Data Infrastructure Research Articles

Enhancing Big Data Processing Efficiency in AI-Based Healthcare Systems: A Comparative Analysis of Random Forest and Deep

This research focuses on optimizing the speed of Big Data processing using Artificial Intelligence (AI) in healthcare applications. The study integrates Random Forest (RF) and Deep Learning (DL) algorithms with cloud-based computing systems to improve data processing efficiency. The dataset includes both structured data, such as Electronic Health Records (EHR), and unstructured data, like medical images. The results show that RF performs better with structured data, achieving a lower Mean Squared Error (MSE) and higher R-squared (R²) than traditional methods. Meanwhile, DL achieves superior accuracy and Area Under the Curve (AUC) in processing unstructured data. By utilizing the distributed computing power of Spark on a cloud platform, the processing speed was significantly enhanced, as demonstrated by a statistically significant reduction in processing time (p < 0.05) observed through a t-test analysis comparing Spark-based computing with traditional methods. Despite these improvements, challenges such as data privacy and infrastructure costs remain. Despite these improvements, challenges such as data privacy and infrastructure costs remain. This research provides a robust framework for real-time healthcare data analysis, highlighting its potential to improve decision-making processes and patient outcomes in medical services.

Modular approach to near-time data management for multi-city atmospheric environmental observation campaigns

Abstract. Urban observation networks are becoming denser, more diverse, and more mobile, while being required to provide results in near time. The Synergy Grant “urbisphere” funded by the European Research Council (ERC) has multiple simultaneous field campaigns in cities of different sizes, collecting data to improve weather and climate models and services, including assessing the impact of cities on the atmosphere (e.g., heat, moisture, pollutant, and aerosol emissions) and people's exposure to extremes (e.g., heat waves, heavy precipitation, air pollution episodes). Here, a solution to this challenge for facilitating diverse data streams from multiple sources, scales (e.g., indoors, regional-scale atmospheric boundary layer), and cities is presented. For model development and evaluation in heterogeneous urban environments, we need meshed networks of in situ observations with ground-based and airborne (remote) sensing platforms. In this contribution we describe challenges, approaches, and solutions for data management, data infrastructure, and data governance to handle the variety of data streams from primarily novel modular observation networks deployed in multiple cities, in combination with existing data collected by partners, ranging in scale from indoor sensor deployments to regional-scale boundary layer observations. A metadata system documents (1) sensors and instruments, (2) the location and configuration of deployed components, and (3) maintenance and events. This metadata system provides the backbone for converting instrument records to calibrated, location-aware, convention-aligned, and quality-assured data products, according to FAIR (findable, accessible, interoperable, and reusable) principles. The data management infrastructure provides services (via, e.g., Application Programming Interface – APIs, apps, integrated computing interfaces – ICEs) for data inspection and subsequent calculations by campaign participants. Some near-real-time distributions are made to international networks (e.g., AERONET, PhenoCam) or local agencies (e.g., GovDATA) with appropriate attribution. The data documentation conventions, used to ensure structured datasets, in this case are used to improve the delivery of integrated urban services, such as to research and operational agencies, across many cities.

Challenges in Implementing Comprehensive Precision Medicine Screening for Ovarian Cancer

Precision medicine has revolutionised targeted cancer treatments; however, its implementation in ovarian cancer remains challenging. Diverse tumour biology and extensive heterogeneity in ovarian cancer can limit the translatability of genetic profiling and contribute to a lack of biomarkers of treatment response. This review addresses the barriers in precision medicine for ovarian cancer, including obtaining adequate and representative tissue samples for analysis, developing functional and standardised screening methods, and navigating data infrastructure and management. Ethical concerns related to patient consent, data privacy and health equity are also explored. We highlight the socio-economic complexities for precision medicine and propose strategies to overcome these challenges with an emphasis on accessibility and education amongst patients and health professionals and the development of regulatory frameworks to support clinical integration. Interdisciplinary collaboration is essential to drive progress in precision medicine to improve disease management and ovarian cancer patient outcomes.

Enhancing Big Data Processing Efficiency in AI-Based Healthcare Systems: A Comparative Analysis of Random Forest and Deep

This research focuses on optimizing the speed of Big Data processing using Artificial Intelligence (AI) in healthcare applications. The study integrates Random Forest (RF) and Deep Learning (DL) algorithms with cloud-based computing systems to improve data processing efficiency. The dataset includes both structured data, such as Electronic Health Records (EHR), and unstructured data, like medical images. The results show that RF performs better with structured data, achieving a lower Mean Squared Error (MSE) and higher R-squared (R²) compared to traditional methods. Meanwhile, DL achieves superior accuracy and Area Under the Curve (AUC) in processing unstructured data. By utilizing the distributed computing power of Spark on a cloud platform, the processing speed was significantly enhanced, as demonstrated by a statistically significant reduction in processing time (p < 0.05) observed through a t-test analysis comparing Spark-based computing with traditional methods. Despite these improvements, challenges such as data privacy and infrastructure costs remain. Despite these improvements, challenges such as data privacy and infrastructure costs remain. This research provides a robust framework for real-time healthcare data analysis, highlighting its potential to improve decision-making processes and patient outcomes in medical services.

Architecting Enterprise-Scale Data Products: A Framework for Advanced Data Science and AI/ML Operations

This article presents a comprehensive framework for building enterprise-scale data products that power modern Customer & Product Analytics, Data Science, artificial intelligence, and machine learning initiatives. The article examines the foundational architecture patterns, pipeline engineering strategies, and advanced distributed computing approaches in both on-prem and cloud. These are essential for developing robust data infrastructure capable of handling complex Data Analytics, Data Science, and AI/ML workflows. The article explores critical aspects of feature engineering at scale, real-time processing capabilities, and the implementation of feature stores, while addressing the challenges of data quality, governance, legal, and security in regulated environments. The article introduces a systematic approach to integrating data products with MLOps pipelines, emphasizing the importance of automated workflows, monitoring systems, and feedback loops in production environments. The findings demonstrate that successful implementation of scalable data products requires a careful balance of architectural decisions, technology selection, and operational practices. The article contributes to the field by providing actionable insights and architectural patterns that organizations can adopt to build resilient, scalable, and efficient data products for their Data Analytics, Data Science, and AI/ML use cases. This article establishes a foundational framework that bridges the gap between theoretical data architecture principles and practical implementation challenges in enterprise settings.

The Tango to Modern Collaboration and Patient-Centric Value Generation in Health Care – a real-world guide from practitioners for practitioners

Background Value-Based Health Care (VBHC) represents a pivotal shift from volume-based to outcome-driven quality metrics centered on patient-valued outcomes. This approach requires collaboration across all participants in the health care value chain; providers, payers, pharma, policymakers and patients (collectively known as the 5Ps). Despite substantial theoretical endorsement of VBHC’s potential for improving health outcomes and system efficiency, empirical evidence detailing its practical implementation remains limited. This field study evaluates the real-word implementation of VBHC within a health care organization. Methods In 2022, a health care collaboration Think Tank initiated this investigation during a breakout session, gathering insights from 12 leading international organizations to construct an empirical VBHC transformation reference guide. Real-world data was collected through structured interviews over a 1-year period, covering the 5 P value chain in various healthcare settings. The VBHC initiatives were analyzed through four stages: initiation, data acquisition, collaborative frameworks, and results evaluation. Results The 12 interviews identified five key enablers for successful VBHC implementation: 1. Organizational Purpose: defining core motivators for change; 2. People: identifying pivotal roles and leadership to endorse change; 3. Resources: securing personnel and financial support; 4. Data Infrastructure: developing interoperable IT systems for effective data sharing and collection; 5. Execution: prioritizing sustained implementation processes. Conclusion The findings highlight that VBHC implementation and adoption is complex and requires incremental advancements, dedicated leadership, and resilient strategic framework spanning over multiple years. A comprehensive understanding of patient populations, risk stratification, and appropriate outcome metrics are essential to measure and deliver the VBHC transformation. Executive endorsement and transition funding during the transformation process are paramount to support this systemic shift. Collaboration among all 5 P stakeholders is essential for success. This field study underscores the importance of continuous learning and adaptation, providing a practical guide to enhance health care quality and efficiency that serves all stakeholders.

POTENSI CULTURAL LANDSCAPE UNTUK PENGEMBANGAN ATRAKSI WISATA DI DESA BONGKASA PERTIWI, BALI

This research aims to identify the potential of the cultural landscape in Bongkasa Pertiwi Village, Badung Regency, Bali, and how these elements can be developed into sustainable tourist attractions. Bongkasa Pertiwi Village has various rich cultural landscape elements, such as rice fields with subak irrigation systems, temples, and springs (beji), which have not been optimally utilized as tourist attractions. Observations and data analysis were carried out by dividing the village area into three village area segments to evaluate the development potential and existing challenges. The results of the study show that: Segments 1 and 2 are dominated by rice field cultural landscapes, and there are springs (beji) and temples Segment 2, while Segment 3 is dominated by tourist attractions and residential areas where there is no cultural landscape whose potential has not been utilized The obstacles faced develop cultural landscapes as tourist attractions are limited infrastructure and lack of measurable data on cultural landscape elements in Bongkasa Pertiwi Village.

Data and service management of the EPOS Volcano Observations Thematic Core Service by the European volcanological community

The Volcano Observations Thematic Core Service (VOLC-TCS) is one of ten core services forming the European Research Infrastructure Consortium for the European Plate Observing System (EPOS ERIC). The main objective of the VOLC-TCS is the implementation of a technical, financial, and legal framework compliant with EPOS ERIC to (i) strengthen the European volcanology community represented by Volcano Observatories (VOs) and Volcanological Research Institutions (VRIs), and (ii) provide virtual access to the community’s data, data products software and services (DDSSs) from volcanoes in Europe and European overseas territories. The efforts of the VOLC-TCS community began prior to the appointment of EPOS as an ERIC and have followed a long-term work plan that started in 2002. One of the main challenges for the management of volcanological data has consisted in their great heterogeneity regarding technical characteristics and also legal aspects (e.g. different data policies among the data providers, different purposes for the use of data, ranging from science to monitoring, early-warning and crisis response, and communication and outreach). Another challenge has derived from the consistency of the VOLC-TCS’ products with the overall service provision of EPOS, which merges services from different Earth Science communities (seismology, GNSS, geomagnetic, geochemistry, geology, etc.). Some of the services used in volcanology are in common with the other communities, thus the implementation work was also devoted to harmonising the data and products with standards defined by other TCSs. Yet another important task has been the implementation of the community Gateway to expose and enable access to services not fully compliant with EPOS, or to services implemented by institutions outside the EPOS perimeter, as well as to create a platform that can act as an interface between the VOLC-TCS and data infrastructures operating at a global level (e.g. WOVOdat). Based on the experience gained and the results achieved, we report here on the state of the art of the VOLC-TCS implementation since 2018 and propose future actions to address some of the main technical challenges and measures to ensure mid-to-long term sustainability of the services.

Assessing the association between multiple parental alcohol events and the risk of being maltreated children in a New Zealand population-based birth cohort.

Child maltreatment is an important component of alcohol's harm to others. No previous studies have assessed the association between multiple parental alcohol events and child maltreatment. This study aimed to measure the impact of parental alcohol-attributable events on the incidence rate of child maltreatment events over the span of childhood and adolescence in New Zealand. Longitudinal analysis using the birth cohort data of all live births (n = 58 359) in New Zealand in 2000, with the cohort followed until age 17 years using linked data from the Statistics New Zealand Integrated Data Infrastructure (IDI) from 1995 to 2017. Zero-inflated negative binomial and count regression models were used to measure the association between the number of child maltreatment events [collected from publicly funded hospital discharges, Programme for the Integration of Mental Health Data (PRIMHD), Oranga Tamariki, Children's Action Plan (CAP) and police data sets] and parental exposure to alcohol-attributable hospitalization or service use for mental health/addiction. Potential confounders were included for parents and children. Of 58 359 children in the cohort, 86% (50 319) had not experienced any documented maltreatment events. Among those at risk of being maltreated children, the maltreatment incidence rate ratio increased by 35.2% (95% confidence interval = 18.1-55.6%) if a child was exposed to parental alcohol-attributable hospitalization or service use for mental health addiction events. For each additional parental alcohol-attributable hospitalization or service use for mental health or addiction, the risk of being maltreated children increased by 12.8%. In New Zealand, hazardous alcohol consumption by parents appears to be associated with child maltreatment alongside other contributing factors, in particular parental lower educational attainment.

Reprocessing of eXpendable BathyThermograph (XBT) profiles from the Ligurian and Tyrrhenian seas over the time period 1999–2019 with a full metadata upgrade

Abstract. The advent of open science and the United Nations Decade of Ocean Science for Sustainable Development are revolutionizing the ocean-data-sharing landscape for an efficient and transparent ocean information and knowledge generation. This blue revolution raised awareness on the importance of metadata and community standards to activate interoperability of the digital assets (data and services) and guarantee that data-driven science preserves provenance, lineage and quality information for its replicability. Historical data are frequently not compliant with these criteria, lacking metadata information that was not retained, crucial at the time of data generation and further ingestion into marine data infrastructures. The present data review is an example attempt to fill this gap through a thorough data reprocessing starting from the original raw data and operational log sheets. The data gathered using XBT (eXpendable BathyThermograph) probes during several monitoring activities in the Tyrrhenian and Ligurian seas between 1999 and 2019 have first been formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never applied, uncertainty specification and bias correction from Cheng et al. (2014). Secondly, a new automatic quality control (QC) procedure has been developed and a new interpolation scheme applied. The reprocessed (REP) dataset has been compared to the data version, presently available from the SeaDataNet (SDN) data access portal, processed according to the pioneering work of Manzella et al. (2003) conducted in the framework of the European Union Mediterranean Forecasting System Pilot Project (Pinardi et al., 2003). The comparison between REP and SDN datasets has the objective to highlight the main differences derived from the new data processing process. The maximum discrepancy among the REP and SDN data versions always resides within the surface layer (REP profiles are warmer than SDN ones) until 150 m depth generally when the thermocline settles (from June to November). The overall bias and root mean square difference are equal to 0.002 and 0.041 °C, respectively. Such differences are mainly due to the new interpolation technique (Barker and McDougall, 2020) and the application of the calibration correction in the REP dataset. The REP dataset (Reseghetti et al., 2024; https://doi.org/10.13127/rep_xbt_1999_2019.2) is available and accessible through the INGV (Istituto Nazionale di Geofisica e Vulcanologia, Bologna) ERDDAP (Environmental Research Division's Data Access Program) server, which allows for machine-to-machine data access in compliance with the FAIR (findable, accessible, interoperable and reusable) principles (Wilkinson et al., 2016).

"The Truth Is, We Must Miss Some": A Qualitative Study of the Patient Eligibility Screening Process, and Automation Perspectives, for Cancer Clinical Trials.

Recruitment of cancer patients into clinical trials (CTs) is a challenge. We aimed to explore how patient eligibility assessment is conducted in practice, what factors support or hinder this process, and to assess the potential usefulness of Clinical Trial Recruitment Support Systems (CTRSS) for patient-to-trial matching. We conducted semi-structured interviews in France with healthcare professionals involved in cancer CTs and experts on trial recruitment. We focused on the stages in-between trial feasibility, and patient information and consent. Interviews were recorded, and the transcripts were analyzed thematically. We used the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 framework to organize our results. We interviewed 25 participants. We identified common steps for cancer patient eligibility assessment: prescreening under medical supervision, followed by the validation of patient-trial matching based on manual chart review. This process built on rich interactions between clinicians, other professionals (clinical research assistants, data scientists, medical coding experts), and patients. Technological factors, mainly related to data infrastructure (both for patient data and trial data), and organizational factors (research culture, incentives, formal and informal research networks) mediated the performance of the recruitment process. Participants had mixed feelings towards CTRSSs; they welcomed automated pre-screening but insisted on manual verification. Given the necessary collaborative nature of multisite trials, coordinated efforts to support a common data infrastructure could be helpful. Material, organizational, and human factors affect cancer patient eligibility assessment for CTs. Patient-to-trial matching tools bear potential, but good understanding of the ecosystem, including stakeholders' motivations, is a prerequisite.

Preface

The Geomatics International Conference (GeoICON) is an annual event organized by the Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember (ITS), Indonesia, since 2016. In 2024, the conference focused on the theme “Geospatial Technology for Mapping the Future: Insights and Solutions for Sustainable Land Development.” This theme highlights the important role that geospatial technology plays in solving challenges related to sustainable land development across different sectors. Geospatial technology helps gather, analyze, and visualize spatial data, which is key for decision-making in areas such as land use, environmental conservation, urban planning, disaster management, and resource management. The goal of this conference is to bring together experts from different fields to collaborate and share knowledge, promoting innovation and contributing to sustainable growth. The 9th Geomatics International Conference (GeoICON) 2024 covered a variety of important topics related to geospatial technology and its applications. These include Geodesy and Geodynamics, Geology and Geophysics, Spatial Data Infrastructure, Geography and Urban Environment, Agriculture and Forestry, Mining and Energy, Cadastral Mapping, and Marine Environment. This year, GeoICON united experts from universities, industries, and government organizations to exchange ideas that inspire innovation and interdisciplinary collaboration. After several years of virtual conferences, GeoICON 2024 returned to an in-person format, offering a chance for face-to-face interaction, strengthening professional networks, and encouraging productive academic discussions. The conference were held on July 24th, 2024, at Hotel Santika Premiere Gubeng, Surabaya, East Java, Indonesia. For GeoICON 2024, we received 109 abstract submissions, and after careful review, we selected 83 papers for publication. We are excited to welcome participants from well-known institutions like Institut Teknologi Sepuluh Nopember (ITS), Universitas Gadjah Mada (UGM), Badan Riset dan Inovasi Nasional (BRIN), Universitas Diponegoro (UNDIP), PT Waskita Karya Tbk., Institut Teknologi Bandung (ITB), Sekolah Tinggi Pertanahan Nasional (STPN), Universitas Lampung (Unila), Institut Pertanian Bogor (IPB), Politeknik Negeri Banjarmasin, Hanoi National University of Education (Vietnam), Badan Informasi Geospasial (BIG), Pemerintah Provinsi DKI Jakarta Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu, National Cheng Kung University (Taiwan), Universiti Teknologi MARA (Malaysia), and Space Research and Technology Institute (Bulgaria). We are deeply grateful to the organizing committee for their hard work in making GeoICON 2024 possible. We would also like to thank all participants for their valuable contributions and the reviewers for their thoughtful feedback, which helped improve the quality of the papers. A special thank you to our sponsors for their generous support: Direktorat Riset dan Pengabdian kepada Masyarakat (DRPM) ITS, PT. Terra Drone Indonesia, PT. Tellus Meridian Survey, PT. Geosolution Pratama Nusantara, Berin Computer, and PT. GeoMosaic Indonesia. Without their support, this event would not have been possible. We look forward to engaging discussions and meaningful contributions that will advance the field of geospatial research and technology. October 31st, 2024 Dr. Muhammad Aldila Syariz, S.T., M.S., Ph.D. The 9th GeoICON 2024 Chairman List of Keynote Speakers and The 9th GeoICON Committee are available in this Pdf.

With great (statistical) power comes great responsibility: A comment on the ethics of using administrative data to investigate marginalised populations

As health data infrastructure improves, we have the opportunity to link increasing volumes of data in order to investigate important health problems. This is perhaps most pertinent when looking at the experiences and outcomes of our most disadvantaged groups, who are often invisible in data obtained through primary research. Whilst these data offer enormous opportunity, there are also ethical implications in their use, which are less frequently discussed than in relation to their qualitative counterparts. As a diverse group of clinicians and academics working across public health, we share our experience and understanding of how we can improve our reflexivity in health data science and ensure that research in this area is ethically conducted in co-production with the people whose data we are using. We discuss the potential opportunities, challenges and impacts of using administrative data to investigate marginalised populations.

Exploring the Preventive Contribution of Data Localisation Against Information Technology Outages: The CrowdStrike Case

The severe impacts of the CrowdStrike outage on the world economy and national ‎critical services, e.g., banking, healthcare, and airports, invoked the urgent need to ‎decrease governmental dependence on foreign tech companies to store and manage ‎national data. An effective alternative to this approach is data localisation. The latter ‎includes the physical placement of data infrastructure within the state’s territory or ‎imposing ultimate national control over data stored in a server located abroad, ‎along with relevant managing software. The research points out the prominence of ‎data localisation to protect national data, and the functionality of services ‎depending on this data, against technical failures that cause outages. In this ‎endeavour, the research reviews the concepts of territoriality and localisation of ‎cyberspace, along with shedding light on the CrowdStrike outage ‎and explaining ‎its reasons and consequences. Then, it introduces a justification for the validity of ‎data localisation for preventing outages' negative impacts and protecting national ‎data. ‎© The Author(s) 2024. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.

The role of artificial intelligence and machine learning in optimizing U.S. healthcare supply chain management

This paper aims to review the role of Artificial Intelligence and Machine Learning in managing the healthcare supply chain in the United States. Healthcare supply chains face several challenges such as fragmentation, lack of real-time visibility, and inventory management issues. However, there are solutions with the help of AI and machine learning, including the availability of predictive analytics to improve demand forecasting, optimization algorithms for inventory and logistics, and automated quality control. Application areas demand forecasting, supplier selection, logistics optimization, quality control, and real-time tracking. The applications of AI in healthcare supply chains have the potential to improve the healthcare supply chain in terms of reduced costs, increased efficiency, optimized decision-making, and better patient outcomes. However, the implementation experience has shown several challenges such as data quality, privacy concerns, regulatory compliance, and workforce adaptation within organizations. Successful implementations in various health organizations in the US give valuable insights into how AI could be well implemented. The future presents several opportunities for supply chain optimization with the rise of blockchain and Internet of Things (IoT) integration. For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support. As AI becomes increasingly critical for competitive advantage, it has the potential to create more resilient, efficient, and patient-centric supply chains in the US healthcare system.

Predictive analytics in emergency healthcare systems: A conceptual framework for reducing response times and improving patient care

This review paper explores the role of predictive analytics in enhancing emergency healthcare systems, emphasizing the potential benefits of reducing response times and improving patient care. Emergency healthcare systems often grapple with inefficiencies that can adversely affect patient outcomes, especially during critical situations. This paper presents a conceptual framework that leverages predictive analytics to address these challenges by integrating diverse data sources, utilizing real-time analysis, and providing decision-support tools. The findings suggest that predictive analytics can significantly enhance operational efficiency by optimizing resource allocation, streamlining patient prioritization, and enabling timely interventions. Additionally, practical recommendations are proposed for healthcare institutions to successfully implement predictive analytics, including investing in data infrastructure, fostering a culture of analytics, and collaborating with technology partners. This framework paves the way for improved emergency response and contributes to a data-driven healthcare environment that enhances overall patient outcomes.

Role of AI in reducing global Plastics use: Predictive analytics for global sustainability

The growth, from a global perspective, in plastic waste sees its necessary promise in Artificial Intelligence. The driving force of Machine Learning, Computer Vision, and Predictive Analytics, shapes each style of waste management and effective collection routes to recycling. In this paper, therefore, the discussion will be on how AI can ensure a reduction in plastic waste, focusing on developing countries such as Nigeria. The theoretical underpinning of this research is on AI adoption, the Technology Acceptance Model, a few real-world case studies in AI for waste reduction, and many challenges that need to be focused on due to issues of data sparsity, infrastructure limitations, and ethics. Using these challenges for unlocking the full potential of AI in the direction of a more sustainable future, with minimal plastic waste at the forefront of environmental well-being, could be better negotiated. As the global community faces the pressing need to tackle plastic pollution, especially in areas with inadequate waste management systems and severe environmental challenges AI technologies present groundbreaking solutions to improve waste management methods, eliminate environmental harm, and foster sustainable growth. A crucial factor influencing the future of AI-driven plastic waste reduction in developing countries is the ongoing progress and implementation of AI technologies.

Cloud data centers and networks: Applications and optimization techniques

As the volume and complexity of big data continue to escalate, optimizing the performance, scalability, and energy efficiency of big data applications within cloud data centers has become increasingly crucial. This journal presents a comprehensive survey of current optimization techniques, focusing on data placement, job scheduling, and network configurations tailored for cloud environments. We explore the impact of various data center topologies on the performance of big data frameworks like Hadoop, emphasizing the trade-offs between performance and energy efficiency. Advanced methodologies, including dynamic data placement strategies, locality-aware scheduling, and innovative reduce task placement techniques, are reviewed in depth. Additionally, we highlight the importance of network power effectiveness (NPE) and examine the role of optical and electronic switching technologies in enhancing data center efficiency. By synthesizing findings from recent studies, this paper provides valuable insights into the optimization of cloud data centers, offering recommendations for improving resource utilization and reducing job completion times while maintaining energy efficiency. The findings contribute to the ongoing efforts to scale and adapt cloud data infrastructures for the rapidly growing demands of big data applications.

Artificial Intelligence in Business Scenario Analysis: A Framework for Enhanced Decision-Making Through What-If Simulations

This article examines the transformative impact of artificial intelligence on scenario analysis and what-if simulations in business analytics, addressing a critical gap in current literature regarding the integration of AI-driven predictive modeling with traditional business planning methodologies. Through a systematic analysis of implementation cases across financial services and e-commerce sectors, the article demonstrates how AI-enhanced simulation models significantly improve the speed, accuracy, and adaptability of scenario planning processes. The findings indicate substantial improvements in analysis time and prediction accuracy compared to traditional methods, particularly in areas of pricing optimization, risk assessment, and stress testing. The article synthesizes data from multiple enterprise implementations to develop a comprehensive framework for AI integration in scenario analysis, addressing key challenges in data quality, model reliability, and real-time processing capabilities. Results suggest that organizations leveraging AI-driven scenario analysis demonstrate enhanced capability in anticipating market fluctuations, optimizing resource allocation, and responding to environmental changes, though implementation success is heavily dependent on data infrastructure maturity and organizational readiness. This article contributes to both theoretical understanding and practical application of AI in business analytics, providing actionable insights for practitioners while identifying critical areas for future research in the evolving landscape of intelligent business simulation technologies.

Conservation data infrastructures: From carbon accounting to multiple biodiversity and social measures

AbstractEnvironmental big data and analytical models are increasingly informing conservation efforts to address global climate and biodiversity crises. Yet, the growing reliance on data‐driven approaches raises concerns regarding biases, uncertainties, and injustices in environmental decision‐making processes.This article presents ‘conservation data infrastructures’ as socio‐technical processes of conceiving, producing, and distributing conservation data that affect multifaceted decisions and practices.Drawing on major carbon conservation programs in Australia and Brazil, we assess how data‐driven investment planning and project assessments set what is valued, how it is measured, and whose interests are accounted for.Both case studies reveal how technological innovations expand carbon accounting methods by integrating ecological and social data with advanced analytical models to encompass a wide range of place‐based impacts. However, data‐driven solutions alone may not lead to transformative changes that fully address existing disparities in environmental priorities and benefit distribution across scales.We conclude that the proposed notion of data infrastructures not only reveals socio‐technical limitations but also elevates multiple perspectives and local realities to reimagine and rework conservation measures.Read the freePlain Language Summaryfor this article on the Journal blog.