Big Data and Analytics in Government Organizations

2017 ◽  
pp. 3-25
Author(s):  
Matthew Chegus
Keyword(s):  
Big Data ◽  
Government Organizations

scholarly journals Data Lake Ecosystem Workflow

2021 ◽  
Author(s):  
R. Salter ◽  
Quyen Dong ◽  
Cody Coleman ◽  
Maria Seale ◽  
Alicia Ruvinsky ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Lake Ecosystem ◽  
Data Governance ◽  
Government Organizations ◽  
Large Scale Data ◽  
Scale Data

The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

scholarly journals Artificial Intelligence Driven Resiliency with Machine Learning and Deep Learning Components

2019 ◽  
Vol 4 (2) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Homeland Security ◽  
Decision Makers ◽  
Actual Behavior ◽  
Direct Impact ◽  
Data Infrastructure ◽  
Government Organizations ◽  
The Right ◽  
Real Challenge

The future of any business from banking, e-commerce, real estate, homeland security, healthcare, and marketing, the stock market, manufacturing, education, and retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the Big Data (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations’ customers. The decision-makers need to get vital insights into the customers’ actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics.

Big data in government organizations: A review

2017 ◽  
Author(s):  
Pwint Phyu Khine ◽  
Wang Zhao Shun
Keyword(s):  
Big Data ◽  
Government Organizations

Analysis of Big Data Using Two Mapper Files in Hadoop

2021 ◽  
Vol 13 (1) ◽  
pp. 69-77
Author(s):  
Jyotsna Malhotra ◽  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
New York ◽  
Big Data ◽  
Stock Price ◽  
Stock Exchange ◽  
Uncertain Data ◽  
Real Life ◽  
Big Data Analytics ◽  
Private Companies ◽  
Government Organizations ◽  
York Stock Exchange

Nowadays, a large amount of valuable uncertain data is easily available in many real-life applications. Many industries and government organizations can exploit this data to extract valuable information. This information can help the managers to enhance their strategies and optimize their plans in making decisions. In fact, various private companies and governments have launched programs with investments and funds in order to maximize profits and optimize resources. This vast amount of data is called big data. The analysis of big data is important for future growth. This paper depicts big data analytics through experimental results. In this paper, data for New York stock exchange has been analyzed using two mapper files in Hadoop. For each year, the calculation of maximum and minimum price of every stock exchange and the average stock price is done.

scholarly journals Empathic Cultural Mapping: Little data, big data, knowledge transfer and exchange

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Suzanne Goopy ◽  
Tanvir Turin ◽  
Anusha Kassan ◽  
Mary O'Brien ◽  
Gavin McCormack ◽  
...  
Keyword(s):  
Big Data ◽  
Large Data ◽  
Community Empowerment ◽  
Social Environments ◽  
Healthy City ◽  
Creative Processes ◽  
Government Organizations ◽  
Story Map ◽  
Statistics Canada ◽  
The University

IntroductionA healthy city is one that continually creates and improves psychosocial and social environments and expands community resources allowing people to develop to their maximum portential. The role of SEDoHs is incontestable, yet we continue to face many of the same SEDoH-related problems despite what we know. Objectives and ApproachThis project presents the idea of "Empathic Cultural Mapping" (ECM). ECM is an interactive story map which brings together vignettes taken from individual stories curated with "big data" derived from places such as Statistics Canada, the City of Calgary, and library holdings at the University of Calgary. ECM seeks to challenge users to re-imagine long held constructions around sectoral, and disciplinary driven interpretations and categorizations of lifestyle, consumption, health, and the environment. ECM seeks to encourage knowledge users from multiple sectors to think beyond what is known and to consider what might be possible. ResultsECM is a creative interactive undertaking. In developing ECM, a range of creative research processes have been used to record and tell the stories of a small group of newcomers (defined as those who migrate, seek refuge, or claim asylum in Canada) and position these within large data. A desire to improve the health and wellbeing of individuals and communties through opening processes of dialogoue between local government, non-government organizations, communitites, and individuals lies at the heart of this project. Knowledge and sense-making are key features of individual and community empowerment within the ECM and are viewed as powerful stimuli for change as well as powerful allies for health and a buffer against its threats. Conclusion/ImplicationsECM creates, shares, and brings together individual stories and 'big data'. It identifies needs that impact health in the everyday. It seeks to improve awareness of the world around us. It encourages people to communicate their experiences. Finally, it achieves its goals by using creative processes.

scholarly journals Environmental Activism and Big Data: Building Green Social Capital in China

2020 ◽  
Vol 12 (8) ◽  
pp. 3386
Author(s):  
Jing Xu ◽  
Huijun Zhang
Keyword(s):  
Social Capital ◽  
Big Data ◽  
Multinational Corporations ◽  
Information And Communication Technologies ◽  
Environmental Governance ◽  
Rapid Development ◽  
Social Capital Theory ◽  
Capital Theory ◽  
Government Organizations ◽  
Information And Communication

The rapid development of information and communication technologies, coupled with the significant progress in the areas of environmental policy and public participation, has led to the advent of environmental big data in China recently. This article applies social capital theory as an analytical lens to shed light on how Chinese environmental non-government organizations (ENGOs) adopt big data to promote environmental governance. This study conducts case studies focusing on two ENGOs: The Institute of Public and Environmental Affairs (IPE) and Green Hunan. Combining a qualitative approach with quantitative analysis, this research examines two big data-induced initiatives: The first involves green supply chain management in the IPE, brand-sensitive multinational corporations (MNCs), and Chinese suppliers of the MNCs, while the second involves the mobile data-based Riverwatcher Action Network of Green Hunan and numerous volunteers nationwide. This study found that big data adoption by ENGOs contributes effectively to building green social capital, including social networks and pro-environmental social norms. Green social capital has important implications for governance in terms of fostering coordination and cooperation across the boundaries of the public, private, and voluntary sectors. This study highlighted the finding that empowerment by big data helps Chinese ENGOs play the role of a change agent in sustainability transitions.

Transparency and Enhanced Efficiency and Accountability Due to Big Data Adoption in Government Agencies and Other Enterprises

2016 ◽  
pp. 38-58
Author(s):  
Dhiraj Jain ◽  
Yuvraj Sharma
Keyword(s):  
Corporate Governance ◽  
Factor Analysis ◽  
Big Data ◽  
Thematic Analysis ◽  
Descriptive Statistics ◽  
Government Agencies ◽  
Government Organizations ◽  
The Government ◽  
Structured Questionnaire

Pertinence of big data is necessary especially in the field of corporate governance where large amount of data is collected, stored, retrieved and manages. The major challenge arises for the government organizations therefore are how to use the breadth and depth of the large amount of available data in an appropriate manner. The purpose of the study was to find out the relevance of Big Data in corporate governance and investigates about the role and reasons behind adopting this technique in various government schemes. The data was collected from the 393 respondents of IT companies through a pre-tested and a structured questionnaire. Thematic analysis, descriptive statistics and factor analysis were used to explain the factors needed to identify transparency and enhanced efficiency & accountability by big data adoption. It was found there were an explosion of big data in the corporate governance and various activities of the government which could be highly relevant.

scholarly journals State of the art in image processing & big data analytics: issues and challenges

2018 ◽  
Vol 7 (3.3) ◽  
pp. 195 ◽  
Author(s):  
S Vahini Ezhilraman ◽  
Sujatha Srinivasan
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Vital Role ◽  
Computer Application ◽  
Image Processing Techniques ◽  
Government Organizations ◽  
Image Science ◽  
Processing Techniques

Image processing, in the contemporary domain, is now emerging as a novel and an innovative space in computing research and applications. Today, the discipline of “computer science” may be termed as “image science”, why because in every aspect of computer application, either science or humanities or management, image processing plays a vital role in varied ways. It is broadly now used in all the industries, organizations, administrative divisions; various social organizations, economic/business institutions, healthcare, defense and so on. Image processing takes images as input and image processing techniques are used to process the images and the output is modified images, video, or collection of text, or features of the images. The resultant output by most image processing techniques creates a huge amount of data which is categorized as Big-data. In this technique, bulky information is processed and stored as either structured or unstructured data as a result of processing images through computing techniques. In turn, Big Data analytics for mining knowledge from data created through image processing techniques has a huge potential in sectors like education, government organizations, healthcare institutions, manufacturing units, finance and banking, centers of retail business. This paper focuses on highlighting the recent innovations made in the field of image processing and Big Data analytics. The integration and interaction of the two broad fields of image processing and Big Data have great potential in various areas. Research challenges identified in the integration and interaction of these two broad fields are discussed and some possible research directions are suggested. 

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

U.S., Canada Collaborate on Big Data in ASD Research

ASHA Leader ◽  
2015 ◽  
Vol 20 (12) ◽  
pp. 16-16
Keyword(s):  
Big Data
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