Search

Business Intelligence is the key technologies that ensures effective decision making through extracting relevant information and providing adapted systems as the Data Warehouses. To access decisional information, the decision maker should express his requirements in Natural Language interfaces without any technical skills, avoiding the IT-Designer intervention. Often, the decision maker's requirements are expressed as WH-questions (What, Who, Where, etc.) or Keyword-like questions. In this paper, we emphasize on a Why-Question asked in Business Intelligence context. This question has not been well dealt in the literature in terms of produced answers. Indeed, to respond this type of question, it is necessary to provide explanations. These explanations are determined by identifying causal relationships between the phenomenon highlighted in the Why-Question and factors that can influence this phenomenon. In this context, we propose an approach on which a system can address a causality problem related to answering a decisional Why-Question. To validate our approach a tool called BI Why Q/A is developed. In order evaluate our proposal in terms of efficiency and relevance, a set of experimental studies is carried out and presented. [ABSTRACT FROM AUTHOR]

Os índices elevados de evasão escolar representam um problema que tem atraído a atenção de muitos gestores e educadores ao redor do mundo. Esse problema acontece quando, por algum motivo, o aluno abandona o curso no qual está matriculado antes de sua conclusão. A evasão escolar gera uma série de prejuízos aos diversos atores envolvidos no processo educacional e pode ocorrer em diversos níveis e modalidades de ensino, por diversas causas. Uma forma de se combater a evasão escolar – e, consequentemente, os problemas causados por ela – consiste em identificar, de forma antecipada, alunos ou perfis de alunos com alto risco de evasão. A identificação prévia desses perfis pode auxiliar os gestores de políticas educacionais na tomada de decisão, como a elaboração de planos e a execução de ações que tentem evitar que os alunos abandonem os seus cursos. Este trabalho propõe a confecção de um banco de dados dimensional utilizado em um data warehouse, para análise das características dos alunos, relacionadas à evasão escolar. Os dados são oriundos da Plataforma Nilo Peçanha (PNP), adquiridos através de processos de extração, transformação e carga dos dados. Processo de Descoberta de Conhecimento em Bancos de Dados foi utilizado para analisar quais características estão relacionadas à evasão escolar de alunos de cursos de nível técnico e de graduação da rede federal de ensino de todo o Brasil, comparando os resultados com os dados do Instituto Federal de Educação, Ciência e Tecnologia da Paraíba (IFPB). As características relacionadas a faixa etária, renda per capita familiar e etnia apresentaram diferenças significativas quando comparados os alunos que abandonaram o curso e aqueles que o concluíram.

New challenges in education demand effective solutions. Although Learning Analytics (LA), Educational Data Mining (EDM) and the use of Big Data are often presented as a panacea, there is a lot of ground to be covered in order for the EDM to answer the real questions of educators. An important step toward this goal is to implement holistic solutions that allow educational stakeholders to engage in the core of the EDM processes. The effectiveness of such an attempt relies on (a) having access to data arranged in an organized and meaningful way and (b) setting a sequence of processes that are flexible and reusable. Therefore, a data pipeline that imports data from a specially developed data warehouse is designed and created. Additionally, it is tested in real-life data, and results are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social & Technology / Jurnal Sosial dan Teknologi (SOSTECH) is the property of Green Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This systematic review focused on understanding the impact of big data on marketing productivity, following the guidelines of systematic literature reviews and using the PICOC (problem/population, intervention, comparison, results, context) method. 50 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science and business, and published between 2020 and 2023. These articles, selected for their relevance and contribution to the study objectives, showed that the big data offers notable benefits in the marketing industry. The ability to customize marketing strategies to individual customer needs, improved optimization, and a better understanding of customer behaviors and preferences were key aspects. These findings highlight how big data can boost productivity in marketing, strengthening customer relationships and increasing loyalty by improving understanding and adaptation to the specific demands and preferences of each customer. This deeper, more personalized approach to consumers represents a significant shift in the effectiveness and efficiency of marketing strategies in the current era. [ABSTRACT FROM AUTHOR]

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions. [ABSTRACT FROM AUTHOR]

Optimal, efficient, and accurate drug stock management at hospital depots is crucial for ensuring the smooth operation of medical and operational services. Therefore, the use of machine learning is currently essential for managing drug stocks at hospital depots more optimally. This optimization process involves stages such as data collection, data pre-processing, attribute selection, data labeling, classification algorithm selection, model training, model evaluation, and result interpretation. The data used in this research includes information on drug stocks at hospital depots with details on drug items, quantities, prices, depot origins, demand trends, and types of transactions. The aim of using these algorithms is to classify drug stock items into categories such as "sufficient," "deficient," and "excess" based on historical data patterns and relevant attributes. Model evaluation is carried out by comparing classification results with actual data and measuring evaluation metrics such as accuracy, precision, recall, and F1-score. It is hoped that the classification results will indicate the need for optimization in the previously implemented algorithms and provide new solutions for managing drug stocks at hospital depots. The Shuffle Frog Leaping algorithm (SFLA) implemented will help drug stock management staff identify demand patterns more optimally, efficiently, and accurately. Thus, this research has the potential to make significant contributions to optimizing drug stock management and decision-making at hospital depots, which will also positively impact the progress of hospital services. [ABSTRACT FROM AUTHOR]

Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large‐scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A‐priori, FP‐growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP‐growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data‐driven approaches to sustainable agriculture. [ABSTRACT FROM AUTHOR]

Background: The widespread adoption of the electronic health record (EHR) has resulted in vast repositories of EHR big data that are being used to identify patterns and correlations that translate into data-informed health care decision making. Problem: Health care professionals need the skills necessary to navigate a digitized, data-rich health care environment as big data plays an increasingly integral role in health care. Approach: Faculty incorporated the concept of big data in an asynchronous online course allowing an interprofessional mix of students to analyze EHR big data on over a million patients. Outcomes: Students conducted a descriptive analysis of cohorts of patients with selected diagnoses and presented their findings. Conclusions: Students collaborated with an interprofessional team to analyze EHR big data on selected variables. The teams used data visualization tools to describe an assigned diagnosis patient population. [ABSTRACT FROM AUTHOR]

Decision-makers, whether at the corporate or enterprise level, do not have the same vision of all decision-making data since their needs vary greatly depending on the application domain, requirements, perception capacity, knowledge level and other factors. Thus, a powerful and robust model to help decision-makers make the right decisions at the right time remains a challenge to achieve. In this perspective, a multidimensional model, intended for a group of decision-makers, consisting of a set of facts analyzed according to different dimensions and including all the indicators and axes of analysis for a specific domain is a solution that needs to be developed and implemented. In this article, we propose a new multi-function approach that adapts to several different situations in terms of decision-makers' preferences. Indeed, when the capacity of the machine used and its visualization constraints are sufficiently important, the latter can choose to have global results, results of their queries. Otherwise, decision makers can choose to weight their preferences, in terms of query attributes, and have in return a set of results that are most relevant to their queries. In the case where the means used (smartphones, tablets, etc.) by decision makers have a limited capacity to be able to consult the results of OLAP queries, we propose to express these preferences in the form of visualization constraints, which will be applied to the schema to represent the expected results incrementally. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake architectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures that have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, identifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this rapidly evolving field. We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further enhance the comparative analysis of the different architectural components. We also explore the differences between data warehouses and data lakes. Our findings provide valuable insights for researchers and practitioners seeking to understand the current state of data-lake technology and its potential future trajectory. [ABSTRACT FROM AUTHOR]

Objectives Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. Materials and Methods We engaged a convenience sample of informatics leaders from CTSA hubs (n  = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. Results EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. Discussion As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. Conclusion This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere. [ABSTRACT FROM AUTHOR]

Hive can store massive data through extended clusters, far exceeding the expansion and storage capabilities of traditional databases, and has become a major tool for building data warehouses in the era of big data. Based on Hive technology, this paper studies the enterprise-level data warehouse architecture composed of data storage layer, Hive data warehouse layer and application layer. Then, the paper studies data warehouse tools, uses HDFS for underlying storage and MapReduce for computing engine, and compares Hive data warehouse with relational database. Then, the ETL process is studied. The data goes through three stages: extraction, conversion and loading, and finally the data flows from the source to the target end. Finally, the system function and test are studied, the subsystems including data processing, data management, data migration and data analysis are constructed, and the core functions are tested. The test results were consistent with the expected results and met the delivery standards. [ABSTRACT FROM AUTHOR]

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines. [ABSTRACT FROM AUTHOR]

Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm. The time series and voltage interaction functions are connected with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. When mining association rules in electrical data sets, the pruning strategy is used to effectively reduce the search space, thus improving the efficiency of the algorithm. The pruning strategy applied in this study is confidence pruning, and the rules that do not meet the conditions are deleted by calculating confidence. For large-scale data sets, the distributed computing framework can dispatch tasks to multiple computing nodes, thus accelerating the computing speed. In the voltage interaction experiment, the storage and processing cost of electrical data is very high, so the research on using compression technology to reduce the dimension of storage space will reduce the computational complexity of the algorithm. The study conducts experiments on the Netloss dataset and three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results showed that the fusion algorithm proposed in the study had precise control ability in power grid datasets, and effectively mined association rules on large-scale datasets. This paper proposes a novel approach for efficient association rule mining in large-scale power grid datasets. A fusion algorithm is developed combining clustering, neural networks, and association rule learning. The technique incorporates data warehousing, time series modeling, and voltage interaction analysis to enhance information extraction from electrical data. Experiments on the Netloss dataset demonstrate the algorithm's effectiveness for power flow control and measurement accuracy compared to standard methods. Results show significant improvements in active power loss and network loss metrics as well. This research provides an important foundation for scalable analytics in smart power systems. [ABSTRACT FROM AUTHOR]

Copyright of Jordanian Journal of Computers & Information Technology is the property of Jordanian Journal of Computers & Information Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Proses pengambilan keputusan yang cepat dan tepat merupakan kebutuhan utama dalam lingkungan bisnis yang dinamis. Namun, banyak perusahaan freight forwarder menghadapi permasalahan dalam efisiensi pengambilan keputusan karena ketidaksempurnaan proses pengolahan data di dalam data warehouse. Untuk mengatasi permasalahan tersebut. Penelitian ini bertujuan untuk mengoptimalkan proses Data Warehouse (DWH) guna meningkatkan produktivitas dalam pengambilan keputusan di perusahaan freight forwarder di Indonesia. Dalam konteks lingkungan bisnis yang semakin kompleks dan dinamis, kebutuhan akan informasi yang tepat waktu dan akurat sangat penting untuk mendukung pengambilan keputusan yang efektif. DWH telah menjadi solusi populer untuk mengintegrasikan dan menganalisis data dari berbagai sumber guna mendukung proses pengambilan keputusan. Business Process Optimization (BPO) diterapkan untuk menganalisis dan merancang ulang proses bisnis yang terkait dengan pengolahan data, sehingga meningkatkan efisiensi dan akurasi pengolahan data dalam DWH. Dalam penelitian ini, digunakan model optimasi yang dirancang untuk memaksimalkan efisiensi proses ETL dan meningkatkan kinerja sistem Online Analytical Processing (OLAP) serta Online Transaction Processing (OLTP). Metode-metode ini diharapkan dapat meningkatkan kualitas dan kecepatan pengambilan keputusan, serta efisiensi operasional perusahaan freight forwarder. Hasil dari penelitian ini diharapkan dapat memberikan produktivitas dan daya saing bagi perusahaan di industri logistik.

Nowadays, modernizing the data warehouse ecosystem is a key challenge in decision support systems. This modernization is crucial for ensuring scalability and meeting evolving business requirements, especially with the advent of big data. A promising solution involves implementing data warehouses with contemporary data stores, such as NoSQL. In this context, we introduce in this paper a framework that leverages Model-Driven Architecture (MDA) to design and implement modern data warehouses across NoSQL data stores. Our MDA approach aims to offer a collaborative, dynamic, and reusable process for developing NoSQL-oriented data warehouses tailored to specific project requirements. It facilitates the automatic and dynamic generation of a hybrid data warehouse model from its conceptual model, which encompasses structural, domain, and access parameters. Moreover, our framework includes the generation of implementation code for the data warehouse, along with a set of files to validate, document, and illustrate the data warehouse schema on a target platform. Finally, we present a detailed case study to highlight the effectiveness of our MDA framework. [JJCIT 2024; 10(2.000): 214-230]

Electronic health records (EHRs) and linked biobanks have tremendous potential to advance biomedical research and ultimately improve the health of future generations. Repurposing EHR data for research is not without challenges, however. In this paper, we describe the processes and considerations necessary to successfully access and utilize a data warehouse for research. Although imperfect, data warehouses are a powerful tool for harnessing a large amount of data to phenotype disease. They will have increasing relevance and applications in clinical research with growing sophistication in processes for EHR data abstraction, biobank integration, and cross-institutional linkage.

In this paper, we present an intelligent methodology for assisting decision-makers in both understanding the structure of a data warehouse model and making decisions. The support module proposed by our method comprises three operations: (i) transforming a data warehouse model into an ontology, allowing for the display of the different terminology related to a specific domain as well as the different semantic relationships between them, (ii) recommending a series of queries to the decision-maker that enables an understanding of the reasoning logic based on the ontology’s structure, (iii) enriching the different results obtained from some analysis tools through the use of advanced machine learning techniques. The originality of our proposed methodology lies in its ability to influence a decision-maker’s thinking in order to encourage him to take full advantage of the services provided by the data warehouse model. We apply our proposed methodology to an extended data warehouse model that enables the analysis of social media data related to people with personality disorders (PD). The primary goal of this model is to provide decision-makers with suitable services that allow them to make meaningful decisions for people with personality disorders around the world. This task was carried out by analyzing the activities and content of people on social media. In addition, one of the main advantages of this model is the use of various artificial intelligence (AI) and natural language processing (NLP) techniques. Our proposed methodology is implemented and the results achieved are evaluated using both quantitative and qualitative methods. [ABSTRACT FROM AUTHOR]

Massive volumes of structured and semi-structured data originating from various sources can be efficiently stored, managed, and analysed using a data warehouse. It is specifically designed for query and analysis purposes, rather than for transaction processing, offering a strong foundation for making data-driven decisions. Organizations and businesses are drawn to cloud platforms for numerous reasons, including faster development and deployment, superior performance, low latency, and cost efficiency. The study delves into the cloud data warehouse services provided by three prominent cloud service providers: GCP, Microsoft Azure, and Amazon Web Services (AWS). Three of the most prominent cloud providers--AWS, Azure, and GCP--offer cloud data warehouse services: Amazon Redshift, Azure Synapse, and Google BigQuery. Selecting the right cloud data warehouse is a complex task due to the myriads of options available from these major providers. By carefully evaluating these factors, organizations can decide which cloud data warehouse service best meets their needs, balancing performance, latency, integration, security, and cost considerations to optimize their cloud strategy. [ABSTRACT FROM AUTHOR]

Over the past two decades, the way computing resources are been developed, deployed, upgraded, and applied changed dramatically, with more and more software and hardware solutions being transferred to cloud technologies. Data Warehouses (DW), defined as a way of organizing corporate data in an integrated manner over (sequential) time periods, "structured & disposed" in order to generate a "single data source", were also affected by the evolution, thus giving rise to the concept of Cloud Data Warehouse (CDW). This technology allows users to be more technologically free, as they do not need to spend time investing in software and hardware, they only pay for the resources they used and the infrastructure itself has greater flexibility and scalability. However, selecting the most suitable platform or technology for a CDW can be a complex task due to the large number of factors that can influence the decision and due to the existing offer in the market. The objective of this paper is to describe the process of benchmarking a set of CDW platforms, with the goal of analyzing and exposing each one's performance results. These platforms are Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse. The metrics to be measured are data loading and query running time, and alias running times. For this benchmark, the dataset used was Star Schema Benchmark (SSB), a dataset based on the well-known TPC Benchmark™ H (TPC-H). [ABSTRACT FROM AUTHOR]

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)—a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration. [ABSTRACT FROM AUTHOR]

The utilization of blockchain technology in the fishing industry has been extensively studied and implemented to address issues such as illegal fishing and carbon emissions control. However, integrating blockchain with the vast amounts of data in the fishing supply chain poses significant challenges. Challenges include managing extensive data such as photos or videos for product traceability throughout their lifecycle, compounded by the growing complexity of cross-border trade and market expansion. Additionally, blockchain's storage capacity limitations present hurdles in fully accommodating and comprehensively storing detailed supply data from a complex and expanding supply chain.While solutions like the Interplanetary File System (IPFS) have been explored for large data storage on the blockchain, this paper proposes a directly integrated blockchain solution tailored for the challenges of fishing with big data. We introduce a novel big data design that preserves blockchain's anonymity and immutability features, addressing storage limitations while maintaining the architecture's purpose. Furthermore, our proposal integrates product supply chain traceability with carbon footprint tracking, enabling comprehensive assessment based on quality, sustainability, and carbon footprint criteria.Despite the proposed solution needing to be tested in real-life situations, we conducted rigorous testing through simulation, white-box evaluation, and complexity analysis. The results demonstrate the potential of our solution to address challenges faced in fisheries supply chains, providing valuable insights for future practical implementation and validation efforts.

Knowing that nowadays information is one of the strongest weapons a company can have, it has a relevant role in the decision-making part of the company. Building the right structures and using the right tools for these structures is essential for a certain company or business to be a leader in the field where it operates. Building IT infrastructure in business is a must for businesses today, one of the most innovative areas today is undoubtedly Business Intelligence. BI has restructured the company's decision-making processes, becoming the main pillar on which decision-making is based. This paper sheds light on the main concepts related to BI, also it focuses on the main techniques of Business Intelligence, BI techniques, and analysis of the current situation of use and involvement of BI in Albanian companies.

Coffee, the second-largest global soft commodity, can take advantage of a comprehensive mining of daily and historical market data for more effective informed trading decisions. Advanced ICT and data mining technologies can change the trading market operation. The existing systems are confronted with certain constraints, including incomplete data, insufficient documentation for storage, and a requirement for a scalable infrastructure for big data analytics, such as a data warehouse or data lakehouse. To address this issue, the paper presents a design and implementation of a coffee commodity trading big data warehouse capable of analyzing various essential parameters for supporting informed decision-making. First, the designed system can automatically collect coffee trading data for New York Arabica coffee futures prices from selected worldwide reports and financial data portals. Next, the Extract, transform, and load (ETL) process is adopted to ingest coffee futures trading crawled data into the 3 layers data warehouse. Finally, the analytical system will extract and visualize selected key dimensions that influence coffee futures prices within different observation windows and perspectives. As a result, we implement a prototype of a coffee trading data warehouse on the crawled data from January 2000 to October 2022 and visualize trends in coffee futures prices based on the collected data for informed decision-making. The construction system is capable of stably operating and processing large volumes of transaction data. This paper will be valuable documentation for reference and decision support for coffee commodity trading enterprises and contribute to the development of future forecasting algorithms.

Achieving privacy-preserving analytical query with fine-grained access control for cloud-based data warehouse (CDW) through the use of online analytical processing (OLAP) tool is a real challenge. This is because the access control must be enforced differently to multiple users while the OLAP query should be excelled from the encrypted DW and the query results are delivered through the public network. Existing solutions employ encryption solutions to apply on DW. However, they mostly overlooked fine-grained access control enforcement to different users and efficient OLAP query performance when there is a large number of users. In this paper, we proposed a PPAC-CDW scheme, a fine-grained and privacy-preserving access control with efficient query processing for OLAP queries for CDW. Our proposed scheme is based on the integration of ciphertext-policy attribute-based encryption, an extended model of materialized view scheme of MOLAP, and the hybrid cloud system. Our proposed scheme enjoys fast query performance based on encrypted pre-computed cube and our proposed B+Tree model. In addition, we introduced an efficient and traceable user revocation mechanism based on proxy re-encryption and blockchain with optimized cost of ciphertext retrieval. Finally, we conducted experiments demonstrating that our scheme offers more efficient data access performance compared to related works.

Abstract In big data, analysis data is collected from different sources in various formats, transforming into the aspect of cleansing the data, customization, and loading it into a Data Warehouse. Extracting data in other formats and transforming it to the required format requires transformation algorithms. This transformation stage has redundancy issues and is stored across any location in the data warehouse, which increases computation costs. The main issues in big data ETL are handling high-dimensional data and maintaining similar data for effective data warehouse usage. Therefore, Extract, Transform, Load (ETL) plays a vital role in extracting meaningful information from the data warehouse and trying to retain the users. This paper proposes hybrid optimization of Swarm Intelligence with a tabu search algorithm for handling big data in a cloud-based architecture-based ETL process. This proposed work overcomes many issues related to complex data storage and retrieval in the data warehouse. Swarm Intelligence algorithms can overcome problems like high dimensional data, dynamical change of huge data and cost optimization in the transformation stage. In this work for the swarm intelligence algorithm, a Grey-Wolf Optimizer (GWO) is implemented to reduce the high dimensionality of data. Tabu Search (TS) is used for clustering the relevant data as a group. Clustering means the segregation of relevant data accurately from the data warehouse. The cluster size in the ETL process can be optimized by the proposed work of (GWO-TS). Therefore, the huge data in the warehouse can be processed within an expected latency.

Designing a database is a crucial step in providing businesses with high-quality data for decision making. The quality of a data model is the key to the quality of its data. Evaluating the quality of a data model is a complex and time-consuming task. Having suitable metrics for evaluating the quality of a data model is an essential requirement for automating the design process of a data model. While there are metrics available for evaluating data warehouse data models to some degree, there is a distinct lack of metrics specifically designed to assess how well a data model conforms to the rules and best practices of Data Vault 2.0. The quality of a Data Vault 2.0 data model is considered suboptimal if it fails to adhere to these principles. In this paper, we introduce new metrics that can be used for evaluating the quality of a Data Vault 2.0 data model, either manually or automatically. This methodology involves defining a set of metrics based on the best practices of Data Vault 2.0, evaluating five representative data models using both metrics and manual assessments made by a human expert. Finally, a comparative analysis of both evaluations was conducted to validate the consistency of the metrics with the judgments made by a human expert. [ABSTRACT FROM AUTHOR]

In big data, analysis data is collected from different sources in various formats, transforming into the aspect of cleansing the data, customization, and loading it into a Data Warehouse. Extracting data in other formats and transforming it to the required format requires transformation algorithms. This transformation stage has redundancy issues and is stored across any location in the data warehouse, which increases computation costs. The main issues in big data ETL are handling high-dimensional data and maintaining similar data for effective data warehouse usage. Therefore, Extract, Transform, Load (ETL) plays a vital role in extracting meaningful information from the data warehouse and trying to retain the users. This paper proposes hybrid optimization of Swarm Intelligence with a tabu search algorithm for handling big data in a cloud-based architecture-based ETL process. This proposed work overcomes many issues related to complex data storage and retrieval in the data warehouse. Swarm Intelligence algorithms can overcome problems like high dimensional data, dynamical change of huge data and cost optimization in the transformation stage. In this work for the swarm intelligence algorithm, a Grey-Wolf Optimizer (GWO) is implemented to reduce the high dimensionality of data. Tabu Search (TS) is used for clustering the relevant data as a group. Clustering means the segregation of relevant data accurately from the data warehouse. The cluster size in the ETL process can be optimized by the proposed work of (GWO-TS). Therefore, the huge data in the warehouse can be processed within an expected latency. [ABSTRACT FROM AUTHOR]

Knowing that nowadays information is one of the strongest weapons a company can have, it has a relevant role in the decision-making part of the company. Building the right structures and using the right tools for these structures is essential for a certain company or business to be a leader in the field where it operates. Building IT infrastructure in business is a must for businesses today, one of the most innovative areas today is undoubtedly Business Intelligence. BI has restructured the company's decision-making processes, becoming the main pillar on which decision-making is based. This paper sheds light on the main concepts related to BI, also it focuses on the main techniques of Business Intelligence, BI techniques, and analysis of the current situation of use and involvement of BI in Albanian companies. [ABSTRACT FROM AUTHOR]

Healthcare facilities use huge quantities of real-time and analytical data to discover meaningful information from patient clinical lab results. Advanced analytics and machine learning algorithms help doctors identify and treat patients more accurately. Accurate models must be trained, tested, and validated with enough data. New real-time data allows healthcare practitioners to quickly and accurately analyse patient demands. Healthcare organizations can improve patient care and outcomes through knowledge discovery. The goal of this effort is to develop a real-time data repository based on patient clinical exams. This collection feeds real-time monitoring panels and machine or deep learning algorithms that forecast patient progression from clinical lab results. Integrate HL7 messages from diverse sources, preprocess them, and add them to an API-accessible data warehouse. In conclusion, the proposed method creates an international-standard data warehouse. This data warehouse can increase healthcare decision-making accuracy and efficacy when utilised with machine learning models, improving patient care and outcomes through more personalised treatment options. [ABSTRACT FROM AUTHOR]