Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Kepler
  2.2. Mast
 3. Major Problem in Big Data-incidental Endogeneity
  3.1. Method to Handle Endogeneity
 4. Astronomical Data and its Characteristics
  4.1. Heterogeneous Data
  4.2. Complicated Data
  4.3. Imaging Data
  4.4. Relation of Big Data with Astronomical Data
 5. Challenges to Astronomical Data Set With Respect To Big Data That Causes Endogeneity
  5.1. Endogeneity in Astronomical Data
 6. General Techniques for Solving Open Problems for Large Scale Astro-statics
  6.1. Developing Effective Statistical Tools and Algorithm for Dealing with Big Data
  6.2. Implementing High Performance Computing
  6.3. Use of Astronomical Pipelines
 7. Astronomical Data Mining
  7.1 Dame
  7.2. Drawbacks for Data Mining
 8. Experiment
  8.1. Galex
  8.2. Stars
 9. Addressing Endogeneity in Big Astronomical Data
  9.1. Identifying Sources of Endogeneity
  9.2. Potential Solution to Endogeneity
  9.3. Solution 1
  9.4. Solution 2
 10. Data and Measurement
  10.1. Extraction of Astronomical Big Numbers
  10.2. Combinatorial Process
  10.3. Scientific Notations
  10.4. Handling Uncertainties for Round Off
 11. Citation and Download of the Big Astronomical Data based upon Individual Needs
  11.1. Format and Size
  11.2. The Dataverse Network
  11.3. Communication Fundamentals
  11.4. Performance Improving
 12. Results
  12.1. Comparing Solutions
  12.2. Experience, Lessons, and Observations
  12.3. Scientific Verification
 13. Conclusions
 Acknowledgements
 References

키워드

저자

• Sumedha Arora [ Guru Nanak Dev University (Rc-Jalandhar) ]
• PankajDeep Kaur [ Guru Nanak Dev University (Rc-Jalandhar) ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605