maszyny: Big data

Evidence: 100 • authority: 72

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracit…

Evidence: 60 • authority: 88

Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach. The era of Big Data has provided business organisations opportunities to improve their management processes. This developmental paper is adopting a mixed-method research approach where qualitative data will underpin a quantitative questionnaire. The early insights are based on an initial eleven qualitative interviews and conceptualised in the following three statements: (i) Project practitioners need to increase their data literacy; (ii) Project practitioners are not utilising the available Big Data based on the 3 Vs; Volume, Velocity and Variety; (iii) Project practitioners need to utilise the structured available…

Evidence: 58 • authority: 80

Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety

Evidence: 58 • authority: 80

Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach

Evidence: 58 • authority: 80

Spatial Big Data: Case Studies on Volume, Velocity, and Variety

Evidence: 52 • authority: 92

Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.. Transfusion medicine generates enormous volumes of data across the vein-to-vein continuum, spanning donor characteristics, laboratory testing, component manufacturing, logistics and recipient outcomes. The emergence of big data infrastructures, coupled with artificial intelligence (AI), offers a transformative opportunity to harness this information for safer, more efficient and better personalized transfusion practices. This narrative review outlines current and potential applications of AI and machine learning (ML) at each phase of the big data pipeline in transfus…

Evidence: 52 • authority: 72

Bloodstain pattern analysis (BPA) is a forensic discipline focused on analyzing bloodstains left at known, or suspected crime scenes through visual pattern recognition and physics-based assessments. This is done with the purpose of drawing inferences about the nature, timing and other details of the crime. At its core, BPA revolves around recognizing and categorizing bloodstain patterns, a task essential for reconstructing events in crimes or accidents, verifying statements made during investigations, resolving uncertainties about involvement in a crime, identifying areas with a high likelihood of offender movement for prioritized DNA sampling, and discerning between homicides, suicides, an…

Evidence: 52 • authority: 80

Quantifying volume, velocity, and variety to support (Big) data-intensive application development

Evidence: 43 • authority: 92

InSAR Techniques for Landslide Study: A Review of Methods, Challenges, and Emerging Trends.. Landslides occur frequently under complex and variable global geomorphological and meteorological conditions, posing serious threats to the ecological environment, human life and property. Traditional monitoring approaches are often inefficient and highly susceptible to external conditions, making them inadequate for large-scale rapid deformation monitoring. Owing to its advantages of high precision, wide-area coverage, and all-weather continuous observation, Interferometric Synthetic Aperture Radar (InSAR) technology has been widely applied in landslide monitoring. This paper systematically reviews…

Evidence: 43 • authority: 92

Digital Dermatology.. Digital transformation is fundamentally reshaping dermatology, creating new opportunities in diagnostics, therapy, and healthcare organization. Large datasets combined with Artificial Intelligence (AI) enable more precise classification and prognosis, particularly through the analysis of clinical and dermoscopic imaging. Increasingly, synthetically generated data is used to train new algorithms, though its clinical validity remains under evaluation. Teledermatology has established itself as an integral part of care. Video consultations and asynchronous image transfers enhance accessibility to dermatological expertise and can bridge care gaps, especially in structurally…

Evidence: 43 • authority: 92

A longitudinal multimodal big data infrastructure for precision poultry monitoring.. Livestock systems are increasingly instrumented with heterogeneous sensors, yet the resulting data remain fragmented, short-lived, and rarely documented as integrated infrastructures. This gap limits the development of robust multimodal artificial intelligence under real production conditions. Here we present a longitudinal multimodal data infrastructure for poultry monitoring, spanning 22 consecutive weeks across five commercial-style barns. The dataset combines continuous RGB video (1080 p, 30 fps), continuous audio (48 kHz), periodic radiometric thermal imaging, and twice-daily environmental measurements…

Evidence: — • authority: 80

Big data: Dimensions, evolution, impacts, and challenges