Ground Transportation Big Data Analytics and Third Party Validation: Solutions for a New Era of Regulation and Private Sector Innovation

2018 ◽  
pp. 47-80
Author(s):  
Matthew W. Daus
Keyword(s):  
Big Data ◽  
Private Sector ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Third Party ◽  
New Era

AusGeochem and Big Data Analytics in Low-Temperature Thermochronology

2021 ◽  
Author(s):  
Samuel Boone ◽  
Fabian Kohlmann ◽  
Moritz Theile ◽  
Wayne Noble ◽  
Barry Kohn ◽  
...  
Keyword(s):  
Big Data ◽  
Low Temperature ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Application Programming Interface ◽  
Large Datasets ◽  
Geochemical Data ◽  
Advisory Group ◽  
New Era ◽  
Analytical Instruments

<p>The AuScope Geochemistry Network (AGN) and partners Lithodat Pty Ltd are developing AusGeochem, a novel cloud-based platform for Australian-produced geochemistry data from around the globe. The open platform will allow laboratories to upload, archive, disseminate and publish their datasets, as well as perform statistical analyses and data synthesis within the context of large volumes of publicly funded geochemical data. As part of this endeavour, representatives from four Australian low-temperature thermochronology laboratories (University of Melbourne, University of Adelaide, Curtin University and University of Queensland) are advising the AGN and Lithodat on the development of low-temperature thermochronology (LTT)-specific data models for the relational AusGeochem database and its international counterpart, LithoSurfer. These schemas will facilitate the structured archiving of a wide variety of thermochronology data, enabling geoscientists to readily perform LTT Big Data analytics and gain new insights into the thermo-tectonic evolution of Earth’s crust.</p><p>Adopting established international data reporting best practices, the LTT expert advisory group has designed database schemas for the fission track and (U-Th-Sm)/He methods, as well as for thermal history modelling results and metadata. In addition to recording the parameters required for LTT analyses, the schemas include fields for reference material results and error reporting, allowing AusGeochem users to independently perform QA/QC on data archived in the database. Development of scripts for the automated upload of data directly from analytical instruments into AusGeochem using its open-source Application Programming Interface are currently under way.</p><p>The advent of a LTT relational database heralds the beginning of a new era of Big Data analytics in the field of low-temperature thermochronology. By methodically archiving detailed LTT (meta-)data in structured schemas, intractably large datasets comprising 1000s of analyses produced by numerous laboratories can be readily interrogated in new and powerful ways. These include rapid derivation of inter-data relationships, facilitating on-the-fly age computation, statistical analysis and data visualisation. With the detailed LTT data stored in relational schemas, measurements can then be re-calculated and re-modelled using user-defined constants and kinetic algorithms. This enables analyses determined using different parameters to be equated and compared across regional- to global scales.</p><p>The development of this novel tool heralds the beginning of a new era of structured Big Data in the field of low-temperature thermochronology, improving laboratories’ ability to manage and share their data in alignment with FAIR data principles while enabling analysts to readily interrogate intractably large datasets in new and powerful ways.</p>

Architecture of modern platforms for big data analytics

2021 ◽  
pp. 67-74
Author(s):  
Liudmyla Zubyk ◽  
Yaroslav Zubyk
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Service Providers ◽  
Big Data Analytics ◽  
Business Value ◽  
Third Party ◽  
Unstructured Data ◽  
Business Decision ◽  
World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

scholarly journals Studying Cloud as IaaS for Big Data Analytics : Opportunity, Challenges

2018 ◽  
Vol 7 (2.7) ◽  
pp. 909
Author(s):  
Amitkumar Manekar ◽  
Dr Pradeepini Gera
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Infrastructure ◽  
Computational Framework ◽  
New Era ◽  
Post Process ◽  
James Watt ◽  
Data Ecosystem

James Watt steam engine revolution was greatest revolution in mankind history in 20th century. In 1776, the first steam engines were installed and working in commercial enterprises. This revolution minimize and make world smaller for human being, now world is connected seamlessly. “Big Data Analytics and Cloud” these two words are second numerous revolutions in 21st century.  We are living in an era of information explosion. These two magical terms are nothing but relatively very new and fortunately diverted all market trends to a new era of computation in last decade. As these two emerging technology are their early childhood, many people were confused with its relevancy and applicability. Cloud Computing is Infrastructure based solution for managing data and computational framework. 2016 was a significantly more important year for this volumes data technology or Big Data eco system as large number of enterprises, and organizations are generating data, storing that data and worried about future aspect of that data. In 2017, corporate world take cognizance of their large volumes structured and unstructured data as these enterprises and organizations continuously generating large volumes data. The term big data doesn’t just refer to the massive amounts of data existing today, it also refers to the whole ecosystem of Storing or gathering data, Different types of data and analyzing that data. In traditional data ecosystem all leverages are with legacy system.  Transforming or migration of these traditional ecosystems to the cloud is full of great challenges and benefits. Cloud computing is an agile and scalable resource access computation paradigm, provides heterogeneous platform seamlessly with infrastructure of internet, exclusively for the trapped and work on pre and post process of big data. Now the challenges are finding opportunity and challenges for managing, migrating and abstracting cloud based big data using cloud infrastructure for future eco system of Big Data Analysis.  This paper is basically focused on this issue. We try to reevaluate the facts of existing Cloud Infrastructure as IaaS for tomorrow’s big data analytics.    

scholarly journals Eco-Innovation and Industry 4.0: A Big Data Usage conceptual model

2018 ◽  
Vol 56 ◽  
pp. 05003 ◽  
Author(s):  
Russell Tatenda Munodawafa ◽  
Satirenjit Kaur Johl
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Economy ◽  
Future Research ◽  
New Era ◽  
Data Usage ◽  
The World ◽  
Development Goals

Driven by Cyber Physical Systems, Big Data Analytics, Internet of Things and Automation, Industry 4.0 is expected to revolutionize the world. A new era beckons for enterprises of all sizes, markets, governments, and the world at large as the digital economy fully takes off under Industry 4.0. The United Nations has also expressed its desire to usher in a new era for humanity with the Sustainable Development Goals 2030 (SDG’s) replacing the Millennial Development Goals (MDG’s). Critical to the achievement of both of the above-mentioned ambitions is the efficient and sustainable use of natural resources. Big Data Analytics, an important arm of Industry 4.0, gives organizations the ability to eco-innovate from a resource perspective. This paper conducts an analysis of previously published research literature and contributes to this emerging research area looking at Big Data Usage from a strategic and organizational perspective. A conceptual framework that can be utilized in future research is developed from the literature. Also discussed is the expected impact of Big Data Usage towards firm performance, particularly as the world becomes more concerned about the environment. Data driven eco-innovation should be in full motion if organizations are to remain relevant in tomorrow’s potentially ultra-competitive digital economy.

Is the private sector more efficient? Big data analytics of construction waste management sectoral efficiency

2020 ◽  
Vol 155 ◽  
pp. 104674 ◽  
Author(s):  
Jinying Xu ◽  
Weisheng Lu ◽  
Meng Ye ◽  
Fan Xue ◽  
Xiaoling Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Waste Management ◽  
Private Sector ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Construction Waste

scholarly journals Readiness to Adopt Big Data Analytics in Private Sector Companies

2020 ◽  
Vol 11 (2) ◽  
pp. 74
Author(s):  
W. J. A. J. M. Lasanthika ◽  
C. N. Wickramasinghe
Keyword(s):  
Big Data ◽  
Private Sector ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Readiness To Adopt

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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