Physical Sciences and Mathematics Commons^™

Design Of Personnel Big Data Management System Based On Blockchain, Houbing Song, Jian Chen, Zhihan Lv

Houbing Song

With the continuous development of information technology, enterprises, universities and governments are constantly stepping up the construction of electronic personnel information management system. The information of hundreds of thousands or even millions of people’s information are collected and stored into the system. So much information provides the cornerstone for the development of big data, if such data is tampered with or leaked, it will cause irreparable serious damage. However, in recent years, electronic archives have exposed a series of problems such as information leakage, information tampering, and information loss, which has made the reform of personnel information management more and …

The Graph Database: Jack Of All Trades Or Just Not Sql?, George F. Hurlburt, Maria R. Lee, George K. Thiruvathukal

George K. Thiruvathukal

This special issue of IT Professional focuses on the graph database. The graph database, a relatively new phenomenon, is well suited to the burgeoning information era in which we are increasingly becoming immersed. Here, the guest editors briefly explain how a graph database works, its relation to the relational database management system (RDBMS), and its quantitative and qualitative pros and cons, including how graph databases can be harnessed in a hybrid environment. They also survey the excellent articles submitted for this special issue.

Vetcompass Australia: A National Big Data Collection System For Veterinary Science, Paul Mcgreevy, Peter Thomson, Navneet K. Dhand, David Raubenheimer, Sophie Masters, Caroline S. Mansfield, Timothy Baldwin, Ricardo J. Soares Magalhaes, Jacquie Rand, Peter Hill, Anne Peaston, James Gilkerson, Martin Combs, Shane Raidal, Peter Irwin, Peter Irons, Richard Squires, David Brodbelt, Jeremy Hammond

Paul McGreevy, PhD

Knowledge As A Service Framework For Disaster Data Management, Katarina Grolinger, Emna Mezghani, Miriam Capretz, Ernesto Exposito

Katarina Grolinger

Each year, a number of natural disasters strike across the globe, killing hundreds and causing billions of dollars in property and infrastructure damage. Minimizing the impact of disasters is imperative in today’s society. As the capabilities of software and hardware evolve, so does the role of information and communication technology in disaster mitigation, preparation, response, and recovery. A large quantity of disaster-related data is available, including response plans, records of previous incidents, simulation data, social media data, and Web sites. However, current data management solutions offer few or no integration capabilities. Moreover, recent advances in cloud computing, big data, and …

Big Data: New Opportunities And New Challenges, Katina Michael, Keith Miller

Associate Professor Katina Michael

We can live with many of the uncertainties of big data for now, with the hope that its benefits will outweigh its harms, but we shouldn't blind ourselves to the possible irreversibility of changes—whether good or bad—to society.

It's no secret that both private enterprise and government seek greater insights into people's behaviors and sentiments. Organizations use various analytical techniques—from crowdsourcing to genetic algorithms to neural networks to sentiment analysis—to study both structured and unstructured forms of data that can aid product and process discovery, productivity, and policy-making. This data is collected from numerous sources including sensor networks, government data …

Big Data: New Opportunities And New Challenges, Katina Michael, Keith W. Miller

Keith Miller

We can live with many of the uncertainties of big data for now, with the hope that its benefits will outweigh its harms, but we shouldn't blind ourselves to the possible irreversibility of changes—whether good or bad—to society.

It's no secret that both private enterprise and government seek greater insights into people's behaviors and sentiments. Organizations use various analytical techniques—from crowdsourcing to genetic algorithms to neural networks to sentiment analysis—to study both structured and unstructured forms of data that can aid product and process discovery, productivity, and policy-making. This data is collected from numerous sources including sensor networks, government data …

Profile Transparency By Design? Re-Enabling Double Contingency, Mireille Hildebrandt

Mireille Hildebrandt

No abstract provided.