Big Data Technologies

This paper addresses the critical challenge of preserving database integrity during legacy system migrations in the education technology (EdTech) sector. Legacy platforms, often burdened with outdated formats, undocumented business logic, and schema incompatibilities, pose significant risks to data accuracy, compliance, and trust. Serving millions of students, families, and educators, data corruption or loss during migration can disrupt learning, violate privacy regulations, and damage institutional reputation. Drawing from real-world experience, the paper presents a three-phase migration paradigm: (A) data assessment and cleansing, (B) migration planning and execution, and (C) post-migration validation and optimization. Tools such as Informatica, Apache NiFi, and QuerySurge are discussed for profiling, pipeline orchestration, and reconciliation at scale. By embedding integrity checks, automation, and governance into every stage of the process, EdTech platforms can modernize securely, ensuring compliance and sustaining trust across educational communities.

Cloud native ETL pipelines support the extract and transform phases of real time claims processing in large scale insurers. The cloud native approach offers dramatic improvements in scalability, reliability, resiliency and agility as well as seamless integration with the diverse set of data sources, destinations and technologies characteristic of large scale insurers. The ETL process extracts data from source systems such as core transaction, fraud, customer and accounting processes, transforms the data to create a usable format for analytics and other applications, and loads the resulting tables into business intelligence or data lake systems for subsequent storage and analysis. By addressing these two phases of the overall ETL process, cloud native ETL pipelines can provide timely, reliable and consistent data to data scientists, actuaries, underwriters and other analysts. Real time processing represents a key priority within the overall claims process: faster, more accurate claim approvals reduce insurer costs, improve customer service and enhance premium pricing. As a result, a variety of claims related use cases are moving from batch to real time.

3510 Résumé L'intelligence artificielle (IA) peut améliorer le contrôle interne en détectant les anomalies et les tendances qui pourraient indiquer des problèmes de fraude ou d'erreur humaine. La maitrise des tâches peut augmenter les risques de fraude, car les individus peuvent développer des stratégies pour contourner les contrôles. En revanche, la non-maitrise des tâches peut augmenter les risques d'erreur et de faute humaine, car les individus peuvent ne pas avoir les compétences nécessaires pour effectuer les tâches correctement, ainsi entraîner des erreurs, des omissions ou des actions non intentionnelles qui peuvent entraîner des conséquences négatives pour l'organisation. Cela souligne l'importance de mettre en place des contrôles internes efficaces pour détecter et prévenir les fraudes, ainsi que pour réduire les risques d'erreur et de faute humaine. L'IA peut jouer un rôle clé dans ce processus en analysant les données et en détectant les anomalies qui pourraient indiquer des problèmes de fraude ou d'erreur humaine. Mots clés : contrôle interne, L'intelligence artificielle, loi de puissance de la performance humaine, erreur humaine, risque, fraude

Many data sets, such as system logs, are generated from widely distributed locations. Current distributed systems often discard this data because they lack the ability to backhaul it efficiently, or to do anything meaningful with it at the distributed sites. This leads to lost functionality, efficiency, and business opportunities. The problem with traditional backhaul approaches is that they are slow and costly, and require analysts to define the data they are interested in up-front. We propose a new architecture that stores data at the edge (i.e., near where it is generated) and supports rich real-time and historical queries on this data, while adjusting data quality to cope with the vagaries of wide-area bandwidth. In essence, this design transforms a distributed data collection system into a distributed data analysis system, where decisions about collection do not preclude decisions about analysis.

This article examines the legal liability of multinational technology companies toward workers in their global supply chains, with particular focus on Kenya's experience as a critical case study in digital labour exploitation. The paper analyzes the troubling implications of Kenya's Business Laws (Amendment) Bill (Senate Bills No. 51 of 2024), which proposes to shield foreign technology companies from liability for human rights violations committed through their subcontractors, representing a significant regression in corporate accountability.
Using Kenya's emergence as a hub for outsourced digital services—particularly content moderation for major social media platforms—the study explores how the intersection of labour export policies, corporate impunity, and inadequate legal protections creates conditions ripe for worker exploitation. The analysis centers on the ongoing legal battle involving Meta (formerly Facebook) and its subcontractor Sama, where Kenyan content moderators have alleged numerous human rights violations including exposure to disturbing content without psychological support, unsafe working conditions, discriminatory practices, below-standard wages, restrictions on unionization, and trafficking-like conditions.
The paper argues that the proposed Kenyan legislation, designed to exempt foreign technology companies from liability in suits arising from outsourced work, fundamentally contradicts the UN Guiding Principles on Business and Human Rights. Through detailed examination of the three pillars of the UN framework—the state duty to protect, corporate responsibility to respect, and access to remedy—the analysis demonstrates how the amendment undermines each principle by removing legal avenues for worker redress while failing to ensure corporate human rights due diligence.
Comparative analysis reveals Kenya's approach as regressive compared to global trends toward greater corporate accountability. The study examines progressive frameworks including the EU's Corporate Sustainability Due Diligence Directive (2024), the UK's Modern Slavery Act, California's Transparency in Supply Chains Act, and emerging Asia-Pacific and African approaches to corporate responsibility. Case studies of successful litigation against multinational corporations—from Lungowe v Vedanta in the UK to Shell cases in the Netherlands—demonstrate the increasing judicial willingness to hold parent companies accountable for subsidiary violations.
The paper identifies how multinational technology companies employ sophisticated corporate structures to evade liability, including multiple layers of subcontracting, jurisdictional shopping, and contractual liability limitations. These mechanisms create legal distance between ultimate beneficiaries of labour and workers themselves, while the proposed Kenyan legislation would facilitate such evasion by providing a jurisdiction where companies can operate with impunity.
Constitutional analysis reveals that the amendment contradicts Kenya's own legal framework, particularly provisions on access to justice (Article 48), fair labour practices (Article 41), and public participation (Articles 10 & 118). The legislation also conflicts with statutory protections under the Employment Act and Occupational Safety and Health Act, while violating Kenya's international obligations under ILO conventions on decent work and occupational safety.
The study projects profound negative consequences for Kenya's digital work economy, including reduced worker protections through creation of a two-tier system, encouragement of a "race to the bottom" in labour standards, reduced investment in worker welfare, weakened civil society advocacy, and international reputation damage. The paper argues that this approach prioritizes short-term economic gains over fundamental worker rights while being economically short-sighted regarding Kenya's long-term competitiveness.
The paper concludes with comprehensive recommendations spanning immediate actions (removing problematic amendment provisions and strengthening judicial independence), medium-term reforms (developing corporate accountability legislation and enhancing remedial mechanisms), and long-term structural changes (regional harmonization and international cooperation). It emphasizes the role of international pressure through diplomatic engagement, economic leverage, civil society support, and consumer activism in advancing corporate accountability.
The analysis positions Kenya's proposed legislation as both morally questionable and strategically misguided, arguing that genuine corporate accountability requires strong legal frameworks holding all actors responsible regardless of nationality or corporate structure. The paper calls for the international community to prevent Kenya's regression from becoming a global template, instead using this moment to strengthen corporate accountability mechanisms worldwide to ensure technological progress benefits all stakeholders rather than concentrating wealth among the powerful while exploiting vulnerable workers.

Keywords: corporate accountability, human rights, technology companies, digital labour, Kenya, UN Guiding Principles, supply chain liability, content moderation, labour exploitation, multinational corporations

Data engineering is a dynamic and ever-changing discipline that demands strong tools for data lineage management due to the growing complexity of data ecosystems. One of the most important ways to solve problems with data traceability, regulatory compliance, and operational efficiency is using automated data lineage tracking. The benefits, drawbacks, and design of automated data lineage tracing systems in contemporary data engineering ecosystems are investigated in this paper. The method follows the flow of data across many systems and processes by using graph-based models, machine learning, and enhanced metadata management. With the help of improved transparency, mistake detection, and simplified data governance, organisations can confidently make choices based on data. In addition, the study details the difficulties of integrating lineage tracking with existing systems and provides recommendations on how to implement it in a flexible and scalable manner. Automatic data lineage tracing has the ability to revolutionise data-driven operations, as shown by the practical case studies and insights provided in this paper. To guarantee speed, dependability, and transparency in data processing processes, automated data lineage tracing has become an essential part of contemporary data engineering ecosystems. Automated systems for recording and visualising data lineage across complex, dispersed contexts are the focus of this research, which also examines their design, implementation, and effect. Errors are common in manual lineage tracking methods, and they can't keep up with the ever-increasing data volume and speed. The suggested approach uses graph-based representation, dependency parsing, and metadata extraction to reveal data transformations, dependencies, and use patterns in real-time. The solution optimises resource utilisation, simplifies debugging, and improves compliance with regulatory requirements by connecting with current data pipelines. This article showcases real-life examples of how automated lineage monitoring has improved data governance and operational efficiency. Our research shows that one of the best ways to build scalable and reliable data engineering techniques is to automate lineage management. Organisations may improve data governance, guarantee regulatory compliance, and optimise data processes with the help of automated data lineage tracing, which is an essential part of contemporary data engineering ecosystems. Focussing on their function in tracing data's genesis, transformation, and destination across intricate pipelines, this research investigates the creation and deployment of automated data lineage systems. These systems provide full transparency into data flows with little human involvement by using cutting-edge methods including metadata harvesting, graph-based representations, and machine learning algorithms. Important obstacles that are tackled include making distributed systems scalable, integrating with various tools, and guaranteeing that lineage information is updated in real-time. By providing trustworthy insights into data dependencies and effect analysis, automated data lineage monitoring considerably decreases operational overhead, enhances data quality, and speeds decision-making processes. After discussing how automated lineage tracking might revolutionise data engineering methods, this article finishes with some suggestions on how businesses can use it.

Resumo: O ar.go inves.ga o impacto do colonialismo digital no campo da arte contemporânea, com ênfase em manifestações ar=s.cas digitais. Par.ndo de uma análise teórica sobre o conceito de colonialismo digital, entendido como uma forma contemporânea de dominação exercida por grandes corporações tecnológicas, o estudo explora as implicações dessa lógica na produção, circulação e recepção de obras de arte em ambientes virtuais. A análise aborda questões como vigilância algorítmica, imposição de padrões tecnológicos ocidentais e a marginalização de expressões ar=s.cas locais, propondo a arte digital como um espaço privilegiado para refle.r e combater essas dinâmicas. Exemplos de ar.stas e obras que desconstroem as estruturas de poder do colonialismo digital são apresentados, oferecendo uma perspec.va crí.ca e interdisciplinar sobre as relações entre arte, tecnologia e jus.ça cultural.

Telecom data models historically centered on OSS/BSS-customers, products, orders, inventory, usage (CDRs), billing, trouble tickets, and network inventory. That foundation still matters, but modern operators run software-defined, cloud-native networks (5G core, ORAN, MEC) that emit high-volume telemetry and support AI agents for assurance, optimization, and customer care. This article reframes the classic telecom model into an AI-first blueprint where facts → metrics → features → decisions → audit form a governed loop across the business and the network.

New methods of sharing and collaborating on data within companies are required due to the fast development of data ecosystems. In order to respond to changing business demands, organisations typically struggle with agility, scalability, and real-time insights caused by traditional centralised architectures and Extract, Transform, Load (ETL) procedures. In order to overcome these obstacles, this research investigates the revolutionary possibilities of the Data Mesh and Zero ETL paradigms. Data Mesh allows cross-functional teams to manage and distribute data as a product, decentralising ownership by aligning it with business domains. By doing away with tedious data pipelines, Zero ETL allows for frictionless, real-time data transfer across platforms and systems, significantly improving efficiency. Taken as a whole, these methods improve decision-making, streamline operations, and promote self-service analytics, all while redefining intra-company data cooperation. This study proves that data accessibility, governance, and time-to-insight all saw considerable improvements after analysing case studies and performance indicators in depth. Data Mesh and Zero ETL have the ability to transform the way organisations use data, encouraging a spirit of creativity, cooperation, and agility, according to the results. Businesses may use this research as a guide to adopt these paradigms and thrive in the ever-changing digital world. Innovative solutions for intracompany data interchange and cooperation are required due to the increasing complexity and volume of current data ecosystems. When it comes to agility, scalability, and real-time, traditional data architectures and Extract, Transform, and Load (ETL) procedures can't cut it. In order to tackle these problems head-on, this research looks at how the Data Mesh and Zero ETL paradigms may work together. By distributing ownership of data and tying it to certain business domains, Data Mesh allows teams to manage data as a product and guarantees interoperability. Zero ETL reduces the time and resources needed for data transformation and transfer while eliminating the need for conventional data pipelines. It enables real-time data exchange. A data-driven decision-making environment that is agile and collaborative is fostered by these paradigms when they are used together. Organisations that use these strategies increase data accessibility, governance, and operational efficiency, as shown by empirical assessments and real-world case studies. Data Mesh and Zero ETL have the ability to transform how companies handle data internally. This might lead to more innovation, less complexity, and a faster return on investment. Results from this research may help businesses update their data strategy and adopt a more collaborative, domain-oriented approach to data management.

The current research is an attempt to analyze the relationship between the use of big data technologies and strategic cost management and their impact on the competitive advantage of a sample of private banks listed on the Iraq Stock Exchange. Due to rising competition in the banking sector and the growing pace of change in the economic and technological environment, big data represents a important strategic asset for improving the performance of banks that are seeking to increase their competitive power and overall efficiency. Thus be able to take advantage of big data technologies and strategic cost management in an appropriate way, which is really appropriate for the dominance of banks over the market, is a must. The study is grounded on a principal hypothesis: The influence of the association of big data technologies and strategic cost management on competitive advantage The data collecting instrument was a questionnaire. The number of questionnaires is 173 distributed over a sample of accountants, auditors, financial analysts, and financial managers in most private banks listed on the Iraq Stock Exchange. Out of the distributed questionnaires, a total of 158 valid responses were returned (91%). The author administered the questionnaires and tested the hypotheses using the advanced statistical program Smart-PLS and Excel, applying Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed a significant effect of the relationship of both the use of big data technologies and strategic cost management have on competitive advantage of all the dimensions (determinants of the competitive advantage), cost and service quality, flexibility and flexibility to changes in the market, and creativity, innovation and time. The researcher further suggests that there is a need to develop the skills of bank employees in big data techniques, exploit big data as a strategic weapon to both enhance efficiency of the operation function and improve the strategic decision making process, and to adopt sophisticated analytics to assess actual performance and facilitate risk management. In addition, the research highlights that big data technologies are aligned with strategic cost management, as a planning tool, and as a cost control tool used to achieve high service quality and operational efficiency, while also fostering a culture of innovation and continuous improvement. This capability will enable banks to transform their internal processes, create customer satisfaction with the immediate response to preferences, and therefore, gain their competitive advantage innovatively and efficiently.

This paper argues that the corruption of the Roman Senate offers a historical mirror for the dysfunction of today’s corporate middle layers. While media narratives often focus on billionaire founders and owners as the architects of systemic abuse, the analysis shows that much of the real enabling power rests with boards, policy directors, consultants, and middle managers who act as modern senators. Using examples from the “Big 7” technology firms, Palantir, and Emerson Collective, the paper highlights how owners such as Elon Musk, Jeff Bezos, Mark Zuckerberg, Tim Cook, Satya Nadella, Sundar Pichai, Jensen Huang, Alex Karp, and Laurene Powell Jobs are not solitary villains but figureheads of systems sustained by cowardice, inertia, and corruption in their corporate senates. Just as Rome’s emperors relied on a self-serving Senate to legitimise and preserve corruption, today’s elites rely on their middle layers to filter decisions, avoid accountability, and maintain the appearance of stability while dysfunction deepens.

31/08/2025

There is always a need to modernize energy delivery of traditional power grids, using intelligent devices and big data technologies, this make them smart. The modernization is performed by deploying equipment such as sensors, smart meters, and communication devices, and by invoking procedures such as real-time data processing and big data analysis. A large data is generated by Smart grids all the time which needs to be analysed. This paper presents definitions and background of smart grid and big data. There are many challenges faced in collecting and analysing this data. In this paper current studies and research developments of big data applications in smart grid are discussed. This paper also summarizes the application leveraged by big data technologies, challenges and opportunities are pointed out in this

Sommario: 1. Preambolo – 2. Cenni sul pegno nel codice civile italiano e sulla teoria della realità del pegno – 3. Il superamento del tradizionale schema-tipo di pegno – 4. Il contemperamento tra tutela del credito e circolazione della ricchezza – 5. Il pegno mobiliare non possessorio ex art. 1, D.L. 59/2016 – 6. Il pegno non possessorio sui beni immateriali: diritti IP e big data – 7. Lo spossessamento quale elemento eventuale e non indispensabile della fattispecie pignoratizia – 8. La funzione della realità del pegno nel quadro frantumato delle garanzie del credito – 9. Il pegno non possessorio come garanzia dinamica: verso la configurazione di un ‘pegno consensuale’ – 10. Prospettive comparatistiche: la c.d. garanzia ‘flottante’ – 11. Riflessioni de iure condendo.

Organizations struggle to attain scalable, secure, and unified data intelligence in the exponential data proliferation age. Conventional data structures are likely to face the problem of data fragmentation, lack of scalability, little or no capability to govern data, and hence, the inability to draw contextualized real-time actions out of this data. This paper examines the strategy of deploying Azure Databricks, Delta Lake, and Unity Catalogue as a complete solution that will help tackle these looming challenges. Azure Databricks can help an enterprise provide a familiar, unified analytics environment running in the cloud to perform high-performance distributed data processing on any scale. Delta Lake reliably and consistently manages data with ACID transactions and applied schema. Unity Catalogue also boosts data governance and security by offering centralized control over access and lineage observation across cross-cloud situations.

This book is a complete guide for professionals and data enthusiasts who want to make the most of Microsoft’s cloud-native ecosystem for big data analytics. It covers essential services  like  Azure  Synapse  Analytics,  Microsoft  Fabric,  and  Power  BI.  The  bookprovides  a  full  framework  for  scalable  data  processing  and  smart  decision-making. Readers will learn best practices for data ingestion, transformation, storage, modeling, and visualization. They will also see how to combine data engineering, data science,and business intelligence workflows within a single Microsoft environment. With practical examples and architectural designs, this book helps readers build secure, effective, and cost-efficient analytics solutions that meet the needs of today’s enterprises.

Dans un environnement marqué par une digitalisation accélérée, les banques marocaines sont confrontées à des transformations profondes dans leur relation avec les clients. L’émergence de nouveaux usages numériques, la multiplication des points de contact et l’exigence croissante de personnalisation imposent aux établissements bancaires de repenser leurs stratégies de communication et d’interaction. Dans ce contexte, les réseaux sociaux apparaissent comme des canaux incontournables, non seulement pour informer, mais aussi pour dialoguer, fidéliser et valoriser l’image de la banque auprès de ses usagers.
Cette étude vise à évaluer dans quelle mesure l’intégration des réseaux sociaux dans les dispositifs multicanaux bancaires peut améliorer la relation client et contribuer à la performance globale des établissements. Pour ce faire, une enquête quantitative a été réalisée auprès de 139 clients bancarisés dans la région TTA, en s’appuyant sur un questionnaire structuré. Les données recueillies ont été traitées par des outils d’analyse descriptive afin d’identifier les tendances d’usage, les niveaux de satisfaction et les attentes des clients.
Les résultats mettent en évidence une forte utilisation de Facebook et YouTube, mais révèlent également une insatisfaction généralisée quant à la pertinence du contenu proposé. La majorité des clients privilégie l’aspect ludique (jeux concours, promotions), tandis que les informations bancaires diffusées sur ces plateformes sont jugées peu utiles. L’étude souligne également le rôle stratégique de l’e-réputation, largement influencée par les commentaires en ligne.
Face à ces constats, il apparaît essentiel pour les banques de développer une stratégie de contenu cohérente, interactive et segmentée par plateforme, afin de répondre aux attentes spécifiques des usagers. Cette démarche constitue une condition nécessaire pour transformer les réseaux sociaux en véritables leviers de fidélisation, de différenciation et d’innovation relationnelle.

The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of Science TM Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.

This book offers a critical account of Karl Marx’s dazzling theory of labour power which is also one of the most influential concepts in the history of contemporary philosophy. Labour power is the dark side of the digital revolution. Working men and women are invisible and treated like human service, flesh and blood automatons or organic extensions of a machine that produces data on its own. Automation is viewed as something magic made possible by algorithms whose life is independent of human beings.


Labour power, however, has not disappeared. Without drivers, Uber cannot connect customers on its platform; without searches on its browser, Google grinds to a halt; without us, Facebook or Instagram is desert. Labour power is the dwarf hidden inside the puppet of technology that allows algorithms to be intelligent and make the biggest profits in the history of capitalism.


The invisible centrality of labour power is the political enigma of our times. Today a new account of the theory of labour power is needed more than ever in order to understand the political economy of digital capitalism on new grounds.


Unlike a long tradition in the history of work, labour power is not only the work or the data it produces, but a potency that does not coincide with its current commodification. The actuality of labour power does not exhaust the virtuality that can be actualised by its faculty. Even when reduced to a commodity, labour power does not exhaust the potency of its being otherwise.


Immersed in the constant propaganda that boosts the latest technological inventions, we neglect the fact that this wealth is produced by us and that it could be ours precisely because it is a part of our potential to be other than what we are at present.


This book is a vibrant invitation to consider the fact that we are always connected with the potency thatis constantly at work in our life. If this were not the case, we would not be alive. If we do not strive to become consciously and collectively active, we will never know.

Many organizations are shifting to a data management paradigm called the "Lakehouse," which implements the functionality of structured data warehouses on top of unstructured data lakes. This presents new challenges for query execution engines. The engine needs to provide good performance on the raw uncurated datasets that are ubiquitous in data lakes, and excellent performance on structured data stored in popular columnar file formats like Apache Parquet. Toward these goals, we present Photon, a vectorized query engine for Lakehouse environments that we developed at Databricks. Photon can outperform existing warehouses on SQL workloads and also supports the Apache Spark API. We discuss the design choices we made in Photon (e.g., vectorization vs. code generation) and describe its integration with our existing SQL and Apache Spark runtimes, its task model, and its memory manager. Photon has accelerated some customer workloads by over 10× and has recently allowed Databricks to set a new audited performance record for the official 100TB TPC-DS benchmark. • Information systems → Database management system engines; Main memory engines; Database query processing.

Managing big data in cloud environments requires a combination of strong security measures and efficient
clustering techniques. With the increasing volume of sensitive data being processed, ensuring secure
storage, retrieval, and management is essential. Advanced cryptographic methods can protect data from
unauthorized access, while machine learning algorithms improve clustering accuracy and efficiency. By
integrating these technologies, cloud infrastructures can enhance both security and performance, reducing
vulnerabilities and optimizing resource allocation. Businesses and organizations relying on cloud platforms
need robust mechanisms to safeguard their data while maintaining operational efficiency. Efficient
clustering techniques help in organizing and processing large datasets, making data management more
effective. Secure cloud based operations ensure compliance with regulatory standards and protect against
cyber threats. Implementing scalable security frameworks strengthens data confidentiality, integrity, and
availability. Leveraging AI-driven models for anomaly detection further enhances security within cloud
environments. The proposed system will integrate security and clustering by using encrypted data storage
combined with machine learning-based data clustering within a cloud environment.

One of the biggest challenges for businesses experiencing a digital transformation was to build and maintain strong positive relations with employees. To do so, companies should develop a proper organisational culture. Despite the recognised importance of organisational relationships, only a limited number of studies focus explicitly on the challenges of relationships among managers and employees in the digitalised organisational culture. Trying to fill the gap, this paper discusses the challenges for employers based on literature review and explorative research. The main purpose of the study is to identify key challenges influencing the employee relationships in the new, post pandemic, digitalised workplace. The theoretical deliberations, based on critical literature review, are supplemented by empirical research conducted in a form of a focus groups with 34 participants (3 focus groups) employed on managerial and non-managerial positions. Authors interviewed informants who had experienced both in-person and remote working pattern. The research was conducted between March and May 2022. The main challenges were identified in the research process. Zoom fatigue, lack of in-person relations, technical issues and life-work balance were the main challenges perceived for employees, while technology limitations, employee governance and A. DEWALSKA-OPITEK, R. VELEZ NUNEZ WyżSza Szkoła GoSpodarki euroreGionalneJ iM. alcide de GaSperi W JózefoWie 254 HR related issues were experienced by employers. To address these challenges and be able to build and maintain strong and positive employee relations in the digitalised environment, employers should provide trust, motivation, remuneration, support, effective communication, sustainable working environment and employee-centred culture. Taking the above factors into consideration, a reference to the proposed 6Cs model is noticeable.

Recently, hybrid multi-site big data analytics (that combines on-premise with off-premise resources) has gained increasing popularity as a tool to process large amounts of data on-demand, without additional capital investment to increase the size of a single datacenter. However, making the most out of hybrid setups for big data analytics is challenging because on-premise resources can communicate with off-premise resources at significantly lower throughput and higher latency. Understanding the impact of this aspect is not trivial, especially in the context of modern big data an-alytics frameworks that introduce complex communication patterns and are optimized to overlap communication with computation in order to hide data transfer latencies. This paper contributes with a work-in-progress study that aims to identify and explain this impact in relationship to the known behavior on a single cloud. To this end, it analyses a representative big data workload on a hybrid Spark setup. Unli...

Customers who use internet banking have the flexibility to manage their assets sometimes when and wherever they choose. Any web-based transactions will, in any instance, be vulnerable to privacy risks. The current system makes use of two-way validation factors (OTP), which are easily cracked by cybercriminals, causing customers to have their personal information invaded without having access to the aggressor's recognised evidence. As a result, in this research, we suggest a flexible SIM sequential-based check approach to safeguard mobile transactions using a waiter-side secure platform built on the Blockchain. The customer's record has the supporter character module (sim) serial number, and if a gate crasher tries to initiate a transaction from another sim, there is a mix-up of the login credentials, at which point the system communicates the intrusive location information to the bank. The bank then sends an email to the subscribed customer alerting them to the peculiarities of the intruder. There is no risk of a gate crasher initiating the transaction from another device because the mobile sim chronicle number is unique to the sim and isn't literally very distanced, eliminating the concerns associated with OTP. By verifying the updated markings throughout the transaction and calculating the contract for transaction confirmation, the Ethereum Blockchain breakthrough provides server-side information base security. For reliable financial exchange, blockchain-based security is mathematically validated.

A decade on from the launch of Amazon's Alexa the smart home's breakout product the vision of semi-automated, pervasively sensed domesticity remains unrealised; industry losses are mounting; and big tech protagonists are in partial or full retreat from their smart home ambitions. Simultaneously, scholars draw attention to the dangers posed by the extractive business model underpinning this instantiation of the internet of things, in terms such as 'data colonialism', and call for an organised 'unmaking' of it. Against this backdrop, drawing on a 2 year study of six families living with smart home technologies (SHTs), we invoke the concept of 'mundane resistance', to describe the ways in which SHTs come to be diminished, or outright rejected, not by a singular user, but in the interplay of multiple members of the home in the course of their everyday participation in its moral economy. The outcome of these exchanges is a surveillance capitalism less panoptic than myopic. At a moment in time when the global economy is increasingly geared around the promise of generative AI, recognising the potential for such uncoordinated everyday acts to, in aggregate, disassemble the grand plans of the tech industry, is of considerable value. It serves both as a rejection of the industry's grandiose self-mythologising and as a statement of the ongoing importance of attending to the situated practices of everyday life, both in its repeating patterns and its quotidian idiosyncrasies.

The processes of generating innovative solutions mostly rely on skilled experts who are usually unavailable and their outcomes have uncertainty. Computer science and information technology are changing the innovation environment and accumulating Big Data from which a lot of knowledge is to be discovered. However, it is a rather nebulous area and there still remain several challenging problems to integrate the multi-information and rough knowledge effectively to support the process of innovation. Based on the new cross discipline Extenics, the authors have presented a collaborative innovation model in the context of Big Data. The model has two mutual paths, one to transform collected data into an information tree in a uniform basic-element format and another to discover knowledge by data mining, save the rules in a knowledge base, and then explore the innovation paths and solutions by a formularized model based on Extenics. Finally, all possible solutions are scored and selected by 3...

Makalah ini memaparkan prosedur instalasi OwnCloud suatu platform open-source untuk layanan
file sync & share pada lingkungan Windows 10 menggunakan XAMPP 5.6.36 sebagai web stack
dan ngrok sebagai reverse proxy guna mempublikasikan server lokal ke internet. Metode yang
diadopsi berlandaskan demonstrasi pada video “Install OwnCloud di localhost maupun
secara online” serta dokumentasi resmi teranyar. Hasil implementasi menunjukkan bahwa
OwnCloud dapat dijalankan secara fungsional di localhost serta diakses melalui domain sementara
https yang disediakan ngrok; namun, terdapat batasan performa dan dukungan resmi karena
OwnCloud merekomendasikan sistem operasi berbasis Linux.

Makalah ini memaparkan prosedur instalasi OwnCloud suatu platform open-source untuk layanan
file sync & share pada lingkungan Windows 10 menggunakan XAMPP 5.6.36 sebagai web stack
dan ngrok sebagai reverse proxy guna mempublikasikan server lokal ke internet. Metode yang
diadopsi berlandaskan demonstrasi pada video “Install OwnCloud di localhost maupun
secara online” serta dokumentasi resmi teranyar. Hasil implementasi menunjukkan bahwa
OwnCloud dapat dijalankan secara fungsional di localhost serta diakses melalui domain sementara
https yang disediakan ngrok; namun, terdapat batasan performa dan dukungan resmi karena
OwnCloud merekomendasikan sistem operasi berbasis Linux.

Most of the current day applications process large amounts of data. There were different trends in computing like mainframes, parallel computing, cluster computing, grid computing as per the requirement of the data size and execution speed. Cloud computing is the new era of computing where efficient utilization of resources can be done with no compromise on data size, execution time and cost of execution. Map Reduce is a programming model which is widely used for processing large scale data intensive applications in cluster, cloud environments. In this paper we have discussed various scheduling algorithms of map reduce tasks. The default schedulers available with Hadoop can be improved to make it more efficient for the cloud environments

Cloudera Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Apache Hive. This paper presents Impala from a user's perspective, gives an overview of its architecture and main components and briefly demonstrates its superior performance compared against other popular SQL-on-Hadoop systems.

In the context of debates about logistics as a critical infrastructure of contemporary capitalism, this article addresses the question of the conditions under which workers' structural power can be translated into the organisation of collective interests. Based on original empirical research (30 interviews) in online food delivery and logistic centres in Poland, the article argues that 'organising potential' is greater in the latter. This finding is explained by the innovative activities of new, radical and grassroots trade union leaders and the availability of 'societal power resources', including the presence of Amazon as a global target of union protest (a 'reverse Amazon effect') and more developed union solidarity networks.

In the early days of navigation, humans relied on visual landmarks such as mountains, coastlines, and structures to determine their location and direction. With the arrival of tools like compasses and sextants, navigation became more precise, enhancing navigation and positioning. In the modern era, outdoor navigation relies heavily on global satellite-based positioning systems such as GPS, allowing individuals to navigate around the globe, locate points of interest, and assist robots in positioning themselves in both known and unknown environments.

While indoor positioning systems have been in development for decades, their adoption has accelerated in recent years in complex environments like airports, supermarkets, and warehouses. These systems are employed for tasks such as resource tracking, automated order picking, and guiding users through complex building layouts. Unlike traditional positioning systems, which rely on a fixed set of techniques, indoor positioning or navigation systems often use diverse technologies and hardware, usually requiring proprietary software or intricate calibration to function. This reliance on proprietary solutions creates several challenges. Users are often required to install software for each specific location or building, fragmenting the user experience and raising concerns about privacy and data ownership. For building owners, the high costs and complexity of developing and deploying these systems limit broader adoption. Moreover, closed systems hinder interoperability, making it difficult to integrate or share data across different platforms.

These challenges extend beyond positioning systems and reflect general issues with user data, where closed applications restrict seamless data exchange from one platform to another. One promising solution involves creating open frameworks that enable seamless data interoperability, giving users greater control over their information while reducing dependence on systems owned and managed by a single organisation. Although regulatory initiatives within the European Union are gradually enabling data interoperability, significant obstacles remain, particularly
in domains where data does not rely on existing standardisations, such as is the case with indoor positioning systems.

This research focuses on enabling seamless data and knowledge exchange between positioning systems, both indoor and outdoor. It addresses the limitations of current approaches by proposing a framework that facilitates the integration, discovery, and management of location data in a way that prioritises user control and interoperability. Central to this research is the development of improved vocabularies that enhance machine understanding of location data, providing a foundation for seamless communication between systems. Additionally, it explores techniques for enabling positioning systems to discover one another without prior coordination, thereby reducing the dependency on pre-established connections and broadening their use and reuse in other use cases. The research also introduces strategies for managing user data effectively, ensuring privacy and flexibility while supporting seamless exchange across systems. These contributions collectively advance the
development of an open, interoperable ecosystem for both indoor and outdoor positioning systems, addressing key limitations of current approaches and paving the way for a future where location data can be stored, shared, and accessed effortlessly across a wide range of use cases and technologies.

This paper explores how Big Data Analytics is revolutionizing the retail sector. With the exponential growth of data from online and offline sources, smart retail systems are leveraging big data to improve customer experiences, inventory management, and operational efficiency. We present a study of various technologies, tools, and frameworks that are enabling this transformation along with real-world examples.

The exponential growth of data and the rising demand for scalable, resilient, and cost-efficient computing resources have driven many enterprises to adopt multi-cloud strategies-leveraging services from multiple cloud vendors such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). While this architectural shift offers numerous benefits including flexibility, vendor independence, and improved fault tolerance, it also introduces significant challenges for data engineering teams tasked with building and maintaining robust data pipelines. This paper provides a comprehensive exploration of the core data engineering challenges in multi-cloud environments, including data integration complexity, increased network latency, fragmented governance protocols, and difficulties in achieving unified observability. Through a structured examination of current literature and industry practices, the study reveals how heterogeneity in cloud architectures creates barriers to seamless big data operations and real-time analytics. In response, the paper proposes a set of strategic frameworks and technical approaches that enable efficient big data integration and analytics across cloud boundaries. These include the adoption of containerized orchestration platforms (e.g., Kubernetes and Apache Airflow), metadata registries (e.g., Apache Atlas), data lakehouse architectures (e.g., Delta Lake, Snowflake), and federated query engines. The paper also evaluates the performance and adaptability of leading ETL tools-such as Apache NiFi, AWS Glue, and Talend-through a comparative analysis supported by tables and performance graphs. Real-world case studies, including those from Netflix and HSBC, illustrate the practical implementations and trade-offs of operating in a multi-cloud environment. The paper concludes by identifying emerging trends such as AI-driven DataOps, decentralized data mesh architectures, and serverless ETL models, which are poised to redefine the future of data engineering. Ultimately, this research serves as both a diagnostic and a prescriptive guide for engineers, architects, and data strategists seeking to navigate the complex terrain of multi-cloud data ecosystems with efficiency, compliance, and innovation.

Le traitement des données en gestion et en recherche vise à collecter, organiser, analyser et interpréter des informations pertinentes pour appuyer la prise de décision ou valider des hypothèses. Il repose sur plusieurs étapes clés : la définition du problème, la collecte des données , leur nettoyage  puis l’analyse descriptive  et statistique . Les données peuvent être quantitatives ou qualitatives, et leur traitement nécessite souvent des outils comme Excel, SPSS, R

The exponential growth in modern systems has significantly transformed how organizations monitor, optimize, and understand their operations. Logs, which record critical facts concerning system performance, user interaction, and infrastructure behavior, play a critical role in diagnosing problems, auditing activities, and facilitating advanced analyses such as predictive modeling for machine learning. Nevertheless, with the rising velocity and volume of logs coming from distributed systems and micro services, conventional log management techniques struggle to aggregate, store, and analyze logs. Previously, centralized logging systems based on relational databases or file storage were employed to collect logs. Although these systems served for small-scale applications, they turned into bottlenecks against the demands of modern distributed systems. Relational databases have scalability challenges, whereas file-based systems don't support complicated analytics or structured queries. Such restrictions hinder real-time insights and consequently influence decision-making processes within time. Integrating Apache Kafka with data lakes has proven to solve such constraints by providing real-time log streaming and affordable and scalable storage. Yet, the problems of data quality, governance, and consistency still exist. This study proposed a unified log management framework leveraging Kafka connect to stream logs into data lakes while ensuring data quality and governances. By implementing automated de duplication and schema evolution within ETL pipelines, this approach addresses key challenges and enables both real time processing and long-term analytics. It unified real time monitoring with predictive maintenance and anomaly detection, optimizing resource utilization in cloud native environment. Future advancement in schema management, data validations and machine learning (ML) integration helps streamline real-time insights and predictive capabilities for improved decision making and operational efficiency.

Big Data alludes to huge amounts of data or pieces of information made by the digitization of everything that gets merged and broken down by explicit advancements. Applied to human services, it will utilize explicit wellbeing information of a populace (or of a specific individual) and conceivably help to prevent epidemics, fix ailment, and chop down costs. Big Data application has a ton of positive and life sparing outcomes. Big data characterizing features incorporate the capacity to deal with massive volume of information and variety at the high speed. Predictive analysis assists doctors with settling on information driven choices within seconds and improve treatment for patients. It should be possible for the patients with complex chronicles, and experiencing various conditions. Heart disease is a pervasive illness which causes demise over the world. A ton of research is going on predictive analytics using machine learning techniques to uncover better choice making. Big Data anal...

Large consumer technology corporations are becoming increasingly influential in health and medicine. While this is sometimes beneficial to public health, it also raises many risks, like inequitable returns to the public sector in public-private medical partnerships or new dependencies on technology firms for the provision of public health goods and services. These risks are not always fully captured by existing frameworks. In this paper, we argue that it is time to adopt a public health ethics perspective on the increasing influence of Big Tech in health and medicine. A public health ethics perspective can not only capture the effects of Big Tech on the health of entire populations, but it also raises the important question of whether governments ought sometimes to intervene in the activities of Big Tech in order to safeguard public health.

In today's dynamic business environment, project management faces increasing complexities and demands for precision, efficiency, and risk mitigation. Big Data Analytics has emerged as a transformative tool, enabling project managers to make data-driven decisions, optimize resources, predict project risks, and enhance overall project outcomes. This research explores how Big Data Analytics integrates with project management processes to drive project success. It highlights key areas such as real-time monitoring, predictive analysis, resource allocation, stakeholder management, and performance evaluation. The study also identifies challenges associated with implementing Big Data solutions, including data privacy, technological infrastructure, and skills gaps. By analyzing current trends, case studies, and future possibilities, this research underscores the critical role Big Data Analytics plays in enhancing project success rates and fostering innovation in project management practices.

The evolution of data platforms is entering a new era characterized by AI-driven automation and self-optimizing capabilities
that address the unprecedented challenges of exponential data growth. As organizations struggle with increasingly complex
data ecosystems, traditional management approaches are becoming inadequate. This document presents how next-generation
data platforms leverage artificial intelligence to transform data operations through four key innovations: metadata intelligence
serving as the nervous system of modern platforms; self-healing data pipelines that autonomously detect and resolve issues;
predictive resource optimization that anticipate computational needs before they arise; and embedded governance frameworks
that make compliance an integral rather than external function. These advancements collectively shift data management from
reactive to proactive paradigms, enabling organizations to derive more excellent value from their information assets while
reducing manual intervention and operational costs.

The landscape of consumer behavior in insurance markets has evolved significantly in recent years, driven predominantly by advancements in big data analytics. This paper examines the dynamics inherent in bundled insurance offerings, emphasizing the role of data-driven insights in shaping consumer preferences and decision-making processes. By analyzing extensive datasets from various channels-such as online interactions, customer feedback, and transaction records-this study delineates the critical factors that influence consumer choices when selecting bundled insurance products. Through a multifaceted analytical approach, the research reveals patterns and correlations that were previously obscured, ultimately providing a comprehensive understanding of consumer tendencies. The findings indicate that consumers are increasingly drawn to personalized bundled offerings that align with their unique needs and risk profiles. Such preferences are shaped by their experience with digital interfaces, pricing strategies, and the perceived value of the bundles. By leveraging machine learning algorithms and predictive modeling techniques, insurers can segment their customer base more effectively, leading to tailored marketing strategies that resonate with specific demographics. The application of big data analytics not only enhances operational efficiency but also fosters deeper customer engagement by enabling insurers to anticipate and respond to evolving consumer demands. In conclusion, this paper contributes to the field by illustrating how big data analytics can transform the approach to consumer behavior analysis in the insurance sector, especially regarding bundled products. The integration of these insights into business strategies allows insurers to maximize their competitive edge, fostering innovation while meeting the nuanced needs of contemporary consumers. As the industry continues to adapt to technological advancements, the recommendations provided outline a pathway for insurance firms aiming to enhance customer satisfaction and loyalty through informed, data-driven decision-making processes.

This paper argues that investing in artificial intelligence (AI) in developing economies involves significant trade-offs requiring ethical, financial, and geopolitical scrutiny. While AI is increasingly seen as a vehicle for technological leapfrogging, such ambitions often mask structural constraints, including weak infrastructure, limited institutional capacity, and external dependency. Using the economic theory of opportunity cost-extended through the political economy and digital governance perspectives-this study critically examines AI policy strategies in Ghana, Kenya, and Rwanda. A qualitative design grounded in secondary data and a thematic analysis reveal how AI investment may reallocate scarce resources away from essential services, exacerbate inequality, and entrench strategic technological dependency. This paper proposes a public policy framework built on four principles-sequential readiness, strategic alignment, ethical governance, and capacity building-to guide equitable AI deployment. It argues for establishing a digital social compact between states, citizens, and technology actors to safeguard public interest in AI-driven development. Finally, this paper outlines a future research agenda emphasizing the mixed-method evaluation of AI's long-term social impacts, including employment, inclusion, and public service delivery.

The evolution of data ma0nagement has necessitated the development of efficient Change Data Capture (CDC) mechanisms to ensure real-time data synchronization across disparate systems. Traditional CDC methodologies, including log-based and trigger-based approaches, often face significant challenges related to latency, schema evolution, and resource consumption. These inefficiencies become more pronounced as organizations scale their data infrastructure to accommodate increasing transactional workloads. While cloudnative platforms such as Google BigQuery and Google Spanner offer enhanced data ingestion capabilities, they still require advanced techniques to mitigate schema drift, minimize processing overhead, and improve fault tolerance. Despite the advancements in CDC technologies, existing approaches remain limited in their adaptability and efficiency. There is a notable gap in research focusing on the integration of artificial intelligence to enhance CDC processes, particularly in differentiating how AI-driven mechanisms perform in cloud-based databases compared to traditional database frameworks. This study seeks to address this gap by introducing a novel AIenhanced CDC framework, leveraging machine learning techniques to optimize schema evolution, event anomaly detection, and real-time data consistency. By benchmarking BigQuery's streaming ingestion, Google Spanner's change streams, and Debezium's log-based CDC, this research presents a comparative analysis to evaluate the impact of AI integration in CDC. The findings of this study indicate that AI-driven CDC solutions significantly outperform conventional methods, demonstrating substantial improvements in reducing latency, enhancing anomaly detection accuracy, and optimizing computational resources. The research underscores the role of predictive analytics and reinforcement learning in CDC, showcasing their ability to automate schema management and refine event tracking processes. The study's results highlight the transformative potential of AI-driven CDC, paving the way for more efficient, scalable, and intelligent data management solutions in modern enterprises.

Big Data processing and analyzing is very crucial across various industries. Rapidly increasing data volume and multiple sources of data generation it has created a challenge to analyze the huge amount of data. Traditional relational database management system RDBMS cannot process the load of data generated. Hadoop Distributed File System (HDFS) is a distributed and enormously parallel processing system suited for big data whereby the storage is created on the distributed file system. Single point of failure is one of the key challenges due to the existence only one single Name Node in the existing HDFS architecture. Metadata summarizes basic information about the distribution of data blocks, block size information and replica management. Newer HDFS architecture for big data storage eliminates the concept of Name Node with the functionalities of the Name Node being stores the metadata using blockchain technology. mathematical cryptographic techniques like hashcode with the help of B...

This report outlines the initial phase of a machine learning-driven endeavor focused on predicting used car prices. Using a Kaggle dataset of 46,000+ used car listings from Pakistan, we conducted data cleaning, analysis, and visualization. This involved encoding categorical features, normalizing numerical ones, and revealing key trends in popular car makes, models, and locations. These findings guided our approach for evaluating regression algorithms in the next phase to predict resale prices. The outcomes of this initial exploration lay the groundwork for subsequent model building and validation.

The examines cognitive and affective considerations in decisions regarding online purchasing, with a focus on the fashion e-commerce sector. It explains key user experience (UX) techniques designed to minimize user abandonment in the consumer experience. The paper emphasizes the need for a seamless checkout design, citing that the reduction of form fields, loading times, and navigation complexity can improve user satisfaction and conversion rates. It demonstrates how perceived security and trust markers like third-party seals, customer reviews, and transparent return policies are key to establishing consumer confidence. Additionally, the study examines the influence of behavioral nudges like urgency cues, personalized offers, and default options from a human-computer interaction point of view. By employing data drawn from A/B testing, heatmaps, and Google Analytics, the study identifies user behavior trends that inform design decisions. Additionally, it assesses the influence of visual design elements and mobile-first interfaces on decision-making processes. By applying digital psychology principles, the study examines emotional drivers like anxiety relief, convenience, and overall satisfaction. The article positions UX design as a strategic tool for establishing trust and engaging consumers. Empirical studies and academic literature are employed to validate these findings, providing practical advice for UX designers and e-commerce platforms. Overall, the findings enhance the understanding of how user-centered design can improve online shopping experiences and improve conversion rates.