud computing, Virtual machine migration is a useful tool for migrating Operating System instances across multiple physical machines. It is used to load balancing, fault management, low-level system maintenance and reduce energy consumption. There are various techniques and parameters available for VM migration. This paper presents the various virtual machine migration techniques.
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Migration Jobs in Cloud Computing
Anita Rani,Pankajdeep Kaur 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.6
Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. In cloud computing, Process and virtual machine migration are two migration techniques. Process migration is a technique in which an active process is moved from one machine to another of possibly different architecture and Virtual Machine (VM) is an emulation of a particular computer system. Virtual machine migration is a useful tool for migrating OS instances across multiple physical machines. Both techniques are used to load balancing, fault management, low-level system maintenance, better communication and reduce energy consumption. This paper presents the various virtual machine and process migration techniques.
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Handling Endogeneity Challenge in Big Astronomical Data
Sumedha Arora,PankajDeep Kaur 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.7
Using Big Data in statistically valid ways is posing a great challenge. The main misconception that lies in using Big Data is the belief that volume of data can compensate for any other deficiency in data. There is a need to use some standards and transparency when using Big Data in survey research. Certain surveys that are based on the Big Data tend to generate more complications and complexities in data such as some important variables tend to correlate with some errournious data. This correlation of data with residual noise causes the endogeneity problem. It is to be solved as a fact the main aim of research work is answering question which could only be done when data is fully analyzed. Through this we can utilize all available information. This paper throws light on addressing endogeneity particularly to the astronomical data set and also provides solutions and techniques for handling endogeneity in the respective data set. Finally it couples big data i.e. whole data of sky with the time domain.
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