원문정보
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
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