Big Data Analytics

This project tries to realize to what extent these factors predict the student's debt repayment. As a guideline, accuracy of the prediction system, which is assessed using RMSE (Root Mean Squared Error), should be a maximum of 10-11 on the hold-out from the training data.

Artificial Intelligence has emerged as a pivotal driver of deep digital transformation across
Supply Chain processes. However, this disruptive change poses several challenges, particularly
in incorporating AI algorithms into these processes, which necessitates their reengineering
often carried out through siloed actions among practitioners. To address this gap, integrative
models to unify these actions are needed. While some studies have explored such models, the
literature lacks a consolidated model applicable to both industry and research. This study aims
to bridge this gap by proposing an integrative AI-Process Framework, developed through an
in-depth exploratory approach built upon multiple conceptual components. Experimental
application to three industrial cases revealed three critical findings: poor coordination and
fragmented AI adoption among supply chain stakeholders, challenges with early-stage AI
integration due to its complexity, and deficient consideration of social implications in AI
deployment. These findings pave the way for future research to focus on developing a more
coordinated strategy for effective AI-value creation in modern supply chains.

Le web est devenu participatif et de nouvelles pratiques de catégorisation de l'information fondées sur des communautés, des stratégies collectives d'indexation et de partage des ressources en ligne émergent. Le monde éducatif est particulièrement concerné. En nous appuyant sur la théorie du connectivisme de George Siemens, nous analysons les pratiques de socialbookmarking dans le domaine de l'éducation. Nous montrons que les espaces de veille et de partage de ressources pédagogiques dans ces communautés peuvent être des lieux privilégiés de personal knowledge management, et de développement de compétences professionnelles. Le personal learning environment (PLE) y a toute sa place. Malgré les tensions en jeu, ils participent à la définition de nouveaux cadres de formation que les systèmes éducatifs ne peuvent ignorer au 21 e siècle.

The rapid evolution of digital health has significantly transformed the landscape of telehealth services, offering innovative solutions to enhance patient care and healthcare delivery. This study delves into the pivotal role of digital health technologies in telehealth, emphasizing their contribution to more effective, efficient, and accessible healthcare services. The focus shifts from a heavy reliance on AI and Large Language Models (LLMs) to a broader spectrum of digital health tools, including electronic health records, telemedicine platforms, mobile health applications, and data analytics. Through a systematic review of literature spanning from 2010 to 2020, this research highlights how digital health tools are reshaping patient-provider interactions, improving diagnostic accuracy, and promoting patient engagement and self-management. The findings indicate a significant positive impact of digital health on telehealth services, suggesting a future where technology-driven healthcare becomes more patient-centered and data-informed. This transition, while promising, also underscores the need for robust cybersecurity measures and addresses the challenges of integrating digital health tools into existing healthcare systems.

This paper represents the concept of keys and relational algebra in database management system. Basically key is an attribute that is able to identify the records in given relation uniquely. Relational algebra is procedural query language. It takes instance of relations as an input and gives the desired output in form of relation. For evaluating desired output there may be several queries in relational algebra but based on record comparisons and block retrievals efficiency of query is calculated and after identifying efficient query, that query is applied to find out the desired set of records in relational algebra.

In-situ processing has received a great deal of attention in recent years. In in-situ scenarios, big raw data files which do not fit in main memory, must be efficiently handled using commodity hardware, without the overhead of a preprocessing phase or the loading of data into a database. In this work, we present an adaptive indexing scheme that enables efficient visual exploration and analytics over big raw data files. Beyond visual exploration and statistics, the scheme enables categorical-based analytics using group-by and filter operations. The proposed scheme combines a tile-based structure that offers efficient exploratory operations over the 2D space, with a tree-based structure that organizes a tile's objects based on their categorical values, enabling efficient visual analytics and the support of advanced visualization methods. The index resides in main memory and is built progressively as the user explores parts of the raw file, whereas its structure and level of granularity are adjusted to the user's exploration areas and type of analysis. We conduct experiments using real and synthetic datasets, and demonstrate that the proposed approach, is in most cases more than 40× faster compared to the existing solutions, and performs around 3 orders of magnitude less I/O operations.

Data exploration and visual analytics systems are of great importance in Open Science scenarios, where less tech-savvy researchers wish to access and visually explore big raw data files (e.g., json, csv) generated by scientific experiments using commodity hardware and without being overwhelmed in the tedious processes of data loading, indexing and query optimization. In this work, we present our work for enabling efficient query processing on raw data files for interactive visual exploration scenarios. We introduce a framework, named RawVis, built on top of a lightweight in-memory tile-based index, VALINOR, that is constructed on-the-fly given the first user query over a raw file and adapted based on the user interaction. We evaluate the performance of prototype implementation compared to three other alternatives and show that our method outperforms in terms of response time, disk accesses and memory consumption.

In-situ processing has received a great deal of attention in recent years. In in-situ scenarios, big raw data files which do not fit in main memory, must be efficiently handled on-the-fly using commodity hardware, without the overhead of a preprocessing phase or the loading of data into a database system. This paper presents RawVis, an open source data visualization system for in-situ visual exploration and analytics over big raw data. RawVis implements novel indexing schemes and adaptive processing techniques allowing users to perform efficient visual and analytics operations directly over the data files. RawVis provides real-time interaction, reporting low response time, over large data files, using commodity hardware.

Data exploration and visual analytics systems are of great importance in Open Science scenarios, where less tech-savvy researchers wish to access and visually explore big raw data files (e.g., json, csv) generated by scientific experiments using commodity hardware and without being overwhelmed in the tedious processes of data loading, indexing and query optimization. In this paper, we present our work for enabling efficient query processing on large raw data files for interactive visual exploration scenarios and analytics. We introduce a framework, named RawVis, built on top of a lightweight in-memory tile-based index, VALINOR, that is constructed on-the-fly given the first user query over a raw file and progressively adapted based on the user interaction. We evaluate the performance of a prototype implementation compared to three other alternatives and show that our method outperforms in terms of response time, disk accesses and memory consumption. Particularly during an exploration scenario, the proposed method in most cases is about 5-10× faster compared to existing solutions, and requires significantly less memory resources.

Im Zuge stetig steigender Komplexitat in Business-Intelligence- Systemen nimmt auch die Bedeutung des Metadatenmanagements (MDM) kon- tinuierlich zu. Die vorliegende Studie untersucht den Status Quo des MDM in der Business Intelligence (BI) aus Sicht der drei BI-Stakeholder-Typen Endan- wender, Entwickler und Entscheider. Die Ergebnisse veranschaulichen, in wel- chen Anwendungsgebieten des MDM derzeit Handlungsbedarf gesehen und wo die grosten Herausforderungen fur die Weiterentwicklung verortet werden. Bei den befragten BI-Stakeholder-Typen zeigen sich Abweichungen in der Bewer- tung der Anwendung des MDM in der BI-Entwicklung, im BI-Betrieb und in der organisatorischen Einbettung. Die differenzierte Analyse der Wahrnehmun- gen der drei Stakeholder-Typen liefert insbesondere BI-Entscheidern Anhalts- punkte fur Priorisierungsentscheidungen unter Berucksichtigung der divergie- renden Fach- und IT-Perspektiven.

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Demand for solar energy increases and it is required to manage the supply of energy effectively. Accurate detection on patterns of energy consumed assist in taking appropriate decisions on generating energy. Even though many traditional techniques have predicted the consumption rate, still improvement is needed in prediction accuracy. The pre-processing is performed initially for handling missing values. The feature selection is accomplished using improved greedy based genetic algorithm (GGA) to extract best features to enhance the performance of the model. Output from feature-selection is passed as input to the classification phase using proposed deep convolutional neural network (CNN) in which future solar energy patterns are classified and predicted timely basis power consumption and it optimize the model by minimize the error. The prediction accuracy is estimated through evaluation metrics such as mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) 0.423, 0.652, and 0.215, respectively. The outcomes achieved in terms of accuracy at 99.75, precision at 99.28, sensitivity, and recall at 100 revealed the efficiency of the proposed classification model. As a result, the proposed future prediction of solar energy was considered efficient since it achieved reduced error values than other prediction algorithms. It assists in maintaining stability in solar-energy based systems.

في حين أن هناك إحصائيات متفرقة حول الوقف الإسلامي، إلا أن هناك حاجة إلى مزيد من الشفافية والتنسيق بين الدول الإسلامية لجمع بيانات شاملة ودقيقة. يمكن أن يساعد الذكاء الاصطناعي وتحليل البيانات في تحسين إدارة الأوقاف وزيادة تأثيرها في المجتمع.

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, they carry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order for an organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimize employee attrition. This study identifies employee related attributes that contribute to the prediction of employees' attrition in organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeria who worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job related records of the employee were the main data which were used to classify the employee into some predefined attrition classes. Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree models and rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictive model that was used to predict new cases of employee attrition. A framework for a software tool that can implement the rules generated in this study was also proposed.

In the era of 5G and the Internet of Everything (IoE), the roles of artificial intelligence (AI), cloud computing, and big data technologies in the realm of digital finance and security will become ever more crucial as the number of online payments steadily increases. The transformation of AI, cloud computing, and big data technologies decrees the paradigm of integration, a technological push towards building a robust framework for digital finance security. These technologies play a pivotal role in smart and efficient automation by enhancing decision-making and the scalability of security measures. The use of AI, cloud computing, and big data technologies is geared to serve as a surveillance and auditing mechanism for the monitoring of real-time transactions in payment systems. The blend of AI, cloud computing, and big data technologies cultivates the integration paradigm, shaping the framework of digital finance security. It is imperative for policy makers and stakeholders in the pertinent areas of software engineering to be attuned to the threats of evolving digital cyber-financial systems. This work highlights the use of AI, cloud computing, and big data technologies as a surveillance and auditing mechanism for the purpose of monitoring real-time transactions in payment systems. Finally, a discussion and future work are outlined.

In various marketing fields, rewarding customer behavior is an effective and successful practice. Loyalty programs serve as one of the very well-known marketing tools for rewarding customers for repeat purchases or repeat patronage. Different behavioral sequences are rewarded with some type of points or benefits, and these rewards are usually redeemable after reaching some predetermined levels. Inherently, loyalty programs are designed to motivate customer transactional behavior, leading to several highly researched topics such as optimal reward and level configurations. However, consumer behavior and loyalty program characteristics have been investigated in terms of their macroscopic interactions. Hence, there will be numerous opportunities to create or enhance consumer insights and proper mechanisms generating or interacting with them better insights being at the sequence, item level, and at the consumer level. This paper attempts to analyze consumer loyalty program interactions within banking transactional datasets and develops generative artificial intelligence based methods to enhance insights or generate mechanisms. The proposed methods and generated models for enhanced insights are then evaluated within a loyalty bank setting. When designing new forms creating customer rewards and be nefit programs operating such generative AI algorithms, that choice dictates the entire personalized algorithmic framework. Eliciting customer information in the form of simple, conventional data structures may not be expressive or complex enough to fit such approache s. At the least, care should be taken so that consumer information can be molded for direct consumption by leveraging generative AI frameworks, the study and exploration of which is currently barely starting, a vast and rich field. Therefore, financial services looking to operate such customer personalization algorithms are advised to thoroughly research and consult hyper-personalization scheme experts and plan for significant investments and deployments to benefit from major AI leaps in analytics and personalization.

This paper provides an in-depth consideration of generative AI-driven automation in integrated payment solutions. It begins by identifying how neural network-enabled insights, including automated analytics, autoencoding, fuzzy modeling, and various optimization strategies, contribute towards the addition of methods with insights in processing diverse financial transactions. In the era of the Internet of Things (IoT), the adoption of said technologies may significantly enhance accuracy and expedite processing, thereby contributing to synergistic gains. It goes on to motivate the importance of our study by identifying its contribution towards the particular review objectives. An activity system is then offered, which benefits actors who are attempting to adjust to the dynamic metamorphosis of payment solutions, to cushion the impact of disruptive AI technologies in payment solutions. We expect that the implementation of generative AIs is the engine to which businesses of the new era will be compelled to attach improved payment systems, as such systems will help all stakeholders simplify operations from their respective operating fields' perspectives. The prevalence of AI is clear from the resilient addition to capacity enhancement of performance management and business intelligence, each by 33%. AI's impacts on banking and payment systems are much more conspicuous if one considers how a significant percentage of banking units deploy chatbots and AI systems. It is unavoidable to neglect that AI will essentially change the way payments function as neural networks within generative AIs are poised to transform payment in businesses and management units. Accordingly, this paper is organized as follows: in the proceeding section, the paper's main content is presented.

The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a stateof-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies' potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.

The community pharmacy is a commercial establishment regulated as a public service.
The normative regulations that have taken place since the year 2000 have compromised, to a
large extent, the profitability of these small companies. Therefore, it has become necessary to
change the management model to compensate for these benefits. This work makes use of
new technologies and, specifically, those related to Industry 4.0, to devise a solution to meet
this need.
Using Design Thinking and Business Model Canvas methodologies, the needs to be
covered by the proposed solution in the work are delimited, to finally obtain a solution where
various technologies such as IoT, Big Data or Machine Learning interact, resulting not only in
the optimization of the management and economic results of the pharmacy, but also in an
improvement of the user's shopping experience.
This work proposes the development of a system based on Industry 4.0 to help
community pharmacies improve their profit margins, reduce their costs, and improve the
customer experience. The business model that will govern its commercialization is defined, as
well as the feasibility or financial plan, which concludes with a "viable" result regarding the
development of this solution. Therefore, it could be put at the service of Spanish pharmacies.
The solution provided could be extrapolated to other businesses in other sectors in the
development of its final stage.

Development of mobile learning projects involves addressing many challenges, from pedagogies (e.g., what must students learn? and how?) to technical issues (e.g., use of context-awareness, use of communication or collaboration methods, mash-up of information). This paper introduces a framework intended to provide different educational tools and information to mobile learning programmers and designers in order to simplify and reduce the project development. The paper offers an overview of the state of the art of mobile learning applications, focusing on some of its problems: lack of interoperability and difficulty of using advanced technologies, such as location-based systems. It also addresses why most of the m-Learning applications do not make use of the existing services and knowledge of the Learning Management Systems, which are in fact the real pillars of the e-learning methodology. The framework design is guided by a new paradigm for development of e-learning tools and platforms: Learning as a Service (LaaS). The main contribution of this framework is to provide contextual information from different sources and sensors (e.g., geographical, motion); and integration of many existing services from e-learning platforms.

6th International Conference on Big Data (CBDA 2025) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data.

Thanks to all of my software engineering colleagues-Mazin, Mario, Ali, Ahmet, Reem, Rsha, and Afnan, for their help and support. Thank you to all of my colleagues in Project EPIC-Jennings, Marina, Melissa, Robert, Joanne, and Lise, who participated in many sessions of my interviews, for helping me learn about their research practices in crisis informatics research. Their cooperation was a great help in fulfilling my research goals. Big thanks go to my home university, King Abdulaziz University, Jeddah, Saudi Arabia, that granted me a scholarship to get this degree, which I'm so grateful for. Finally, thanks to my small family, Ehab and Joanna, for supporting me with the time, love and care I needed, and for my great mother and brothers, parents-in-law and sister-in-law Rehab, for their prayers and thoughts. v CONTENTS CHAPTER 1 .

Global energy requirements are continuously increasing. Conventional methods of producing more energy to meet this growth pose a great threat to the environment. CO 2 emissions and other bi-products of energy production and distribution processes have dire consequences for the environment. Efficient use of energy is one of the main tools to restrain energy consumption growth without compromising on the customers requirements. Improving energy efficiency requires understanding of the usage patterns and practices. Smart energy grids, pervasive computing, and communication technologies have enabled the stakeholders in the energy industry to collect large amounts of useful and highly granular energy usage data. This data is generated in large volumes and in a variety of different formats depending on its purpose and systems used to collect it. The volume and diversity of data also increase with time. All these data characteristics refer to the application of Big Data. "All praises be to God, on whom we depend for sustenance and guidance." I would like to express my sincere gratitude to my supervisor Professor Matti Vartiainen and advisor Professor Jukka Nurminen for their continuous motivation, support and guidance. I would also like to give very special thanks to my instructor Ms. Sanja Scepanovic for taking keen interest in this research and providing conscientious directions throughout the process. I would take this opportunity to highlight CIVIS project and VTT's Green Campus initiative as the main source of inspiration for my work. I would like to extend my special thanks to Mr. Janne Peltonen from VTT for his counselling and efforts in arranging the data for my research. Last but not the least, I would like to mention the contribution of my family as the main source of strength in my life. I am grateful for a wife who believes in me more than I do myself, the unconditional support and affection of my parents and most of all for the refreshing energy that my little fairy, Zeenia, induces in me.

Global energy requirements are continuously increasing. Conventional methods of producing more energy to meet this growth pose a great threat to the environment. CO 2 emissions and other bi-products of energy production and distribution processes have dire consequences for the environment. Efficient use of energy is one of the main tools to restrain energy consumption growth without compromising on the customers requirements. Improving energy efficiency requires understanding of the usage patterns and practices. Smart energy grids, pervasive computing, and communication technologies have enabled the stakeholders in the energy industry to collect large amounts of useful and highly granular energy usage data. This data is generated in large volumes and in a variety of different formats depending on its purpose and systems used to collect it. The volume and diversity of data also increase with time. All these data characteristics refer to the application of Big Data. "All praises be to God, on whom we depend for sustenance and guidance." I would like to express my sincere gratitude to my supervisor Professor Matti Vartiainen and advisor Professor Jukka Nurminen for their continuous motivation, support and guidance. I would also like to give very special thanks to my instructor Ms. Sanja Scepanovic for taking keen interest in this research and providing conscientious directions throughout the process. I would take this opportunity to highlight CIVIS project and VTT's Green Campus initiative as the main source of inspiration for my work. I would like to extend my special thanks to Mr. Janne Peltonen from VTT for his counselling and efforts in arranging the data for my research. Last but not the least, I would like to mention the contribution of my family as the main source of strength in my life. I am grateful for a wife who believes in me more than I do myself, the unconditional support and affection of my parents and most of all for the refreshing energy that my little fairy, Zeenia, induces in me.

The web is being used more and more by users of mobile devices. In addition, it is increasingly possible to track the user's location, which provides immense opportunities in geospatial data and its management. Due to the use of location information in services for each mobile device, a large size of spatial data makes it difficult to process spatial queries efficiently and, therefore, we need a lightweight and scalable approach to process large amounts of stored data in distributed file systems. For the most part, all SNSs (social network services) focus on connecting the user account with their location information, such as check-in services, which helps them collect information about user activities and ratings. Of location, but also increases the load of data on their servers. . In this article we propose an indexing technique in combination with efficient processing of Boolean top-k spatial queries where location data is compressed to save space and the Boolean query helps filter results so that unrelated data is not processed, what helps to save space and faster processing of queries.

The integration of multidimensional big data analytics with dynamic financial risk assessment has become pivotal in enhancing decision-making frameworks across industries. This study explores the synergy between advanced analytics techniques and financial risk management, leveraging historical and predictive data to optimize decision accuracy and efficiency. A conceptual framework is developed, highlighting the interplay between data dimensions, financial indicators, and adaptive modeling. The findings emphasize the transformative potential of big data analytics in financial decision-making, offering significant implications for policymakers and organizations.

AI-driven predictive analytics plays a crucial role in advancing digital transformation within the financial technology (fintech) sector. This paper explores how artificial intelligence (AI) algorithms analyze large datasets to forecast trends, manage risks, and enhance operational efficiency in fintech. By leveraging predictive analytics, fintech companies can make data-driven decisions, personalize customer experiences, and improve financial services. This study reviews various AI-driven predictive models and their applications in fintech, highlighting their impact on innovation and competitiveness. Case studies illustrate successful implementations, emphasizing the transformative potential of AI-driven predictive analytics in driving fintech forward.

Traditional association rule mining based on the support-confidence framework provides the objective measure of the rules that are of interest to users. However, it does not reflect the utility of the rules. To extract non-redundant association rules in support-confidence framework frequent closed itemsets and their generators play an important role. To extract non-redundant association rules among high utility itemsets, high utility closed itemsets (HUCI) and their generators should be extracted in order to apply traditional support-confidence framework. However, no efficient method exists at present for mining HUCIs with their generators. This paper addresses this issue. A postprocessing algorithm, called the HUCI-Miner, is proposed to mine HUCIs with their generators. The proposed algorithm is implemented using both synthetic and real datasets.

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters -in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB TM , significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.

Data cleansing is an important prerequisite for reliable data evaluation and system-gaining knowledge of programs, directly impacting the exceptional insights and model overall performance. This paper provides a comprehensive examination of modern information-cleaning methodologies, focusing on their sensible packages and effectiveness across diverse datasets. We analyze six primary categories of information cleansing techniques: missing statistics management, outlier detection, information standardization, reproduction removal, consistency validation, and data type transformations. Our systematic assessment exhibits that automated information cleansing pipelines, at the same time efficient, require careful configuration primarily based on area context. Key findings imply that hybrid procedures-combining statistical techniques with area-precise policies-attain advanced consequences compared to standalone strategies, showing a 23% improvement in statistically satisfactory metrics. We also perceive that early-stage records validation significantly reduces downstream processing mistakes by 45%. The implications of this research suggest that companies ought to implement iterative facts-cleaning workflows, incorporating continuous validation and area expert remarks. Furthermore, our findings emphasize the significance of documenting cleansing decisions to ensure reproducibility and keep information lineage. This work offers a structured framework for practitioners to select and implement suitable facts-cleaning techniques based totally on their particular use cases and facts characteristics.

As the volume of data generating is increasing continuously in this internet world, the term Big Data is becoming a very popular jargon in today's market. Big Data is used in various sectors of the digital world. In this paper an exertion is made to exhibit that even the kid mental health are venturing into Big Data pool to take all advantages from its different propelled instruments and advancements. The paper introduces the audit of endeavors made in youngster mental health area utilizing Big Data ideas and procedures. Age of technology we live in, every aspect of our life is almost digitalized, be it physical or virtual aspects. One of the most important phases of everybody’s life is undoubtedly their childhood. The base of entire life is built on childhood. We can shape up an entire generation into almost perfection through just tracking and analyzing the right at the start of life. We use Big Data to preserve information about a child right from the start of their lives, wh...

Penelitian ini melakukan analisa perbandingan data warehouse cloud computing dengan data warehouse konvensional yang pada umumnya semua data yang tersimpan pada data warehouse tersebut harus dilindungi dengan menggunakan protokol kriptografi dengan algoritma yang paling aman. Sehingga data yang tersimpan pada warehouse tersimpan dengan aman dan terkontrol. Selain itu, setiap jenis usaha perlu menjabarkan kebutuhannya dalam memilih penyimpanan data sentralisasi, desentralisasi, atau kedua cara penyimpanan serta pertimbangkan efektifitas dan efisiensi bagi perusahaan memilih cloud atau konvensional. Keterkaitan antara data warehouse dan kriptografi ini akan menunjukan kehandalan dari suatu sistem dalam menjaga kode rahasia antara pihak terkait. Kata Kunci : Data, Sistem Informasi, Teknologi Informasi, Data Warehouse, Cloud Computing, Kriptografi, Algoritma

In an era where generative AI (GenAI) is reshaping industries, public understanding of this phenomenon remains limited. This study addresses this gap by analysing public beliefs about GenAI using the Technology Acceptance Model (TAM) and Diffusion of Innovations Theory (DOI) as frameworks. We adopted a big-data approach, utilising machine-learning techniques to analyse 21,817 public comments extracted from an initial set of 32,707 on 44 YouTube videos discussing GenAI. Our investigation surfaced six pivotal themes: concerns over job and economic impacts, GenAI's potential to revolutionise problemsolving, its perceived shortcomings in creativity and emotional intelligence, the proliferation of misinformation, existential risks, and privacy decay. Emotion analysis showed that negative emotions dominated at 58.46%, including anger (22.85%) and disgust (17.26%). Sentiment analysis echoed this negativity, with 70% negative. The triangulation of thematic, emotional, and sentiment analyses highlighted a polarised public stance: recognition of GenAI's transformative potential is tempered by significant concerns about its implications. The findings offer actionable insights for engineering managers and policymakers. Strategies such as awareness-building, transparency, public engagement, balanced communication, governance, and human-centred development can address polarisation and build trust. Ongoing research into public opinion remains essential for aligning technological advancements with societal expectations and acceptance.

The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies’ social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers’ wor...

The aims of this study were threefold: 1) study the research gap in carpark and price index via big data and natural language processing, 2) examine the research gap of carpark indices, and 3) construct carpark price indices via repeat sales methods and predict carpark indices via the AutoML. By researching the keyword "carpark" in Google Scholar, the largest electronic academic database that covers Web of Science and Scopus indexed articles, this study obtained 999 articles and book chapters from 1910 to 2019. It confirmed that most carpark research threw light on multi-storey carparks, management and ventilation systems, and reinforced concrete carparks. The most common research method was case studies. Regarding price index research, many previous studies focused on consumer, stock, press and futures, with many keywords being related to finance and economics. These indicated that there is no research predicting carpark price indices based on an AutoML approach. This study constructed repeat sales indices for 18 districts in Hong Kong by using 34,562 carpark transaction records from December 2009 to June 2019. Wanchai's carpark price was about four times that of Yuen Long's carpark price, indicating the considerable carpark price differences in Hong Kong. This research evidenced the features that affected the carpark price indices models most: gold price ranked the first in all 19 models; oil price or Link stock price ranked second depending on the district, and carpark affordability ranked third.

Big data is, without doubt, a hot topic nowadays, moreover because the development of new technology makes it possible to analyze all available ever-growing data which easily amasses terabytes of information. However, big data poses both a big opportunity and a big challenge at the same time, as the power does not truly rely on processing it anymore, but rather on how it can be transformed into meaningful knowledge that can be used to make intelligent decisions. The big data age requires a big data mindset. Hence, the implications of big data are, not to say the least, revolutionary and in this article, we discuss the meaning of big data and argue over its strategic implications for the firm. We believe that in order for big data to become an asset and serve business effectively, trying to define the dimensions of big data is of utmost importance. In consequence, the present article presents a perspective which combines the conventional and more technical dimensions of big data with the more challenging dimensions of context, connectedness and complexity. It is argued that by understanding this expanded perspective, decision makers can much better appreciate the value added which big data has to offer and how it can become a competitive advantage for the firm.

Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.

The purpose of the study was to apply Big Data analytics as a tool for evaluating electronic resources usage in the academic library setup in Kenya with reference to the library of one private university. Log files of postgraduate students were mined from the server where the offsite access platform (ezproxy) has been installed. Descriptive statistical techniques such as mean, standard deviation and percentages were computed. Data was transferred to the Statistical Package for Social Sciences (SPSS) software was aided in the analysis. Results revealed that in terms of usage intensity, total URL count was 2,352, the highest user made 283 downloads and the mean URL count was 49 downloads. Further findings revealed that no user utilized more than 5 databases over a period of one year. The mean usage intensity score for respondents who were trained or orientated on e-resource usage was above average at 69.0 while those who had not received training were below average at 29.8. It was concluded that big data analytics is a necessary and powerful tool for investigating electronic resources seeking and usage trends and patterns within Kenyan university libraries. Through big data analysis and data mining, usage patterns and trends such as usage intensity that might not have accurately been revealed through other tools are unearthed. Big data analytics has revealed user preferences and intensity of utilization of various databases and helped in detection of redundant databases. From the usage patterns, it was clear that the level of utilization of the University library's e-resource platform was very low. Most of the databases accessible through the platform were redundant. Further, only two databases namely e-book central and ebscohost were the most popular among users while the rest were barely being utilized if at all. For most students, just one or two databases were sufficient in meeting their research needs. An integrated data analytics model for investigating university library's e-resources usage is proposed.

Big Data Analytics (BDA) has emerged as a transformative force in
healthcare, offering innovative solutions to analyze large and complex
datasets for actionable insights. This systematic review, encompassing 142
peer-reviewed studies published between 2010 and 2024, explores the tools,
techniques, and applications of BDA in healthcare. The findings reveal the
critical role of BDA in enhancing clinical decision-making, optimizing
hospital workflows, and advancing medical research. Key applications such
as predictive analytics for disease prevention, real-time monitoring through
IoT integration, and precision medicine through genomic analysis are
highlighted. Tools like Hadoop, Spark, and TensorFlow, combined with
advanced techniques such as machine learning and natural language
processing, have been pivotal in transforming healthcare data into
actionable knowledge. However, the review also identifies significant
challenges, including data integration issues, algorithmic bias, and ethical
concerns related to patient privacy and data security. By addressing these
barriers, BDA has the potential to revolutionize healthcare delivery,
providing more personalized, efficient, and equitable care. This study
provides a comprehensive understanding of the current state of BDA in
healthcare, its limitations, and its promising future applications, offering
valuable insights for researchers, policymakers, and healthcare
practitioners.

Rahapelihaitat nähdään yhteiskunnan hyvinvoinnin kannalta suuriksi, minkä johdosta asiaan on katsottu välttämättömäksi puuttua lainsäädännöllisin keinoin ja lisäämällä rahapelaamisen vastuullisuustoimia. Tämän lisäksi rahapeliautomaatteja on pidetty suljettuina koronavirusepidemian vuoksi. Veikkaus Oy arvioi, että toimenpiteiden seurauksena valtiolle tuloutetut vuosittaiset rahapelituotot vähenevät noin 300 miljoonalla eurolla. Samalla kun rahapelihaitat vähenevät, tämä merkitsee rahapelituotoilla rahoitettujen edunsaajien rahoituksen alenemista. Selvityksessä tarkastellaan neljää eri vaihtoehtoa suomalaisen rahapelijärjestelmän kehittämistyölle. Työryhmä asettaa ensisijaiseksi vaihtoehdoksi kokonaisuudistuksen, jossa rahapelituotot ohjataan valtion yleiskatteellisiksi tuloiksi ja nykyiset edunsaajat siirretään budjetin kehysmenettelyn piiriin. Edunsaajien ja kansalaisjärjestötoiminnan rahoituksen mitoittaminen, tehostaminen ja vaikuttavuuden arviointi tarvitsee huolellisen valmiste...

This paper conceptualizes the term big data and describes its relevance in social research and journalistic practices. We explain large-scale text analysis techniques such as automated content analysis, data mining, machine learning, topic modeling, and sentiment analysis, which may help scientific discovery in social sciences and news production in journalism. We explain the required e-infrastructure for big data analysis with the use of cloud computing and we asses the use of the main packages and libraries for information retrieval and analysis in commercial software and programming languages such as Python or R

Big data elah menjadi salah satu pendorong utama inovasi dan transformasi digital di berbagai sektor. Penelitian ini bertujuan untuk mengeksplorisasi peluang yang ditawarkan oleh big data dalam mendorongnya inovasi dan transformasi digital. Metode yang saya gunakan adalah tinjauan pustaka dan analisis. Kualitatif terhaddap studi kasus di beberapa jurnal industri. Hasil penelitian menunjukan bahwa big data dapat meningkatkan pengambilan keputusan, personalisasi layanan, dan efisinsi oprasional. Namun, tantangan seperti privasi data dan kebutuhan akan infrastruktur yang memadai juga perlu diperhatikan. Kesimpulan dari penelitian ini menekankan pentingnya pemanfaatan big data untuk mencapai titik keunggualn kompetitif di era digital.

ABSTRAK Kemajuan teknologi jaringan data (networked data) telah memfasilitasi perkembangan pesat dalam aplikasi Internet of Things (IoT). Dengan terhubungnya berbagai perangkat pintar melalui jaringan, data yang dikumpulkan dan diproses menjadi sangat penting untuk pengambilan keputusan dan pemantauan sistem secara real-time. Artikel ini membahas berbagai inovasi terbaru dalam pengolahan data yang terhubung dalam ekosistem loT, termasuk protokol komunikasi, platform cloud, serta teknik analitik berbasis Al yang mendukung pengelolaan dan pemanfaatan data secara efisien. Selain itu, juga dibahas tantangan yang dihadapi dalam hal keamanan dan skalabilitas sistem IoT. Penelitian ini bertujuan untuk memberikan gambaran mengenai tren teknologi terkini dalam mendukung aplikasi loT dan prospek masa depan dalam pengembangan jaringan data yang lebih efisien dan aman.

Kemajuan teknologi berbasis komputer dan internet telah berdampak pada banyaknya informasi dalam dunia pendidikan dalam memantau pelaksanaan akademik siswa, mengevaluasi pelaksanaan, menetapkan modul pendidikan, dan mengenali gangguan dalam pengajaran. Variabel-variabel yang dianggap penting dalam mengkaji pemanfaatan informasi yang sangat besar dalam pengajaran dapat dikumpulkan menjadi tiga judul utama. Pada awalnya komponen ini adalah pengembangan lingkungan pendidikan dengan peralatan dan program yang sesuai untuk pengumpulan dan kapasitas informasi, penanganan untuk memberikan landasan bagi pemikiran informasi yang sangat besar. Komputer berkapasitas tinggi dengan prosesor yang mumpuni lebih disukai di sisi peralatan, sedangkan inovasi komputasi awan, situasi yang memberdayakan aplikasi data besar atau sistem pencatatan data besar dapat dimanfaatkan di sisi program komputer. Strategi pengumpulan data dalam penelitian ini dapat menjadi pertimbangan penulisan informasi yang luar biasa. Tujuan utama dalam pemeriksaan informasi besar dalam pengajaran adalah untuk menangani dan menguraikan informasi besar dan memanfaatkan hasilnya, terutama dalam proses pengambilan keputusan. Dengan cara ini, pilihan yang diambil akan kembali menjadi penghargaan tersendiri bagi mitra dalam segmen pengajaran. Kesimpulan dari pertimbangan ini adalah akan diperoleh hasil yang berharga baik bagi siswa, pelatih maupun pencipta pilihan dalam bidang pengajaran.

Data mining merupakan salah satu teknik penting dalam analisis data besar yang memungkinkan ekstraksi informasi berharga dari kumpulan data yang sangat besar dan kompleks. Jurnal ini mengulas konsep dasar data mining serta pendekatan interdisipliner yang terlibat dalam penerapannya di berbagai sektor, seperti kesehatan, bisnis, pendidikan, dan energi. Data mining mengintegrasikan berbagai disiplin ilmu seperti statistik, matematika, dan ilmu komputer untuk memproses data, menemukan pola, dan menghasilkan prediksi yang dapat meningkatkan pengambilan keputusan. Dalam penerapannya, data mining menghadapi sejumlah tantangan, termasuk kualitas data, masalah privasi, serta kompleksitas dan skalabilitas algoritma. Prospek masa depan data mining sangat menjanjikan, terutama dengan kemajuan teknologi seperti big data, kecerdasan buatan (AI), dan pembelajaran mesin (machine learning), yang memperluas potensi penggunaannya, termasuk dalam aplikasi IoT. Jurnal ini juga menyoroti studi kasus yang menggambarkan penerapan data mining di berbagai sektor serta tantangan dan solusi yang dihadapi dalam implementasinya. Kesimpulannya, meskipun data mining menawarkan berbagai manfaat, tantangan yang ada memerlukan perhatian serius agar teknologi ini dapat berkembang dan diterapkan dengan efektif di masa depan.