Large-Scale Sensor Network Analysis

2013 ◽  
pp. 314-347 ◽  
Author(s):  
Joaquin Vanschoren ◽  
Ugo Vespier ◽  
Shengfa Miao ◽  
Marvin Meeng ◽  
Ricardo Cachucho ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Vital Signs ◽  
Sensor Data ◽  
Atmospheric Conditions ◽  
Big Data Applications ◽  
The World ◽  
Sheer Size ◽  
Effective Use

Sensors are increasingly being used to monitor the world around us. They measure movements of structures such as bridges, windmills, and plane wings, human’s vital signs, atmospheric conditions, and fluctuations in power and water networks. In many cases, this results in large networks with different types of sensors, generating impressive amounts of data. As the volume and complexity of data increases, their effective use becomes more challenging, and novel solutions are needed both on a technical as well as a scientific level. Founded on several real-world applications, this chapter discusses the challenges involved in large-scale sensor data analysis and describes practical solutions to address them. Due to the sheer size of the data and the large amount of computation involved, these are clearly “Big Data” applications.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Effective Use of Website in Large-Scale Event Marketing

2021 ◽  
pp. 282-298
Author(s):  
Seda Derinalp Canakci
Keyword(s):  
Large Scale ◽  
Eastern Mediterranean ◽  
International Tourism ◽  
Mega Events ◽  
Event Marketing ◽  
The World ◽  
International Attention ◽  
International Industry ◽  
Effective Use ◽  
Industry Professionals

The Eastern Mediterranean International Tourism and Travel Fair (EMITT), which will be held for the 25th time in Istanbul in 2022, is preparing to host national and international industry professionals and thousands of tourists who want to take advantage of the new and exciting travel opportunities offered by holiday destinations and travel companies from all over the world. EMITT Fair will open its doors to visitors for the 25th time in 2022. A well-prepared website is of paramount importance to the event industry today. The Eastern Mediterranean International Tourism and Travel Fair is also one of the mega events considered among large-scale events within the scope of congress and fair organizations. Participation in events, which can result in national and international attention, usually begins with examining the websites prepared for the event. This study aims to examine the effectiveness of the website of the Eastern Mediterranean International Tourism and Travel Fair.

Recommender System in the Context of Big Data

2016 ◽  
pp. 231-246
Author(s):  
Khadija Ateya Almohsen ◽  
Huda Kadhim Al-Jobori
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Large Scale ◽  
Singular Value ◽  
Web Content ◽  
Large Scale Systems ◽  
Existing Problems ◽  
Current State ◽  
The World ◽  
Value Decomposition

The increasing usage of e-commerce website has led to the emergence of Recommender System (RS) with the aim of personalizing the web content for each user. One of the successful techniques of RSs is Collaborative Filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. However, as the world enter Big Data era, CF has faced some challenges such as: scalability, sparsity and cold start. Thus, new approaches that overcome the existing problems have been studied such as Singular Value Decomposition (SVD). This chapter surveys the literature of RSs, reviews the current state of RSs with the main concerns surrounding them due to Big Data, investigates thoroughly SVD and provides an implementation to it using Apache Hadoop and Spark. This is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The results proved the scalability of SVD-based RS and its applicability to Big Data.

VEGA, AN ENVIRONMENT FOR GRAVITATIONAL WAVES DATA ANALYSIS

2000 ◽  
Vol 09 (03) ◽  
pp. 293-297 ◽  
Author(s):  
D. BUSKULIC ◽  
L. DEROME ◽  
R. FLAMINIO ◽  
F. MARION ◽  
L. MASSONET ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gravitational Wave ◽  
Gravitational Waves ◽  
Large Scale ◽  
Data Access ◽  
The World ◽  
Gravitational Wave Detectors ◽  
Batch Data ◽  
New Generation

A new generation of large scale and complex Gravitational Wave detectors is building up. They will produce big amount of data and will require intensive and specific interactive/batch data analysis. We will present VEGA, a framework for such data analysis, based on ROOT. VEGA uses the Frame format defined as standard by GW groups around the world. Furthermore, new tools are developed in order to facilitate data access and manipulation, as well as interface with existing algorithms. VEGA is currently evaluated by the VIRGO experiment.

28pm1-E-2 Big Data Analysis of Highway Based on Sensor Data of an Accelerometer Integrated in a Vehicle

2015 ◽  
Vol 2015.7 (0) ◽  
pp. _28pm1-E-2-_28pm1-E-2
Author(s):  
Sana Talmoudi ◽  
Yoshio Takaeda ◽  
Tetsuya Kanada ◽  
Hiroki Kuwano
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Sensor Data

scholarly journals Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiangming Sun ◽  
Nina Jeliazkova ◽  
Vladimir Chupakhin ◽  
Jose-Felipe Golib-Dzib ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Large Scale Dataset

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

scholarly journals Big Data Applications in Social Media Research: The Experience of Unsuccessful Data Analysis of Ukrainian Facebook

2018 ◽  
pp. 94-105
Author(s):  
A. Tokarev
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Base ◽  
Research Question ◽  
Functional Limitations ◽  
State University ◽  
High Expectations ◽  
Big Data Applications ◽  
Primary Means ◽  
Academy Of Sciences

This article showcases a detailed description of the first stage of research on the discourse of Ukrainian opinion leaders on Facebook conducted by a team of researchers representing MGIMO University, Lomonosov Moscow State University, and Institute of Economy at the Russian Academy of Sciences. Convinced that it is Facebook that serves as the primary means of communication of politicians with the population in Ukraine, the team built a data base consisting of posts written over a 10-month period by 176 profiles belonging to the representatives of Ukrainian elites, and applied machine data analysis. The research question was the following: What strategies on the conflict in Donbas are verbalized by the Ukrainian elites? The author faced three challenges and limitations of machine data processing and analysis:unsuccessful operationalization of terms; functional limitations of the Semantic Archive Platform, of which the author turned out to have unreasonably high expectations; lack of understanding of peculiarities of Big Data analysis. Nevertheless, it was the failure of this pilot research that helped raise crucial questions for further research, primarily on the criteria for shaping a data base and on formulating of the research questions for software. This experience turned to be essential for second and third stages of the research project that were completed a year and half after the project was launched. Hence the necessity to make public all the considerations on this research.

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