scholarly journals MOSAICKING MEXICO - THE BIG PICTURE OF BIG DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 407-412
Author(s):  
F. Hruby ◽  
S. Melamed ◽  
R. Ressl ◽  
D. Stanley
Keyword(s):  
Big Data ◽  
Cloud Cover ◽  
Timely Manner ◽  
Shadow Elimination ◽  
Data Volume ◽  
Big Picture ◽  
Mosaic Generation ◽  
Processing Architectures ◽  
Innovative Techniques

The project presented in this article is to create a completely seamless and cloud-free mosaic of Mexico at a resolution of 5m, using approximately 4,500 RapidEye images. To complete this project in a timely manner and with limited operators, a number of processing architectures were required to handle a data volume of 12 terabytes. This paper will discuss the different operations realized to complete this project, which include, preprocessing, mosaic generation and post mosaic editing. Prior to mosaic generation, it was necessary to filter the 50,000 RapidEye images captured over Mexico between 2011 and 2014 to identify the top candidate images, based on season and cloud cover. Upon selecting the top candidate images, PCI Geomatics’ GXL system was used to reproject, color balance and generate seamlines for the output 1TB+ mosaic. This paper will also discuss innovative techniques used by the GXL for color balancing large volumes of imagery with substantial radiometric differences. Furthermore, post-mosaicking steps, such as, exposure correction, cloud and cloud shadow elimination will be presented.

scholarly journals MOSAICKING MEXICO - THE BIG PICTURE OF BIG DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 407-412 ◽  
Author(s):  
F. Hruby ◽  
S. Melamed ◽  
R. Ressl ◽  
D. Stanley
Keyword(s):  
Big Data ◽  
Cloud Cover ◽  
Timely Manner ◽  
Shadow Elimination ◽  
Data Volume ◽  
Big Picture ◽  
Mosaic Generation ◽  
Processing Architectures ◽  
Innovative Techniques

The project presented in this article is to create a completely seamless and cloud-free mosaic of Mexico at a resolution of 5m, using approximately 4,500 RapidEye images. To complete this project in a timely manner and with limited operators, a number of processing architectures were required to handle a data volume of 12 terabytes. This paper will discuss the different operations realized to complete this project, which include, preprocessing, mosaic generation and post mosaic editing. Prior to mosaic generation, it was necessary to filter the 50,000 RapidEye images captured over Mexico between 2011 and 2014 to identify the top candidate images, based on season and cloud cover. Upon selecting the top candidate images, PCI Geomatics’ GXL system was used to reproject, color balance and generate seamlines for the output 1TB+ mosaic. This paper will also discuss innovative techniques used by the GXL for color balancing large volumes of imagery with substantial radiometric differences. Furthermore, post-mosaicking steps, such as, exposure correction, cloud and cloud shadow elimination will be presented.

The Big Picture: Big Data Computing

CHANCE ◽  
2013 ◽  
Vol 26 (2) ◽  
pp. 28-32 ◽  
Author(s):  
Nicole Lazar
Keyword(s):  
Big Data ◽  
Big Picture ◽  
Big Data Computing

Big Data and Analytics

2020 ◽  
pp. 47-65
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Analytics ◽  
Data Warehousing ◽  
Big Data Analytics ◽  
Structured Data ◽  
Social Media Analytics ◽  
Data Set ◽  
Data Volume ◽  
Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

Big Data and Internet of Things for Analysing and Designing Systems Based on Hyperspectral Images

2018 ◽  
pp. 240-260
Author(s):  
Peyakunta Bhargavi ◽  
Singaraju Jyothi
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Future Internet ◽  
Urban Environments ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
The Internet ◽  
Sensors And Actuators ◽  
Fully Integrated ◽  
Data Volume

The recent development of sensors remote sensing is an important source of information for mapping and natural and man-made land covers. The increasing amounts of available hyperspectral data originates from AVIRIS, HyMap, and Hyperion for a wide range of applications in the data volume, velocity, and variety of data contributed to the term big data. Sensing is enabled by Wireless Sensor Network (WSN) technologies to infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The communication network creates the Internet of Things (IoT) where sensors and actuators blend with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). With RFID tags, embedded sensor and actuator nodes, the next revolutionary technology developed transforming the Internet into a fully integrated Future Internet. This chapter describes the use of Big Data and Internet of the Things for analyzing and designing various systems based on hyperspectral images.

Blending Technology, Human Potential, and Organizational Reality

Big Data ◽  
2016 ◽  
pp. 1422-1451
Author(s):  
Jurgen Janssens
Keyword(s):  
Big Data ◽  
Added Value ◽  
Human Potential ◽  
Project Approach ◽  
True Value ◽  
Big Picture ◽  
Data Ecosystem ◽  
Definition Of ◽  
Complementary Actions ◽  
Organizational Reality

To make the deeply rooted layers of catalyzing technology and optimized modelling gain their true value for education, healthcare or other public services, it is necessary to prepare well the Big Data environment in which the Big Data will be developed, and integrate elements of it into the project approach. It is by integrating and managing these non-technical aspects of project reality that analytics will be accepted. This will enable data power to infuse the organizational processes and offer ultimately real added value. This chapter will shed light on complementary actions required on different levels. It will be analyzed how this layered effort starts by a good understanding of the different elements that contribute to the definition of an organization's Big Data ecosystem. It will be explained how this interacts with the management of expectations, needs, goals and change. Lastly, a closer look will be given at the importance of portfolio based big picture thinking.

A Hierarchical Security Model for Multimedia Big Data

Big Data ◽  
2016 ◽  
pp. 441-453
Author(s):  
Min Chen
Keyword(s):  
Big Data ◽  
Access Control ◽  
Multimedia Data ◽  
Temporal Constraints ◽  
Security Model ◽  
Multimedia Big Data ◽  
Role Hierarchy ◽  
Data Volume ◽  
Spatio Temporal ◽  
Role Based

In this chapter, the author proposes a hierarchical security model (HSM) to enhance security assurance for multimedia big data. It provides role hierarchy management and security roles/rules administration by seamlessly integrating the role-based access control (RBAC) with the object-oriented concept, spatio-temporal constraints, and multimedia standard MPEG-7. As a result, it can deal with challenging and unique security requirements in the multimedia big data environment. First, it supports multilayer access control so different access permission can be conveniently set for various multimedia elements such as visual/audio objects or segments in a multimedia data stream when needed. Second, the spatio-temporal constraints are modeled for access control purpose. Finally, its security processing is efficient to handle high data volume and rapid data arrival rate.

Big Data Quality for Data Mining in Business Intelligence Applications

2021 ◽  
pp. 64-91
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Quality ◽  
Business Intelligence ◽  
Future Research ◽  
Quality Factors ◽  
Data Volume ◽  
Mining Algorithms ◽  
Data Mining Application

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

scholarly journals Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research

2019 ◽  
pp. 089443931988845 ◽  
Author(s):  
Alexander Christ ◽  
Marcus Penthin ◽  
Stephan Kröner
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Predictive Modeling ◽  
Hot Spots ◽  
Quantitative Research ◽  
Hot Spot ◽  
Future Research ◽  
Original Research ◽  
Cultural Education ◽  
Data Volume

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE.

scholarly journals Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Haipeng Peng ◽  
Ye Tian ◽  
Jürgen Kurths
Keyword(s):  
Big Data ◽  
Compressive Sensing ◽  
Data Transmission ◽  
Matrix Multiplication ◽  
Wireless Sensor ◽  
Storage Space ◽  
Secret Key ◽  
Encrypt Data ◽  
The Matrix ◽  
Data Volume

Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption.

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