PABED A Tool for Big Education Data Analysis

Cloud computing and big data have risen to become the most popular technologies of the modern world. Apparently, the reason behind their immense popularity is their wide range of applicability as far as the areas of interest are concerned. Education and research remain one of the most obvious and befitting application areas. This research paper introduces a big data analytics tool, PABED Project Analyzing Big Education Data, for the education sector that makes use of cloud-based technologies. This tool is implemented using Google BigQuery and R programming language and allows comparison of undergraduate enrollment data for different academic years. Although, there are many proposed applications of big data in education, there is a lack of tools that can actualize the concept into practice. PABED is an effort in this direction. The implementation and testing details of the project have been described in this paper. This tool validates the use of cloud computing and big data technologies in education and shall head start development of more sophisticated educational intelligence tools

NOSQL – a solution for big data storage issues

Из-за роста объемов данных и усложнения структуры хранимых данных потребовались новые методы построения инфраструктуры баз данных. Сейчас использование одного сервера является дорогостоящим и сложным. В связи с этим используют облачные хранилища данных. Также новые гибкие методы позволил обрабатывать запросы быстрее. Это достигается с помощью двух подходов: ручной шардинг и распределенный кэш. Таким образом появились системы NoSQL. Они имеют ряд преимуществ перед традиционными базами данных. NoSQL – общий термин, который описывает множество технологий, которые обладают общими характеристиками. NoSQL является общим решением некоторых проблем, которые возникают при организации хранения данных. Due to the growth of data volumes and the complexity of the structure of stored data, new methods for building a database infrastructure were required. Using one server is expensive and complicated right now. In this regard, they use cloud data storage. Also, new flexible methods allowed processing requests faster. This is achieved using two approaches: manual sharding and distributed cache. Thus, NoSQL systems appeared. They have several advantages over traditional databases. NoSQL is a generic term that describes many technologies that share common characteristics. NoSQL is a common solution to some of the problems that arise when organizing data storage

Cloud Computing for Supply Chain Management and Warehouse Automation: A Case Study of Azure Cloud

In recent times, organizations are examining the art training situation to improve the operation efficiency and the cost of warehouse retail distribution and supply chain management. Microsoft Azure emerges as an expressive technology that leads optimization by giving infrastructure, software, and platform resolutions for the whole warehouse retail distribution and supply chain management. Using Microsoft Azure as a cloud computing tool in retail warehouse distribution and supply manacle management contributes to active and monetary benefits. At the same time, potential limitations and risks should be considered by the retail warehouse distribution and the supply chain administration investors. In this research summary of the cloud figuring tool, both public and hybrid in supply chain administration and retail, warehouse distribution is addressed. A brief introduction to the use of Microsoft Azure technology is provided. This is followed by the application of cloud computing to warehouse retail distribution and supply chain management activities. At the same time, the negative and positive aspects of familiarizing this Microsoft Azure technology in the modern supply chain and retail distribution are debated. Also, the circumstance for the third-party logistics services suppliers has indicated respect for automation and cybersecurity solutions in a cloud environment. Lastly, the upcoming research practices and following technological trends are offered as the conclusion

Enhancing Institutional Assessment and Reporting Through Conversational Technologies: Exploring the Potential of AI-Powered Tools and Natural Language Processing

This study explores the potential of conversational technologies, AI-powered tools, and natural language processing (NLP) in enhancing institutional assessment and reporting processes in higher education. The traditional approach to assessment often involves labor-intensive manual analysis of extensive data and documents, which burdens institutions. To address these challenges, AI-powered tools, such as ChatGPT, LangChain, Poe, Claude, and others, along with NLP techniques, are investigated in relationship to their ability to improve institutional assessment practices and output. By leveraging these advanced technologies, assessment officers and institutional effectiveness, researchers can engage in dynamic conversations with data, transforming spreadsheets and documents from static artifacts into interactive resources. These tools streamline communication, collaboration, and decision-making processes, empowering committees and working groups to achieve their goals effectively. Additionally, the potential applications of NLP in analyzing vast amounts of institutional data, including student feedback, faculty evaluations, and institutional documents, shall be discussed. Language models enable the extraction of meaningful insights from unstructured data sources, facilitating real-time decision-making processes. Ethical considerations related to data privacy, mining, and compliance with regulations like FERPA are crucial aspects addressed in this study. The contribution of this research lies in uncovering the transformative impact of conversational technologies, AI-powered tools, and NLP techniques on institutional assessment and reporting. By embracing these advancements responsibly and ensuring alignment with ethical principles, institutions can unlock the full potential of these tools, facilitating more efficient, data-driven decision-making processes in higher education. The study showcases how conversational technologies, AI-powered tools, and NLP techniques offer new possibilities for improving institutional assessment and reporting practices. By integrating these technologies responsibly and addressing ethical considerations, institutions can enhance their assessment processes and make more informed decisions based on comprehensive, real-time insights

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed