Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms

David Haynes; Philip Mitchell; Eric Shook

doi:10.3390/ijgi9110690

Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110690 ◽

2020 ◽

Vol 9 (11) ◽

pp. 690

Author(s):

David Haynes ◽

Philip Mitchell ◽

Eric Shook

Keyword(s):

Big Data ◽

Web Applications ◽

Raster Data ◽

Mobile Web ◽

New Knowledge ◽

Comprehensive Performance ◽

The World ◽

Raster Analysis ◽

And Performance ◽

Patterns And Processes

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust assessment comparing the efficiency of raster data analysis on big data platforms. This research begins to address this issue by establishing a raster data benchmark that employs freely accessible datasets to provide a comprehensive performance evaluation and comparison of raster operations on big data platforms. The benchmark is critical for evaluating the performance of spatial operations on big data platforms. The benchmarking datasets and operations are applied to three big data platforms. We report computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three raster different datasets.

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Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

10.20944/preprints202008.0504.v1 ◽

2020 ◽

Author(s):

David Haynes ◽

Philip Mitchell ◽

Eric Shook

Keyword(s):

Big Data ◽

Web Applications ◽

Mobile Web ◽

New Knowledge ◽

The World ◽

Raster Analysis ◽

Common Framework ◽

Comprehensive Comparison ◽

And Performance ◽

Patterns And Processes

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allows researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are also at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust framework to assess the capabilities of geospatial analysis on big data platforms. This research begins to address this issue by establishing a geospatial benchmark that employs freely accessible datasets to provide a comprehensive comparison across big data platforms. The benchmark is a critical for evaluating the performance of spatial operations on big data platforms. It provides a common framework to compare existing platforms as well as evaluate new platforms. The benchmark is applied to three big data platforms and reports computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three different datasets.

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HTML5 and the Mobile Web

International Journal of Innovation in the Digital Economy ◽

10.4018/jide.2012040104 ◽

2012 ◽

Vol 3 (2) ◽

pp. 40-56 ◽

Cited By ~ 1

Author(s):

Kevin Curran ◽

Aaron Bond ◽

Gavin Fisher

Keyword(s):

World Wide Web ◽

Mobile Devices ◽

Web Sites ◽

World Wide ◽

Development Process ◽

Web Applications ◽

Mobile Web ◽

Advantages And Disadvantages ◽

The World ◽

Uniform Solution

Accessing the Web from mobile devices is a popular practice. Trends show that the mobile space is becoming the method through which many consumers access content – both through native and web applications. These applications have expanded the browsing experience, but have also complicated the development process. A need exists for a simple, uniform solution which HTML5 is attempting to address. HTML is a mark-up language, now on its fifth edition, used for structuring and presenting content on the World Wide Web. Because of the large increase in users of mobile devices, internet access on these devices is widely used. The creation of web sites, web documents, and applications are done with HTML5, as it is compatible with both PC and mobile device browsers. However, with its lengthy development process, it is not yet apparent if HTML5 can incorporate the demands of developers in a changing environment. This paper provides an overview of the use of HTML5 in structuring and presenting content on the World Wide Web and compatibility issues on mobile browsers, key features, tool’s, and the advantages and disadvantages on the mobile web devices as well as the state of the mobile web.

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HR Challenges in Big Data

Ushus - Journal of Business Management ◽

10.12725/ujbm.35.4 ◽

2016 ◽

Vol 15 (2) ◽

pp. 49-55

Author(s):

Pala SuriyaKala ◽

Ravi Aditya

Keyword(s):

Big Data ◽

Driving Force ◽

Data Analytics ◽

Performance Metrics ◽

Data Sets ◽

Human Capital Management ◽

The World ◽

To Come ◽

And Performance ◽

Talent Acquisition

Human resources is traditionally an area subject to measured changes but with Big data, data analytics, Human capital Management, Talent acquisition and performance metrics as new trends, there is bound to be a sea change in this function. This paper is conceptual and tries to introspect and outline the challenges that HRM faces in Big Data. Big Data is as one knows the world of enormous generation which is revolutionizing the world with data sets at exabytes. This has been the driving force behind how governments, companies and functions will come to perform in the decades to come. The immense amount of information if properly utilized can lead to efficiency in various fields like never before. But to do this the cloud of suspicion, fear and uncertainty regarding the use of Big Data has to be removed from those who can use it to the benefit of their respective areas of application.HR traditionally has never been very data centric in the analysis of its decisions unlike marketing, finance, etc.

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An Analysis of Security and Performance Concerns in Mobile Web Application Development: Challenges and Open Issues

International Journal of Recent Contributions from Engineering Science & IT (iJES) ◽

10.3991/ijes.v5i3.7330 ◽

2017 ◽

Vol 5 (3) ◽

pp. 26

Author(s):

Douglas Kunda ◽

Mumbi Chishimba ◽

Mwenge Mulenga ◽

Victoria Chama

Keyword(s):

Web Application ◽

Web Applications ◽

Application Development ◽

Mobile Web ◽

Web Application Development ◽

And Performance ◽

Session Hijacking ◽

Application Developers ◽

Cross Site ◽

Open Issues

The paper focuses on security and performance concerns in mobile web development. The approach used in the study involved surveying journal publications to identify security and performance concerns. The paper highlights some of the contemporary issues currently being faced by application developers as they create, update and maintain mobile web applications including Cross-Site Scripting, Cookie hijacking/theft, location hijacking, history theft, behaviour analysis, session hijacking, API design, security and the type of web server used considered.

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Comparison between SQL and NoSQL Databases and Their Relationship with Big Data Analytics

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2019/v4i230108 ◽

2019 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Wajid Ali ◽

Muhammad Usman Shafique ◽

Muhammad Arslan Majeed ◽

Ali Raza

Keyword(s):

Big Data ◽

Data Storage ◽

Data Analytics ◽

Relational Databases ◽

Web Applications ◽

Distributed Storage ◽

Big Data Analytics ◽

Nosql Databases ◽

Advantages And Disadvantages ◽

The World

A key ingredient in the world of cloud computing is a database that can be used by a great number of users. Distributed storage mechanisms become the de-facto method for data storage used by companies for the new generation of web applications. In the world of data storage, NoSQL (usually interpreted as "not only SQL" by developers) database is a growing trend. It is said that NoSQL alternates with the most widely used relational databases for the data storage, but, as the name implies, it does not fully replace the SQL. In this paper we will discuss about SQL and NoSQL databases, comparison of traditional SQL with NoSQL databases for Big Data analytics, NoSQL data models, types of NoSQL data stores, characteristics and features of each data store, advantages and disadvantages of NoSQL and RDBMS.

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Demystifying Big Data in the Cloud

Geo-Intelligence and Visualization through Big Data Trends - Advances in Geospatial Technologies ◽

10.4018/978-1-4666-8465-2.ch011 ◽

2015 ◽

pp. 264-304 ◽

Cited By ~ 1

Author(s):

Gebeyehu Belay Gebremeskel ◽

Yi Chai ◽

Zhongshi He

Keyword(s):

Big Data ◽

Data Processing ◽

Web Applications ◽

Privacy And Security ◽

Web Resources ◽

Corporate Security ◽

Widespread Adoption ◽

Novel Approaches ◽

And Performance ◽

Cloud Infrastructures

Big data in the cloud are an emerging paradigm for huge and federated data processing, storing and distributing by deploying web applications. Scalability, elasticity, pay-per-use pricing, and an advance of ICT scale from large and dynamic applications and performance are the major reasons for the success and widespread adoption of big data cloud infrastructures. It is ‘no secret of the enterprise data', which is challenging for privacy and security. In this chapter, authors deeply discussed and introduce novel approaches and methodologies to easily understood big data phenomenon and technology towards data or web resources privacy and security. Nutshell, big data has a powerful potential to predict cloud risks to develop and deploy corporate security strategies. The chapter's contribution is, in general, to gain a meaningful insight of big data in the cloud and its applications, which is hot issues for today's businesses to make proactive and knowledge-driven decisions.

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Demystifying Big Data in the Cloud

Web-Based Services ◽

10.4018/978-1-4666-9466-8.ch088 ◽

2016 ◽

pp. 2001-2031

Author(s):

Gebeyehu Belay Gebremeskel ◽

Yi Chai ◽

Zhongshi He

Keyword(s):

Big Data ◽

Data Processing ◽

Web Applications ◽

Privacy And Security ◽

Web Resources ◽

Corporate Security ◽

Widespread Adoption ◽

Novel Approaches ◽

And Performance ◽

Cloud Infrastructures

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The beginning of phygital world

South Asian Journal of Engineering and Technology ◽

10.26524/sajet.2020.2.7 ◽

2020 ◽

Vol 10 (2) ◽

pp. 1-4

Author(s):

Evgeny Soloviov ◽

Alexander Danilov

Keyword(s):

Big Data ◽

Learning Styles ◽

21St Century ◽

Digital Technology ◽

Aviation Education ◽

Digital World ◽

The World ◽

Unique Experience ◽

Smart Data

The Phygital word itself is the combination pf physical and digital technology application.This paper will highlight the detail of phygital world and its importance, also we will discuss why its matter in the world of technology along with advantages and disadvantages.It is the concept and technology is the bridge between physical and digital world which bring unique experience to the users by providing purpose of phygital world. It is the technology used in 21st century to bring smart data as opposed to big data and mix into the broader address of array of learning styles. It can bring new experience to every sector almost like, retail, medical, aviation, education etc. to maintain some reality in today’s world which is developing technology day to day. It is a general reboot which can keep economy moving and guarantee the wellbeing of future in terms of both online and offline.

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