The Research of Component-Based Software Development Application on Data Management in Smart Education

<p style="text-align: justify;"><span style="font-family: "times new roman";">In the context of big data, with the rapid development of cloud computing and mobile networks, smart education has opened up the era of digitization, networking and smart cloud. Smart learning, smart education, interactive online classrooms and personalized learning have become new trends in future education development. The era of big data has had a great impact on my country's education field. A variety of network technologies and smart classrooms have been widely used in the teaching process, realizing large-scale reforms of traditional education models. The realization of the smart education platform can provide students with a learning platform with a personalized learning experience and a good smart learning atmosphere, which greatly improves the traditional teaching and learning mode. This article first analyzes the key points of smart education and provides the design principles of smart math education platform. In order to promote smart education and smart learning, secondly, it designs and implements an open and shared interactive smart math education platform. Provide specific reference materials for researchers in smart service, smart management, smart evaluation, and smart education.</span>

The rapid advancement of information technology and ongoing education reforms have introduced smart education as an innovative teaching model. This study aims to explore the application and impact of smart education platforms in Teaching English to Speakers of Other Languages (TESOL) within four junior high schools in China. Through a detailed analysis of questionnaire responses from English language teachers, the study investigates the impact of these platforms on teaching practices. Results indicate that using smart teaching platforms has increased teachers&amp;rsquo; engagement, motivation, and satisfaction. Additionally, teachers have reported a deeper understanding of smart education concepts, improved practical teaching skills, and overall professional development. Despite these benefits, the study highlights the challenges of implementing smart education platforms. The findings suggest that while smart education platforms can potentially enhance TESOL effectiveness, further improvements, and extensive research are necessary to optimize their use fully.

This paper scrutinizes the integral role of Trans-Disciplinary Communication (TDC) in conceptualizing and implementing Data Management and Sharing (DMS) plans in alignment with federal guidelines such as those from NIH and NSF. We begin by dissecting existing federal mandates for research data management, elucidating their pivotal role in maintaining data integrity and enabling accessibility. Using a case study as our methodological approach, we exemplify how TDC can effectively enhance the dialogue and practices surrounding DMS plans, fulfilling the requirements imposed by funding organizations. Furthermore, this research offers a novel contribution to the ongoing discourse by highlighting how DMS plans are foundational in promoting effective TDC and, by extension, advancing discussions in areas like Diversity Equity and Inclusion (DEI), STEM Education, and Collaboration.

Ethics in digital resource processing and access are strongly coupled with human computer interaction because they produce trust between user and system. Over the past decades, we have seen a rapid increase of digital born resources in business applications where i.e. different stakeholders, ranging from patients, doctors to Data Scientists must cope with ethical aspects and regulations. Amongst others, innovative Model View Controller (MVC) based data management systems in the healthcare sector are analysing and managing an ever-growing, vast amount of sensitive data, enabling its efficient and comprehensive use in decision-making processes. E.g. in data-driven collaboration processes like drug development, caregiving or clinical patient monitoring. MVC is one of the most popular architectural patterns (Holzinger et.al 2010), which is the reason to investigate this design pattern further in terms of ethical aspects within human computer intractable systems. However, new regulations, like the GDPR have a huge impact on sensitive data management. However, ethical regulations could not first be considered by those who make use of information system functionalities, but must be an inherent part of considerations during software development process. This short paper will bring up and discuss ethical issues in decision making systems system software development, along with an existing ethic by design framework in the scope of health-related research projects.

New generation of health care IT systems are collecting and storing more and more data of patients. Useful knowledge can be extracted from the data in EMR or PHR to provide medical advises to patients, while through data analysis the result statistics can be used to support the scientific research. However, RDBMSs-based framework is not able to support the requirements of massive health care data storage, management and analysis. To solve the problem, this paper proposes a massive data management and analysis solution based on Hadoop to archive better performance, scalability and fault tolerance. The data management framework is presented. Besides, 2 different data analysis methods based on MapReduce and Hive are proposed. Experiment results of data upload, data query and data analysis show that the performance of the proposed framework is greatly improved, and a brief summary of the performance and the differences between 2 methods of MapReduce and Hive is also discussed.

Nowadays organizations are often inspired to become data-driven enterprises that continuously and systematically use data (from internal and external sources) to inform strategic decision-making and actions to improve operational efficiencies, products, services, customer experience, and competitiveness. Such use of data implies that organizational actors across different organizational functions are making decisions based on information derived from analytical insights. Yet, many companies find the journey to becoming a data-driven enterprise a bumpy ride. A key issue faced by most firms is that their data management approach is not ready for data and analytics-based decision-making. In this teaching case, we follow the journey of an industrial enterprise, PETRONAS, attempting to transform into a data-driven enterprise. We report on four key data-driven ‘blockers’ encountered by PETRONAS Downstream and provide insights into steps taken to address these data challenges. This case study is suitable for courses that explore digital transformation and data management in an organizational context, from strategic and operational perspectives. For example, instructors teaching a Digital Transformation course might use this case for a session dedicated to learning about data-readiness for digital transformation. It is intended for postgraduate students (Master and MBA levels) as well as late-stage undergraduate students. Specific angles that could be explored in depth are: identifying and capturing data-related requirements and issues from the perspectives of different organizational actors; examining interdependencies between functional and technical aspects of data management and use (e.g., requirements, availability, sustainability of data supply) for fast and reliable decision-making; sourcing of data: evaluating in-house and outsourced approaches for data sourcing and management; implementation of data-related projects and change management.

In this paper, we design a pluggable data management solution for modern mobile platforms (e.g., Android). Our goal is to allow data management mechanisms and policies to be implemented independently of core app logic. Our design allows a user to install data management solutions as apps, install multiple such solutions on a single device, and choose a suitable solution each for one or more apps. It allows app developers to focus their effort on app logic and helps the developers of data management solutions to achieve wider deployability. It also gives increased control of data management to end users and allows them to use different solutions for different apps. We present a prototype implementation of our design called BlueMountain, and implement several data management solutions for file and database management to demonstrate the utility and ease of using our design. We perform detailed microbenchmarks as well as end-to-end measurements for files and databases to demonstrate the performance overhead incurred by our implementation.

Component-Based Software Engineering (CBSE) is the concept of software development using existing components. However, the components themselves have many forms in the software. Some studies suggest that components are executable. In building an application, components are needed in different forms so that it can be used not only in an application but can also be used on a website. This study aims to produce another form of a component in the Model-View-Controller (MVC) based software model which can later be implemented in website development. In this case, the case is taken in a vehicle testing system application. The component model itself uses the CBSE approach by utilizing several UML diagrams in its design. First, use usecase to show the pattern of user interaction with the system being built. Second, activity diagrams to illustrate the flow of work activities that occur between actors and systems. Third, component diagrams to illustrate how the relationships between components. The model produced in this study is a component in a different form that can be placed in a library in web programming using the PHP language and in accordance with the CBSE concept.

Watch the VIDEO here. Presenter – Maria SöderholmResearch data management (RDM) is a complex and dynamic topic, and demands diverse expertise, skills and knowledge. The RDM expertise includes subtopics like collection/provision of data; storage and processing of data; long-term preservation requirements of the data; and funders’ demands and solutions to share, re-find and re-use research data. Usually the expertise related to these RDM subtopics is spread to several university units, both academic and administrative. Therefore, many tasks related to RDM, for example, day-to-day practices, the supply of services and the development work are best carried out in a network-based cooperation.In the presentation, we will introduce our RDM related partnership and networking of Aalto University. As a starting point, we introduce the internal service development working principles that our RDM network work is based on. However, the focus will be on describing our RDM working group and development activities.Aalto’s Research Data Management Programme forms the backbone for RDM work. It establishes seven separate action points for RDM activities: 1) open access publishing; 2) implementation of data management planning tool; 3) metadata catalogue for open data; 4) data publishing; 5) comprehensive repository service for storage, back-up and collaboration; 6) combining openness and innovation; and 7) RDM awareness building activities. The actors in the RDM network consist of Research and Innovation Services (leading the group), IT Services, and Learning Centre (previously Library).In the presentation, we will introduce the core actors in more detail, paying attention to the complementarity of the roles; and the activities and the aims, which steer the work. We also discuss the meaning and consequences of the network-based cooperation for the working group.First, the RDM group acts as a joint platform for comprehensive RDM information. Important means of data gathering are surveys and informal discussion with researcher. In the future, formal group discussions on RDM needs are hosted. The previous discussions with researchers have underlined the importance of arranging services for both disciplinary and data specific needs in addition to common university level service needs.Second, the group is a messenger of the RDM topics. Our task is to rise internal RDM awareness and disseminate national and international information and development trends in the university.The third and the most challenging aim is the RDM service planning and delivery. Our task is to identify the existing in-house services, to map the suitable services provided by national and international agents as well as to recognize the needs for new services. Our service portfolio covers both consultation/informational services and technical, hands-on services. However, many of our research data services are still in the planning or piloting stage, thus cooperation with researchers is essential.In our experience, the network-based collaboration model that foster individuals’ interconnectedness is crucial for surviving with the built-in dynamism of RDM. This model provides a non-hierarchical and flexible environment for actions to meet the increasing expectations for research data services we face from the funders, governments, and researchers.

Grounded in the theory of multi-modal discourse analysis, this paper conducts an in-depth exploration of the current challenges encountered in college English smart teaching. Leveraging the online smart teaching platform, it maximizes the exploitation and utilization of multi-modal teaching resources, seamlessly integrates multidisciplinary knowledge, and actively pursues the online and offline integration of the college English smart teaching system. Initiating from the four dimensions of smart teaching environment creation, teaching mode construction, teaching method innovation, and teaching evaluation enhancement, a comprehensive and systematic overall design for college English multi-modal smart teaching will be executed. The aim is to fully propel the profound integration of information technology and college English teaching, break through the crucial bottlenecks in the implementation of “Internet + Education”, enhance the quality and effectiveness of college English teaching in an all-round and multi-level manner, furnish potent theoretical support and practical exemplars for the reform and development of college English teaching, and contribute to the realization of the talent cultivation objectives in higher education.

Early detection of infant hearing loss in the private health care sector of South Africa

PurposeEffective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM).Design/methodology/approachA literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle.FindingsIt is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure.Research limitations/implicationsThe system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry.Originality/valueThis paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.

Fog computing, a novel computing paradigm, migrates cloud computing to the edge of network and consequently decrease the overhead of process and movement of the large-scale big data. Fog computing is promising to lower communication delay and offload network traffic with the scale of big data increasing in large-scale IoT application, which brings micro-data centers to the network edge, enabling the network edge to perceive, process, storage and calculate the massive data, reducing the amount of transferred data. However, numerous fog nodes at the network edge face an external attack resulting in serious security threats arising from the trusting relationship vulnerability because of the lack of the consideration of the properties of the fog computing system. This paper addresses the design for developing secure distributed data management platform for fog computing in large-scale IoT application, along with a blockchain-based data management implementation of the platform, which approaches the major challenges: how to realize the integration of data security and storage management for fog computing in large-scale IoT application and enrich rational interoperability for interconnected things. Experimental results show that the system works well to empower data provenance and transparency and defend against unauthorized access effectively in fog computing, scales well with loss of the performance of communication and computing maintaining in acceptable range, verifying the efficiency and feasibility of our design to provide privacy, integrity and fine-grained data management for fog computing in large-scale IoT application by introducing the data management platform based upon the blockchain network and smart contracts therein.

The idea of digital health ecosystems development will be discussed in terms of four interdependent perspectives, including engineering smart devices, legal systems, business models and education strategies.The study uses mixed methods and includes secondary data sources such as academic publications, policy papers, and industry reports. This approach aims to identify the current changes in digital health care worldwide and the challenges that come with them. Results show that smart devices, wearables, and IoMT systems can offer real-time monitoring and preventative care in about 60% of cases when used in high-income countries. However, these technologies still struggle with interoperability and cybersecurity problems. Legal systems vary significantly by region. The EU’s GDPR provides a decent level of privacy protection, but the new requirements for implementation hinder innovation. Meanwhile, India is working on the Digital Personal Data Protection Act (2023) but has yet to address issues with enforcing the law. In the telemedicine sector, business models are shifting. Subscription models are growing by around 25% each year, along with hybrid models. However, value-based models have great potential if data management and oversight practices improve significantly. Successful adoption depends on effective educational strategies, as research indicates that over 40% of healthcare professionals do not have training in digital health. This highlights the urgent need to integrate such training into the curriculum. The paper concludes that digital health ecosystems can only become resilient, fair, and sustainable through the collaboration of technology, law, business, and education.

The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud - XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.