CLOUD-NATIVE DATA ANALYTICS PLATFORM WITH INTEGRATED GOVERNANCE: A MODERN APPROACH TO REAL-TIME STREAM PROCESSING AND FEATURE ENGINEERING

This paper addresses data management threats, challenges, and measures for mitigation of such data threats in the telecommunication industry. The telecommunications industry handles tonnes of essential, personal and sensitive data on staff, clients and customers. This data is critical in developing product lines and marketing strategies for telecom companies, opening them up to opportunities that otherwise would have been beyond their reach. As a result, telecom companies’ databases are rendered vulnerable to violation and unauthorised access from insiders, third-party entities, as well as hackers. They have become premium target for cyberattacks, malware, interference, data theft and loss of vital data. While systems for the management of data in the telecom industry are crucial in gathering, storing, and analysing data, it is equally important for such systems to be efficient enough to provide a safe and secure mode of accessing, analysing and storing data assets. This is to prevent illegitimate use of sensitive data, cyberattacks, malware and data infringements on telecom data assets.

As enterprises undergo rapid digital transformation, the volume, velocity, and variety of data have grown exponentially, leading to unprecedented challenges in data management, security, and compliance. Traditional, manual data governance frameworks are no longer sufficient to handle the scale and complexity of modern data ecosystems, often resulting in data silos, poor data quality, and regulatory non-compliance. This paper proposes a comprehensive AI-Driven Unified Data Governance Framework designed for modern enterprise platforms. By integrating artificial intelligence (AI) and machine learning (ML) at the core of the data governance lifecycle, the proposed framework automates critical functions such as data discovery, classification, quality assurance, lineage tracking, and policy enforcement. We present a layered architectural model that seamlessly operates across hybrid and multi-cloud environments, supporting paradigms like data mesh and data fabric. Through four detailed enterprise case studies spanning financial services, healthcare, global retail, and smart manufacturing, we demonstrate the empirical benefits of AI-driven governance, including up to 72% reduction in compliance reporting time and significant improvements in data quality and operational efficiency. Furthermore, we provide a comparative analysis of leading enterprise governance platforms and introduce a six-level AI Governance Maturity Model. Finally, the paper explores future trends, including the integration of Large Language Models (LLMs), autonomous self-healing data pipelines, and federated learning, providing a strategic technology roadmap for the next decade of enterprise data governance.

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

In today's digital era, the mortgage industry is undergoing a significant transformation, driven by advancements in data analytics and cloud computing technologies. Mortgage originators are increasingly relying on advanced analytics to optimize various aspects of their operations, from lead generation to risk management. By leveraging cloud computing, these originators gain access to real-time mortgage data analytics and reporting capabilities, enabling them to make informed decisions, detect market trends, and enhance operational efficiency while mitigating compliance risks. Despite the benefits, the industry faces traditional challenges in data management, including the integration of disparate data sources, data security, and compliance concerns. However, cloud computing offers solutions to these challenges by providing scalability, flexibility, and seamless integration capabilities. Implementation hurdles such as legacy system integration and data migration require careful planning and strategic collaboration with technology partners. Nevertheless, by overcoming these challenges and harnessing the power of cloud computing, mortgage originators can streamline their operations, lower costs, and improve customer experiences, ultimately driving growth and success in the mortgage industry.

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

Internet of Things (IoT) has been flourishing the communication and networking system in recent years, while experiencing a growing phase in agricultural field. Precision agriculture requires sensor integration, automatic control, and networking and data processing capabilities. With the implementation of rapidly growing IoT, challenges of scalability and data management can be overcome. In this research, we have developed an IoT based smart monitoring system for agricultural practice including client application- web application and android application; and designed a controlling system for power efficient irrigation. We have used LoRa, wireless RF transceiver to overcome the limitations of area coverage and have enabled to established real time data communication. While our android application makes this system feasible for users. Real time data were collected deploying the system and empirical data has been included in the result section.

The emergence of new hardware architectures, and the continuous production of data open new challenges for data management. It is no longer pertinent to reason with respect to a predefined set of resources (i.e., computing, storage and main memory). Instead, it is necessary to design data processing algorithms and processes considering unlimited resources via the “pay-as-you-go” model. According to this model, resources provision must consider the economic cost of the processes versus the use and parallel exploitation of available computing resources. In consequence, new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.

Hardware techniques and environments underwent significant transformations in the field of information technology, represented by high-performance processors and hardware accelerators characterized by abundant heterogeneous parallelism, nonvolatile memory with hybrid storage hierarchies, and RDMA-enabled high-speed network. Recent hardware trends in these areas deeply affect data management and analysis applications. In this paper, we first introduce the development trend of the new hardware in computation, storage, and network dimensions. Then, the related research techniques which affect the upper data management system design are reviewed. Finally, challenges and opportunities are addressed for the key technologies of data management and analysis in new hardware environments.

The advent of Industry 4.0 has irrevocably shaped the landscape of organizational operations and Human Resources (HR), compelling a symbiotic integration with Human Capital Management (HCM) to foster enhanced employee experiences and streamline HR functionalities. This paper seeks to elucidate the impacts, challenges, and prospective advancements of integrating HCM methodologies with Industry 4.0 technologies, weaving a narrative that is pivotal for contemporary and future HR practices. The imperative nature of recognizing HCM within the context of Industry 4.0, elucidating a paradigm where human resources are navigated through a lens of technological and strategic management [1]. Internet of Things (IoT), big data analytics, and robotics, the HR realm is transcending traditional boundaries, embedding technologically driven approaches into its core. Through a meticulously crafted framework, the paper explores the resultant digitalized human capital and introduces smart, data - driven workforce management strategies while also revealing the potent influence of these integrations across various HR practices, including recruitment, employee engagement, performance management, and learning and development. The repercussions of Industry 4.0 on production systems, offering a foundation to explore its cascading impacts on HR practices and policies [2]. This integration harbors the potential to manifest a revolutionized HR framework, whereby artificial intelligence, data analytics, and IoT converge to facilitate an employee experience that is not only richly engaging but also significantly contributive towards organizational objectives. While steering through the challenges of technological adaptation, skill enhancement, and data management, the paper navigates towards a future where HCM and Industry 4.0 collaboratively sculpt an epoch of intelligent, adaptive, and employee - centric HRM, thereby holding a significant implication for scholars and practitioners in the field.

In today's digital landscape, the integration of mobile applications with server systems presents both opportunities and challenges in data management. This symbiotic relationship necessitates seamless synchronization between mobile devices and servers, particularly in scenarios where internet connectivity fluctuates. Strategies such as Data Storage Codification for Enhanced Optimization offer a solution, enabling robust data storage and retrieval processes that remain functional even in offline settings. By leveraging mechanisms such as cloud document servers and unique identifiers, organizations can ensure the secure transmission and tracking of data between mobile apps and servers, enhancing overall efficiency and user experience. Efficient management of server interactions and data storage on mobile devices is crucial for ensuring uninterrupted operation and data integrity. By implementing a meticulous system of request tracking and drafting in both mobile applications and servers, organizations can streamline the transmission of data, even in intermittent internet connectivity scenarios. Leveraging technologies such as pub-sub models facilitates efficient communication between mobile apps and servers, ensuring prompt acknowledgment and processing of requests, thereby enhancing overall system reliability and responsiveness. The seamless integration of mobile applications with server systems necessitates a strategic approach to data management, particularly concerning synchronization and tracking of requests. By adopting comprehensive strategies such as those outlined in the current thread, organizations can navigate the complexities of modern data management effectively. This entails leveraging cloud document servers, unique identifiers, and pub-sub models to facilitate secure and efficient transmission of data between mobile devices and servers, ultimately enhancing operational efficiency and user satisfaction.

Background & objectivesMany low- and middle- income countries (LMICs) have attempted to implement trauma registries with varying degrees of success. This study aimed to understand the registry implementation mechanism in LMICs better. Study objectives include assessment of the current use of trauma registries in LMICs, identification of barriers to the process and potential areas for intervention, and investigation of the registry implementation experience of key stakeholders in LMICs.MethodsAn initial narrative review of articles on trauma registry use in LMICs published in English between January 2017 and September 2023 was conducted. Key findings identified in this review were used to establish a theoretical framework from which an interview guide was subsequently developed. Expert consultation with key stakeholders in trauma registry implementation in two LMICs was conducted to assess the experience of registry implementation further.ResultsThe presence of trauma registries in LMICs is limited. Key implementation barriers include funding concerns, uncoordinated administrative efforts, lack of human and physical resources (i.e., technology, equipment), and challenges in data management, analysis, and quality. Stakeholder interviews highlighted the importance of trauma registry development but echoed some obstacles, notably funding and data collection barriers.Interpretation & conclusionsBarriers to registry implementation are ubiquitous and may contribute to the low uptake of registries in LMICs. One potential solution to these challenges is the application of the WHO International Registry for Trauma and Emergency Care. Future studies examining context-specific challenges to registry implementation and sustained utilization are required.

The wealth of available datasets in genomics and proteomics has spawned a corresponding interest in knowledge discovery methods that enable researchers to attain substantial biological meanings pertaining to model various human diseases. The scale and complexity of these datasets give rise to important challenges in data management and analysis. This paper aims at designing a framework that helps researchers to better conduct data analysis through seamless biological data integration from various literature and online databases. A rule-based program is designed and implemented to access the GO database and retrieve datasets related to apoptosis. An advanced relational learning method is employed to infer causal relationships between the retrieved datasets. As a result, this framework aims at providing a level of query processing beyond even some high-profile websites. This will lead to an improved understanding of the pathogenesis of a certain disease and eventually to its therapy. In addition, this work also provides a comprehensive picture of biological processes, gene functions and protein properties that may detect disease patterns and lead to novel disease concepts.

The era of Heterogeneous systems and Big Data computing is already here. Handling huge amount of data poses new challenges in data management and in the effective usage of memory, caches, heterogeneous structures and available bandwidth. In addition, computing requirements of Big Data is unique; in many occasions the processing required per storage access is limited (i.e. low Instructions/Byte) which presents a new challenge and opportunities for computer architects. The increasing performance requirement are driving the industry towards parallel execution of threads (or tasks) being executed by energy efficient computing (e.g. heterogeneous) engines. In this talk well present some of the research being performed at the Technion, which is related to effective usage of caches and heterogeneous systems in multiasking environment and will provide a scent of remedy to the situation. In addition we will present the substantial issue of non-temporal-memory-access in Big Data environment and suggest some hopefully stimulating solutions.

Geomarketing revolutionizes retail location strategies by leveraging Geographic Information Systems (GIS) and geolocation technologies, shifting from traditional intuition-based approaches to precise, data-driven decisions. It enhances retail visibility, customer engagement, and operational efficiency through detailed spatial analysis, sales forecasting, and consumer behavior understanding. By integrating spatial analysis with the retail marketing mix, it underscores the critical role of 'Place' in optimizing retail strategies. Despite challenges in data management and technology integration, the future of geomarketing looks promising, with artificial intelligence and machine learning poised to further advance spatial analytics and decision-making processes in the retail industry.

Chapter 32 - StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time