Data Storage Systems Commons^™

The Impact Of Student Engagement Activities On Future Climate Change Adaptation: The Case Of Student Simulation Models, Bassel Mostafa Elkalaf

Future Journal of Social Science

This paper explores the critical role of student engagement in addressing the growing challenges of climate change, with a focus on the Model United Nations (MUN) as a case study. As climate-related security threats increase globally, educational platforms that prepare youth for effective leadership in climate politics are more essential than ever. MUN, a widely practiced student activity simulating global policy-making, provides a valuable opportunity for students to deepen their understanding of the interconnectedness between climate change, peace, and security. By participating in MUN simulations, students engage in debates, develop innovative solutions, and practice diplomatic skills, all while exploring the …

Big Ideas, Small Data: Opportunities And Challenges For Data Science And The Social Services Sector, Gerri Dimas, Lauri Goldkind, Renata Konrad

Social Service Faculty Publications

The social services sector, comprised of a constellation of programs meeting critical human needs, lacks the resources and infrastructure to implement data science tools. As the use of data science continues to expand, it has been accom- panied by a rise in interest and commitment to using these tools for social good. This commentary examines overlooked, and under-researched limitations of data science applications in the social sector—the volume, quality, and context of the available data that currently exists in social service systems require unique considerations. We explore how the presence of small data within the social service contexts can result …

Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu

Modeling, Simulation and Visualization Student Capstone Conference

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, …

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

Publications

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 …

Big Data Investment And Knowledge Integration In Academic Libraries, Saher Manaseer, Afnan R. Alawneh, Dua Asoudi

Copyright, Fair Use, Scholarly Communication, etc.

Recently, big data investment has become important for organizations, especially with the fast growth of data following the huge expansion in the usage of social media applications, and websites. Many organizations depend on extracting and reaching the needed reports and statistics. As the investments on big data and its storage have become major challenges for organizations, many technologies and methods have been developed to tackle those challenges.

One of such technologies is Hadoop, a framework that is used to divide big data into packages and distribute those packages through nodes to be processed, consuming less cost than the traditional storage …

A Study Of Scalability And Cost-Effectiveness Of Large-Scale Scientific Applications Over Heterogeneous Computing Environment, Arghya K. Das

LSU Doctoral Dissertations

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data.

In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures …

Special Issue: Neutrosophic Information Theory And Applications, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophiclogic,symboliclogic,set,probability,statistics,etc.,are,respectively,generalizations of fuzzy and intuitionistic fuzzy logic and set, classical and imprecise probability, classical statistics, and so on. Neutrosophic logic, symbol logic, and set are gaining significant attention in solving many real-life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistency, and indeterminacy. A number of new neutrosophic theories have been proposed and have been applied in computational intelligence, multiple-attribute decision making, image processing, medical diagnosis, fault diagnosis, optimization design, etc. This Special Issue gathers original research papers that report on the state of the art, as well as on recent advancements in neutrosophic information theory in soft computing, artificial intelligence, …

A Distributed Intrusion Detection Model Via Nondestructive Partitioning And Balanced Allocation For Big Data, Xiaonian Wu, Chuyun Zhang, Runlian Zhang, Yujue Wang, Jinhua Cui

Research Collection School Of Computing and Information Systems

There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as …

Comparative Analysis Of Big Data Analytics Software In Assessing Sample Data, Soly Mathew Biju, Alex Mathew

Journal of International Technology and Information Management

Over the last few years, big data has emerged as an important topic of discussion in most firms owing to its ability of creation, storage and processing of content at a reasonable price. Big data consists of advanced tools and techniques to process large volumes of data in organisations. Investment in big data analytics has almost become a necessity in large-sized firms, particularly multinational companies, for its unique benefits, particularly in prediction and identification of various trends. Some of the most popular big data analytics software used today are MapReduce, Hive, Tableau and Hive, while the framework Hadoop enables easy …

When Machines Decide: Creating Justice.Exe, Randy Dryer, Suresh Venkatasubramanian, Austin Anderson, Abigail Busath, Logan Cox, Morgan Cox, Logan Erickson, Zachary Grena, Joseph Hutchins, Skyler Jayson, Andrew Yang

Law School Historical Documents

Our Praxis Lab centered on the increasingly important need for algorithmic literacy, transparency and oversight, particularly in the context of the use of algorithms in the criminal justice system. Accordingly, we (1) designed an educational simulation to illustrate the function of algorithms in the context of criminal sentencing; (2) created several pre-packaged course modules that faculty in a variety of disciplines could integrate into their existing curriculum to educate students about algorithms and their increasing role in modern society; and (3) curated a set of best practices designed to promote the transparency and oversight of algorithmic systems.

Creating Best Bathymetry Using The Iqmulus High Volume Fusion And Analysis Platform For Geospatial Point Clouds, Quillon Harpham, Jennifer Herbert, Vibeke Skytt

International Congress on Environmental Modelling and Software

New data acquisition techniques are emerging and are providing a fast and efficient means for multidimensional spatial data collection. Single and multi-beam echo-sounders, airborne LIDAR, SAR satellites and mobile mapping systems are increasingly used for the digital reconstruction of the environment. All these systems provide point clouds, often enriched with other sensor data providing extremely high volumes of raw data. With these acquisition approaches, a great deal of data is collected, but it often requires harmonisation and integration before reaching its maximum use potential. For use in modelling waves and flow in seas and oceans, collections of surveys of water …

Systems Approach To Link Big Socio-Ecological Geo-Data To Food Systems Sustainability, Quang Bao Le, Chandrashekhar Biradar, Enrico Bonaiuti, Richard Thomas

International Congress on Environmental Modelling and Software

Rapid development of multi-dimensionally, multi-scale, timely updated socio-ecological geo-data presents an opportunity as potential information resource for supporting effective decision- making of stakeholders involved with food systems, from daily routines of individuals to strategic decisions of manager and policy-makers. However, the use of this type of big data toward supporting the sustainability of food systems at different scales still fall short of (i) what information commonly needed by food system actors to response and adapt to socio-ecological change and enhance the system performance, (ii) interoperability between different types of data across scales, and (iii) sufficient guidance to utilize big data …

Factors Affecting Big Data Technology Adoption, Nayem Rahman

Student Research Symposium

With the advancement of computer science, hardware and software engineering, and computing power, and later with the advent of the internet, social networking tools and other sources such as sensors data growth has increased significantly. These data are called big data which are mostly unstructured, generated in large volumes, data need to be captured in near real-time. To handle big data a completely new set of tools and technologies are being emerged. I have studied big data literature to identify the factors that might influence big data adoption. I was able to list quite a few factors or attributes that …

Defining A Smart Nation: The Case Of Singapore, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose - The purpose of this paper is to identify the key characteristics and propose a working definition of a smart nation.Design/methodology/approach - A case study of Singapore through an analysis of the key speeches made by senior Singapore leaders, publicly available government documents and news reports since the launch of the smart nation initiative in December 2014 was carried out.Findings - Just like smart cities, the idea of a smart nation is an evolving concept. However, there are some emerging characteristics that define a smart nation.Research limitations/implications - The paper provides an initial understanding of the key characteristics and …

Ranked Similarity Search Of Scientific Datasets: An Information Retrieval Approach, Veronika Margaret Megler

Dissertations and Theses

In the past decade, the amount of scientific data collected and generated by scientists has grown dramatically. This growth has intensified an existing problem: in large archives consisting of datasets stored in many files, formats and locations, how can scientists find data relevant to their research interests? We approach this problem in a new way: by adapting Information Retrieval techniques, developed for searching text documents, into the world of (primarily numeric) scientific data. We propose an approach that uses a blend of automated and curated methods to extract metadata from large repositories of scientific data. We then perform searches over …

A Cris Data Science Investigation Of Scientific Workflows Of Agriculture Big Data And Its Data Curation Elements, Benjamin D. Branch, Peter N. Baker, Jai Xu, Elisa Bertino

Libraries Faculty and Staff Presentations

This joint collaboration between the Purdue Libraries and Cyber Center demonstrates the next generation of computational platforms supporting interdisciplinary collaborative research. Such platforms are necessary for rapid advancements of technology, industry demand and scholarly congruence towards open data, open access, big data and cyber-infrastructure data science training. Our approach will utilize a Discovery Undergraduate Research Investigation effort as a preliminary research means to further joint library and computer science data curation research, tool development and refinement.

Evaluation And Analysis Of Distributed Graph-Parallel Processing Frameworks, Yue Zhao, Kenji Yoshigoe, Mengjun Xie, Suijian Zhou, Remzi Seker, Jiang Bian

Publications

A number of graph-parallel processing frameworks have been proposed to address the needs of processing complex and large-scale graph structured datasets in recent years. Although significant performance improvement made by those frameworks were reported, comparative advantages of each of these frameworks over the others have not been fully studied, which impedes the best utilization of those frameworks for a specific graph computing task and setting. In this work, we conducted a comparison study on parallel processing systems for large-scale graph computations in a systematic manner, aiming to reveal the characteristics of those systems in performing common graph algorithms with real-world …