Traditional Data Management Systems Research Articles

Reference architecture design for developing data management systems in smart farming

The traditional data management systems prove inadequate to handle the volume, velocity, and variety of the data within farm business processes. Smart farming technologies offer advanced data management systems as a practical solution to these challenges. However, data is complex and originates from many sources; hence many aspects of data must be considered during the data management design of smart farming systems. This study proposes a reference architecture for data management in smart farming, developed through domain analysis and architecture modeling approaches. The domain analysis provides insights into the common and variant features and modules of the smart farming system, resulting in a blueprint representing family features across various smart farming domains. The effectiveness of the proposed reference architecture has been evaluated through two case studies, demonstrating its efficacy in designing data management systems for smart farming. The study found that the percentage of reused modules in the case studies, compared to the provided reference architecture, was 82.6%. The outcomes of this research will pave the way for further exploration in smart farming, particularly addressing data management issues within smart farming systems.

Blockchain Based MediChain System Connecting Pharma, Medicals and Patients Together.

In today’s world almost all hospital uses hardcopy for patient data store and for booking appointment. All patient data is available on paper and user need to manage that all over he goes. Consider ’A’ patient got admitted in City Pune, all his data is stored on paper and what medicines he took. All the info about his health is stored there. After someday ‘A’ patient gone to another city far away without carrying any documents and there he got and health emergency and is not in condition of speaking and need urgently help. But due to lack of info available about patient doctor could not urgently take decisions. So, to reduce this risk our system is developed. The distribution of Health records becomes a time consuming and expensive process when we use the traditional client-server healthcare data management system where each hospital/clinic maintains its own database of patients’ medical records. A patient’s treatment is further delayed if the patient moves from one hospital to another hospital across different regions or countries. Moreover, most of the time a patient must repeat several laboratory and radiology tests. So, to address this the patient’s medical data from different hospitals are stored in a Blockchain based storage making it easily accessible by patients and the hospitals. And the pharma company will be able to store the information of medicines and medicals will be available to verify the medicines if they are from trustworthy companies. Keyword - - Dapp, Web3.Js, CSS.

Securing Construction Workers’ Data Security and Privacy with Blockchain Technology

The construction industry, characterized by its intricate network of stakeholders and diverse workforce, grapples with the challenge of managing information effectively. This study delves into this issue, recognizing the universal importance of safeguarding data, particularly amid rising concerns around unauthorized access and breaches. Aiming to harness the potential of blockchain technology to address these challenges, this study used hypothetical biographical and safety data of construction workers securely stored on a Hyperledger Fabric blockchain. Developed within the Amazon Web Services (AWS) cloud platform, this blockchain infrastructure emerged as a robust solution for enhancing data security and privacy. Anchored in the core principles of data security, the model emerges as a potent defender against the vulnerabilities of traditional data management systems. Beyond its immediate implications, this study exemplifies the marriage of blockchain technology and the construction sector, and its potential for reshaping workforce management, especially in high-risk projects and optimizing risk assessment, resource allocation, and safety measures to mitigate work-related injuries. Practical validation through transaction testing using Hyperledger Explorer validates the model’s feasibility and operational effectiveness, thus serving as a blueprint for the industry’s data management. Ultimately, this research not only showcases the promise of blockchain technology in addressing construction data security challenges but also underscores its practical applicability through comprehensive testing, thus heralding a new era of data management that harmonizes security and efficiency for stakeholders’ benefit.

BLOCKCHAIN UTILIZATION IN THE EDUCATION SECTOR: PROSPECTS AND CHALLENGES

Blockchain technology has been attracting attention as a potential solution to address many issues associated with traditional data management systems. The education sector is also exploring its potential to include improving various aspects of education. One of the opportunities of blockchain technology in education is secure and tamper-proof digital record-keeping, which can eliminate the need for physical certificates and transcripts, making the credentialing process more efficient, accurate, and transparent. Blockchain can facilitate micro-credentialing for lifelong learning, creating new opportunities for individuals to acquire and verify specific skills or knowledge. Additionally, transparent and secure funding and donations can be established using blockchain technology, which will help to eliminate fraud and ensure that donations are used for their intended purpose. However, there are also significant challenges that need to be addressed to make Blockchain a reality in education. These include concerns around the scalability and interoperability of blockchain networks, the need for standards and regulations to ensure the reliability and accuracy of data, and the potential costs and technical expertise required to implement and maintain blockchain systems. In addition, digital literacy and data privacy concerns need to be addressed to ensure that students, teachers, and other stakeholders are equipped to use blockchain technology effectively and responsibly. While the opportunities and challenges of Blockchain in education are significant, careful consideration and collaboration are needed to fully realise its potential. Thus, this paper provides the reader with a solid background about Blockchain and delves into its opportunities and challenges to be utilised in education.

Conceptual modeling of Big Data extraction phase

As the amount of information exceeds the management and storage capacity of traditional data management systems, several domains need to take into account this growth of data, in particular the decision-making domain known as Business Intelligence (BI). Since the accumulation and reuse of these massive data stands for a gold mine for businesses, several insights that are useful and essential for effective decision making have to be provided. However, it is obvious that there are several problems and challenges for the BI systems, especially at the level of the ETL (Extraction-Transformation-Loading) as an integration system. These processes are responsible for the selection, filtering and restructuring of data sources in order to obtain relevant decisions. In this research paper, our central focus is especially upon the adaptation of the extraction phase inspired from the first step of MapReduce paradigm in order to prepare the massive data to the transformation phase. Subsequently, we provide a conceptual model of the extraction phase which is composed of a conversion operation that guarantees obtaining NoSQL structure suitable for Big Data storage, and a vertical partitioning operation for presenting the storage mode before submitting data to the second ETL phase. Finally, we implement through Talend for Big Data our new component which helps the designer extract data from semi-structured data.

FIR Security System Using Blockchain Technology

Abstract: FIR (First Information Report) datamanagement is a critical task for law enforcement agencies to maintain law and order. Traditional FIR data management system often plagued by issues of data tampering, unauthorized access, and lack of transparency. In order to overcome this blockchain technology has been implemented which offers a decentralized, secure, and transparent solution to these problems. Storing FIR data on the blockchain can provide several advantages, including increased security, transparency, and immutability. Blockchain technology is a decentralized system that is resistant to modification, making it ideal for storing sensitive data such as FIR data. This system proposes a blockchain-based FIR data management system that utilizes smart contracts to automate the process of registering and accessing FIR data. The immutability of blockchain records can increase the reliability and trustworthiness of FIR data, while the decentralization of the blockchain ensures that there is no single point of failure. The user interface provides a graphical user interface for authorized users to interact with the system, Crime Investigators can view the data from database using their authentication credentials. Thereports, which are prepared by witnesses and police officers, are accessible to the investigator (admin).Investigators have the authority to edit data (i.e., update,remove, and so on), and this data aids investigators in speeding up their investigations and identifying offendersmore quickly. The evidences are secured using Block chain technology with SHA algorithm to create the hash values for each record. Secure cryptography functions to encrypt FIR information before storing on server has beenimplemented. The blockchain technology has the potential to revolutionize FIR data storage and improve the efficiency and effectiveness of law enforcement agencies.

Developing scalable management information system with big financial data using data mart and mining architecture

The traditional Management Information System (MIS) with Big Financial Data (BFD) for corporate financial diagnosis has many limitations such as the data is not summarized thus these causing increases in query times, and also the complexity in analysis. The creation of a Data Mart (DM) leads to a great summarization of data, such that contains only essential business information. And by using data mining techniques we can be extracting unknown useful information from DM and apply it to make important decisions for the business. Thus, in this paper we are adopting an architecture of six layers; interface layer, analysis layer, extract transformation load layer, data mart layer, data mining layer, and evaluating layer, MIS with BFD using DM and Mining (MIS-BFD-DMM) is proposed, which is not only permits the use of DM and mining technologies in decision support, but also the full utilization of non-financial/financial info held by businesses. This paper offers the benefits of building and integrating DM with mining. Also determines the distinction between DM and a relational database for decision-makers to get information. The test and analysis are achieved in the terms of useful metrics (accuracy, balance accuracy, F-measure, precision, recall, and time). As a result, Data returned from arranged star schema is far faster than ERD. In conclusion, the SVM is best than other algorithms in terms of the parameters of the confusion matrix.

Ethereum Blockchain using AES-CMAC

Managing and storing data are crucial tasks for any industry, since they require accurate and secure record-keeping. Using blockchain technology has significantly increased recently due to its capability to address some of the challenges that come with traditional data storage and management systems. In this paper, we will explore the use of Ethereum smart contracts powered by the Solidity programming language using AES-CMAC.

Using Big Data Technology to Protect the Environment

In the context of digitalization of all aspects of the surrounding world, data is becoming particularly relevant as one of the most valuable resources. The concept of "big data" means a huge amount of information, the size of which is too large, or it is created too quickly or has a structuring that does not allow it to be processed using traditional data management systems. Currently, large amounts of data and analytics are increasingly used by government agencies, non-governmental organizations and private companies in the field of environmental protection. The range of practical use of this technology is quite wide: from improving energy efficiency, tracking climate change over long periods of time, monitoring water quality, and ending with the promotion of environmental justice. This article describes several extremely promising applications of large data sets and their analytics, which can help achieve the goals of environmental protection and sustainable development, provide environmental benefits, help research on the environment, its conservation and protection. The widespread adoption of big data processing solutions allows us to illustrate the range of initiatives and approaches to reduce the environmental burden used by government agencies, non-governmental organizations and private companies.

Coordinated Development of Urban Intelligent Transportation Data System and Supply Chain Management

With the development of the urban economy, the number of people using various means of transportation is also increasing, resulting in a huge workload of the traffic data system and prone to errors. Supply chain management can formulate a reasonable production plan according to the comprehensive information data generated by the supply chain management system, such as market demand analysis, purchasing demand analysis, and supplier assessment and evaluation. Therefore, this paper proposes the coordinated development of urban intelligent transportation data system and supply chain management, so as to improve the overall efficiency of the logistics system and the level of customer service. This paper aims to study the importance and advantages of the coordinated development of urban intelligent transportation data system and supply chain management. As can be seen from the data in Table 2, the percentage of people using a car increased by 18.2% in 2015, and by 2020, the percentage of people using a car increased by 36.9%. As shown in Figure 10, the traditional urban traffic data management system has the disadvantages of large amount of data and various and complex data types. Among them, the percentage of large amount of data is between 70% and 75%, and the percentage of diverse and complex data is between 62% and 68%. It can be seen that the number of people using cars is increasing, resulting in an increasing workload for the transportation system. On this basis, the intelligent traffic data system should be used to solve this problem and the coordinated development of the intelligent traffic data system and supply chain management can achieve a win-win situation.

FROM SHARING TO SELLING: CHALLENGES AND OPPORTUNITIES OF ESTABLISHING A DIGITAL HEALTH DATA MARKETPLACE USING BLOCKCHAIN TECHNOLOGIES.

During the COVID-19 pandemic, we witnessed how sharing of biological and biomedical data facilitated researchers, medical practitioners, and policymakers to tackle the pandemic on a global scale. Despite the growing use of electronic health records (EHRs) by medical practitioners and wearable digital gadgets by individuals, 80% of health and medical data remain unused, adding little value to the work of researchers and medical practitioners. Legislative constraints related to health data sharing, centralized siloed design of traditional data management systems, and most importantly, lack of incentivization models are thought to be the underpinning bottlenecks for sharing health data. With the advent of the General Data Protection Regulation (GDPR) of the European Union (EU) and the development of technologies like blockchain and distributed ledger technologies (DLTs), it is now possible to create a new paradigm of data sharing by changing the incentivization model from current authoritative or altruistic form to a shared economic model where financial incentivization will be the main driver for data sharing. This can be achieved by setting up a digital health data marketplace (DHDM). Here, we review papers that proposed technical models or implemented frameworks that use blockchain-like technologies for health data. We seek to understand and compare different technical challenges associated with implementing and optimizing the DHDM operation outlined in these articles. We also examine legal limitations in the context of the EU and other countries such as the USA to accommodate any compliance requirement for such a marketplace. Last but not least, we review papers that investigated the short-, medium-, and long-term socioeconomic impact of such a marketplace on a wide range of stakeholders.

Fast detection of denial constraint violations

The detection of constraint-based errors is a critical task in many data cleaning solutions. Previous works perform the task either using traditional data management systems or using specialized systems that speed up error detection. Unfortunately, both approaches may fail to execute in a reasonable time or even exhaust the available memory in the attempt. To address the main drawbacks of previous approaches, we present the FAst Constraint-based Error DeTector (FACET) to detect violations of denial constraints (DCs). FACET uses column sketch information to organize a pipeline of special operators for DC predicates and it implements these operators using a set of efficient algorithms and data structures that adapt to different data characteristics and predicate structures. We evaluate our system on a diverse array of datasets and constraints, showing its robustness and performance gains compared to different types of DBMSs and to a specialized system.

ByShard

The emergence of blockchains has fueled the development of resilient systems that can deal with Byzantine failures due to crashes, bugs, or even malicious behavior. Recently, we have also seen the exploration of sharding in these resilient systems, this to provide the scalability required by very large data-based applications. Unfortunately, current sharded resilient systems all use system-specific specialized approaches toward sharding that do not provide the flexibility of traditional sharded data management systems. To improve on this situation, we fundamentally look at the design of sharded resilient systems. We do so by introducing BYSHARD, a unifying framework for the study of sharded resilient systems. Within this framework, we show how two-phase commit and two-phase locking ---two techniques central to providing atomicity and isolation in traditional sharded databases---can be implemented efficiently in a Byzantine environment, this with a minimal usage of costly Byzantine resilient primitives. Based on these techniques, we propose eighteen multi-shard transaction processing protocols. Finally, we practically evaluate these protocols and show that each protocol supports high transaction throughput and provides scalability while each striking its own trade-off between throughput, isolation level, latency , and abort rate. As such, our work provides a strong foundation for the development of ACID-compliant general-purpose and flexible sharded resilient data management systems.

BGL Group: Artificial Intelligence (AI) Strategy

This research presents a detailed case analysis of BGL Group, a leading, international, distributor of insurance and household financial services. The AI strategy is described by analysing and evaluating a set of AI applications covering a variety of business areas: 1. Machine learning for pricing; 2. Chatbot AI technology to improve the customer experience in e-service; 3. Customer experience design thinking and a/b testing in new product development; 4. Voice recognition and Natural Language Processing (NLP) in call centre operations; 5. AI techniques for market segmentation. Each application is described in detail, and the concept of value creation in service markets is illustrated using data flow diagrams of customer interactions for different stages of the customer journey. A benefits matrix model is proposed that captures the principal AI benefits to both the supplier and the customer. The case discussion uses a new model, an AI systems map, to describe and explain the overall landscape of current AI applications, traditional Management Information Systems (MIS) and possible future application areas based on broad AI strategies and cognitive AI/thinking machines. Some concluding remarks are made on the importance of a digital first culture, up-to-date digital infrastructure and technology partnerships for successful implementation of AI systems, the crucial role of big data in AI strategies, and the growing importance of AI ethics in business applications. Finally, some propositions are offered regarding the future direction of AI in insurance markets.

On data lake architectures and metadata management

Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are mainly characterized by volume, velocity, variety and veracity issues. Big data-related issues strongly challenge traditional data management and analysis systems. The concept of data lake was introduced to address them. A data lake is a large, raw data repository that stores and manages all company data bearing any format. However, the data lake concept remains ambiguous or fuzzy for many researchers and practitioners, who often confuse it with the Hadoop technology. Thus, we provide in this paper a comprehensive state of the art of the different approaches to data lake design. We particularly focus on data lake architectures and metadata management, which are key issues in successful data lakes. We also discuss the pros and cons of data lakes and their design alternatives.

Research on the Construction of Green Management Information System for Sustainable Development

Faced with the fact that sustainable development has gradually become the common goal of all mankind, the traditional management information system has been difficult to adapt to this historical trend, so it is of great significance to study green management information system in the context of the new era. Starting from the concept of green, this paper combines the theory and method of life cycle and management information system development to theoretically discuss the greening of management information system planning and development. Based on the full consideration of economic and environmental benefits, this paper proposes the relevant methods to realize the greening of management system planning and development. And the related methods of green development have certain reference significance for the implementation process of green concept and management information system integration.

Big Data Hadoop Tools and Technologies: A Review

The word ‘big data’ represents a huge volume of data which can be in a structured, semi-structured, unstructured form. Big Data is generated in a very large amount making it difficult to process using traditional techniques. In order to process these generated data traditional big data management system is inefficient to manage the large amount of data. Hadoop is a framework which is designed to process the large data sets and provides high performance, fault tolerance from a single server to thousands of machines. In this paper we describe a detailed review of big data and Hadoop along with a comparison of various tools/technologies of big data.

A Review on Hadoop Eco System for Big Data

In this era of information age, a huge amount of data generates every moment through various sources. This enormous data is beyond the processing capability of traditional data management system to manage and analyse the data in a specified time span. This huge amount of data refers to Big Data. Big Data faces numerous challenges in various operations on data such as capturing data, data analysis, data searching, data sharing, data filtering etc. HADOOP has showed a big way of various enterprises for big data management. Big data hadoop deals with the implementation of various industry use cases. To master the Apache Hadoop, we need to understand the hadoop eco system and hadoop architecture. In this paper we brief on the Hadoop architecture and hadoop eco system.

Access control technologies for Big Data management systems: literature review and future trends

Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.

Product and supply chain related data, processes and information systems for product portfolio management

Traditional product data management (PDM) systems have evolved to support the product lifecycle management (PLM). The combination, also referred to as PDM/PLM has developed into a product master data (PMD) repository. The PMD and business process related data are utilised in product portfolio management (PPM). However, companies' tendency to have productrelated data in silos among multiple business processes and information systems (IS) results in uncertain information for PPM. This study focuses on data, processes and information systems related to PMD and supply chain product data (SCPD), PPM and supply chain (SC) processes and information systems. The study highlights the related challenges in providing fact-based data for PPM analysis and decision-making. The results indicate that the key PMD and SCPD have not been connected back to PPM as automated and integrated data flow from enterprise resource planning (ERP) system to the PDM/PLM system. The key SCPD consists of product-related volume and cost information that should be linked to PPM analysis and decision making. These findings are critical to further develop data, processes and IS to support strategic and financial PPM analysis and decision making on what products a company should have in the portfolio.