Archaeoastronomy in the Big Data Age: Origin and Peculiarities of Obtaining Data on Objects and Artifacts

2019 ◽  
Vol 15 (S367) ◽  
pp. 455-457
Author(s):  
Mina Spasova ◽  
P. Stoeva ◽  
A. Stoev
Keyword(s):  
Big Data ◽  
Everyday Life ◽  
Fine Arts ◽  
Scientific Data ◽  
Data Scarcity ◽  
Initial Form ◽  
Early Emergence ◽  
And Storage ◽  
Huge Variety ◽  
Global Data

AbstractThe impressive transition from an era of scientific data scarcity to an era of overproduction has become particularly noticeable in archaeoastronomy. The collection of astronomical information about prehistoric societies allows the accumulation of global data on: – the oldest traces of astronomical activity on Earth, emotional and rational display of celestial phenomena in astronomical folklore, “folk astronomy” and timekeeping, in fine arts and architecture, in everyday life; – the most ancient applied “astronomy” – counting the time by lunar phases, accumulation and storage of ancient databases in drawings and pictographic compositions in caves and artificially constructed objects; – “horizon” astronomy as an initial form of observational cult astronomy, preserved only in characteristic material monuments (the oldest cult observatories) with indisputable astronomical orientations. The report shows the importance of collecting the maximum number of artifacts and monuments from prehistory associated with the early emergence of interest in celestial phenomena. Spiritual, emotional and rational (including practical) needs that have aroused interest in Heaven are discussed. The huge variety of activities in realizing the regularity (cyclicity) of celestial phenomena as a stimulus for their use for orientation in space and time is shown.

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

scholarly journals Challenges of Big Data analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Measurement Errors ◽  
Modern Society ◽  
Big Data Analysis ◽  
Small Scale ◽  
Paradigm Change ◽  
Statistical Inferences ◽  
The One ◽  
And Storage

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

The Datafied Society

2017 ◽  
Keyword(s):  
Big Data ◽  
Everyday Life ◽  
The Novel ◽  
Research Practices ◽  
Knowledge And Skills ◽  
Its Analysis ◽  
New Knowledge ◽  
Novel Methods ◽  
Way Of Thinking ◽  
Machine Readable

As machine-readable data comes to play an increasingly important role in everyday life, researchers find themselves with rich resources for studying society. The novel methods and tools needed to work with such data require not only new knowledge and skills, but also a new way of thinking about best research practices. This book critically reflects on the role and usefulness of big data, challenging overly optimistic expectations about what such information can reveal, introducing practices and methods for its analysis and visualisation, and raising important political and ethical questions regarding its collection, handling, and presentation.

scholarly journals Data Intensive Scalable Computing (DISC)

2020 ◽  
Author(s):  
Mario A. R. Dantas
Keyword(s):  
Big Data ◽  
Scalable Computing ◽  
Data Intensive ◽  
And Storage

This work presents an introduction to the Data Intensive Scalable Computing (DISC) approach. This paradigm represents a valuable effort to tackle the large amount of data produced by several ordinary applications. Therefore, subjects such as characterization of big data and storage approaches, in addition to brief comparison between HPC and DISC are differentiated highlight.

scholarly journals The Application of Big Data in Modern National Economy and Politics

2021 ◽  
pp. 133-142
Author(s):  
Weili Tian
Keyword(s):  
Big Data ◽  
Technology Research ◽  
Privacy And Security ◽  
Response Strategies ◽  
Existing Problems ◽  
Social Governance ◽  
Government Decision ◽  
Rapid Spread ◽  
Development Data ◽  
Global Data

Big data is a new stage of informatization development. With the convergence and integration of information technology and human production and life, the rapid spread of the Internet, global data showing explosive growth and massive agglomeration, have had a significant impact on economic development, social governance, national management, and people’s lives.Countries around the world regard the promotion of economic digitization as an important driving force for innovation and development, and have made forward-looking layouts in cuttingedge technology research and development, data open sharing, privacy and security protection, and talent training.In-depth understanding of the current situation and trends of big data development, and its impact on economic and social development, analyze the achievements and existing problems of my country’s big data development, summarize and discuss the government’s response strategies, and promote the innovation of government management and social governance models, and realize government decision-making Identification, precise social governance, and efficient public services all have important meanings.

scholarly journals A note on precision-preserving compression of scientific data

2020 ◽  
Author(s):  
Rostislav Kouznetsov
Keyword(s):  
Numerical Data ◽  
Lossy Compression ◽  
Scientific Data ◽  
Essential Information ◽  
Network Bandwidth ◽  
Simple Implementation ◽  
Optimal Accuracy ◽  
Point Data ◽  
Multipoint Statistics ◽  
And Storage

Abstract. Lossy compression of scientific data arrays is a powerful tool to save network bandwidth and storage space. Properly applied lossy compression can reduce the size of a dataset by orders of magnitude keeping all essential information, whereas a wrong choice of lossy compression parameters leads to the loss of valuable data. The paper considers statistical properties of several lossy compression methods implemented in "NetCDF operators" (NCO), a popular tool for handling and transformation of numerical data in NetCDF format. We compare the effects of imprecisions and artifacts resulting from use of a lossy compression of floating-point data arrays. In particular, we show that a popular Bit Grooming algorithm (default in NCO) has sub-optimal accuracy and produces substantial artifacts in multipoint statistics. We suggest a simple implementation of two algorithms that are free from these artifacts and have twice higher precision. Besides that, we suggest a way to rectify the data already processed with Bit Grooming. The algorithm has been contributed to NCO mainstream. The supplementary material contains the implementation of the algorithm in Python 3.

scholarly journals Design of Link Evaluation Method to Improve Reliability based on Linked Open Big Data and Natural Language Processing

2018 ◽  
Vol 7 (3.33) ◽  
pp. 168
Author(s):  
Yonglak SHON ◽  
Jaeyoung PARK ◽  
Jangmook KANG ◽  
Sangwon LEE
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Language Processing ◽  
Confidence Level ◽  
Linked Data ◽  
Evaluation Method ◽  
Data Sets ◽  
Knowledge Based ◽  
Global Data ◽  
Improve Reliability

The LOD data sets consist of RDF Triples based on the Ontology, a specification of existing facts, and by linking them to previously disclosed knowledge based on linked data principles. These structured LOD clouds form a large global data network, which provides a more accurate foundation for users to deliver the desired information. However, it is difficult to identify that, if the presence of the same object is identified differently across several LOD data sets, they are inherently identical. This is because objects with different URIs in the LOD datasets must be different and they must be closely examined for similarities in order to judge them as identical. The aim of this study is that the prosed model, RILE, evaluates similarity by comparing object values of existing specified predicates. After performing experiments with our model, we could check the improvement of the confidence level of the connection by extracting the link value.  

Sign in / Sign up

Export Citation Format

Share Document