This chart highlights the percentage of companies using Big Data data in France in 2015, by sector of activity. It can be seen that in the transport sector, a quarter of the companies surveyed reported using big data, also known as "big data." The concept of big data refers to large volumes of data related to use of a good or a service, for example a social network. Being able to process large volumes of data is a significant business issue, as it allows them to better understand how users behave in a service, making them better able to meet user expectations.

This dataset is about book series and is filtered where the books is Resource management concepts for large systems, featuring 10 columns including authors, average publication date, book publishers, book series, and books. The preview is ordered by number of books (descending).

El tamaño del mercado del mercado de análisis de big data de la cadena de suministro se clasifica en función de la aplicación (minorista, atención médica, transporte y logística, fabricación, otros) y producto (suministro local Cadena de análisis de big data, análisis de big data de la cadena de suministro en nube) y regiones geográficas (América del Norte, Europa, Asia-Pacífico, América del Sur y Medio Oriente y África).

Este informe proporciona Las ideas sobre el tamaño del mercado y pronostican el valor del mercado, expresado en USD millones, en estos segmentos definidos.

This statistic illustrates the level of adoption of Big Data by French companies in 2016. According to this study, nearly 30 percent of the companies surveyed were in the concept learning stage.

This study aimed to determine the feasibility and effectiveness of wearable devices in detecting early physiological changes prior to the development of prediabetes [1-3]. The study generated digital biomarkers for remote, mHealth-based prediabetes and hyperglycemia risk to classify which individuals should undergo further clinical testing. The primary inclusion criteria were subjects aged 35-65 years, inclusive, including only post-menopausal females, with a point of care A1C measurement between 5.2-6.4%, inclusive. Blood was collected during the study for measurement of glucose, hemoglobin A1C, lipoproteins, and triglycerides. Participants wore a Dexcom 6 continuous glucose monitor (CGM) and an Empatica E4 wristband for 10 days while receiving a standardized breakfast meal every other day. At the end of the 10 days, the participant returned to the clinic for an oral glucose tolerance test (OGTT). Research data collected includes physiological measurements from wearable devices such as heart rate, accelerometry, and electrodermal conductance.

Abstract Biodiversity research has advanced by testing expectations of ecological and evolutionary hypotheses through the linking of large-scale genetic, distributional, and trait datasets. The rise of molecular systematics over the past 30 years has resulted in a wealth of DNA sequences from around the globe. Yet, advances in molecular systematics also have created taxonomic instability, as new estimates of evolutionary relationships and interpretations of species limits have required widespread scientific name changes. Taxonomic instability, colloquially "splits, lumps, and shuffles," presents logistical challenges to large-scale biodiversity research because (1) the same species or sets of populations may be listed under different names in different data sources, or (2) the same name may apply to different sets of populations representing different taxonomic concepts. Consequently, distributional and trait data are often difficult to link directly to primary DNA sequence data without extensive and time-consuming curation. Here, we present RANT: Reconciliation of Avian NCBI Taxonomy. RANT applies taxonomic reconciliation to standardize avian taxon names in use in NCBI GenBank, a primary source of genetic data, to a widely used and regularly updated avian taxonomy: eBird/Clements. Of 14,341 avian species/subspecies names in GenBank, 11,031 directly matched an eBird/Clements; these link to more than 6 million nucleotide sequences. For the remaining unmatched avian names in GenBank, we used Avibase's system of taxonomic concepts, taxonomic descriptions in Cornell's Birds of the World, and DNA sequence metadata to identify corresponding eBird/Clements names. Reconciled names linked to more than 600,000 nucleotide sequences, ~9% of all avian sequences on GenBank. Nearly 10% of eBird/Clements names had nucleotide sequences listed under 2 or more GenBank names. Our taxonomic reconciliation is a first step towards rigorous and open-source curation of avian GenBank sequences and is available at GitHub, where it can be updated to correspond to future annual eBird/Clements taxonomic updates.

Ce graphique met en évidence le pourcentage d'entreprises utilisant des données des Big Data en France en 2015, par secteur d'activité. On peut observer que dans le domaine des transports, un quart des entreprises interrogées ont déclaré utiliser les Big Data, également appelées « mégadonnées ». Le concept de Big Data désigne des volumes importants de données liés à l'utilisation d'un bien ou d'un service, par exemple un réseau social. Le fait de pouvoir traiter des volumes importants de données constitue un enjeux important pour les entreprises, car cela leur permet de mieux comprendre comment se comportent les utilisateurs d'un service, ce qui les rend mieux à même de répondre aux attentes des utilisateurs.

NASA has the aim of researching aviation Real-time System-wide Safety Assurance (RSSA) with a focus on the development of prognostic decision support tools as one of its new aeronautics research pillars. The vision of RSSA is to accelerate the discovery of previously unknown safety threats in real time and enable rapid mitigation of safety risks through analysis of massive amounts of aviation data. Our innovation supports this vision by designing a hybrid architecture combining traditional database technology and real-time streaming analytics in a Big Data environment. The innovation includes three major components: a Batch Processing framework, Traditional Databases and Streaming Analytics. It addresses at least three major needs within the aviation safety community. First, the innovation supports the creation of future data-driven safety prognostic decision support tools that must pull data from heterogeneous data sources and seamlessly combine them to be effective for NAS stakeholders. Second, our innovation opens up the possibility to provide real-time NAS performance analytics desired by key aviation stakeholders. Third, our proposed architecture provides a mechanism for safety risk accuracy evaluations. To accomplish this innovation, we have three technical objectives and related work plan efforts. The first objective is the determination of the system and functional requirements. We identify the system and functional requirements from aviation safety stakeholders for a set of use cases by investigating how they would use the system and what data processing functions they need to support their decisions. The second objective is to create a Big Data technology-driven architecture. Here we explore and identify the best technologies for the components in the system including Big Data processing and architectural techniques adapted for aviation data applications. Finally, our third objective is the development and demonstration of a proof-of-concept.

Technological breakthroughs such as high-throughput methods, genomics, single-cell studies, and machine learning have fundamentally transformed research and ushered in the big data era of biology. Nevertheless, current data collections, analyses, and modeling frequently overlook relative specificity, a crucial property of molecular interactions in biochemical systems. Relative specificity describes how, for example, an enzyme reacts with its many substrates at different rates, and how this discriminatory action alone is sufficient to modulate the substrates and downstream events. As a corollary, it is not only important to comprehensively identify an enzyme’s substrates, but also critical to quantitatively determine how the enzyme interacts with the substrates and to evaluate how it shapes subsequent biological outcomes. Genomics and high-throughput techniques have greatly facilitated the studies of relative specificity in the 21st century, and its functional significance has been demonstrated in complex biochemical systems including transcription, translation, protein kinases, RNA-binding proteins, and animal microRNAs (miRNAs), although it remains ignored in most work. Here we analyze recent findings in big data and relative specificity studies and explain how the incorporation of relative specificity concept might enhance our mechanistic understanding of gene functions, biological phenomena, and human diseases.

Ce graphique présente les types de sources utilisées par les entreprises utilisatrices de Big Data en France en 2015, selon le secteur. D'après la source, 92 % des entreprises du domaine des transports utilisaient des données de géolocalisation. Dans le domaine de l'hébergement et de la restauration, les trois quarts des entreprises sondées ont déclaré utiliser des données provenant des médias sociaux. Le concept de Big Data désigne des volumes importants de données liés à l'utilisation d'un bien ou d'un service, par exemple un réseau social ou un objet connecté tel qu'un GPS. Le fait de pouvoir traiter des volumes importants de données constitue un enjeux important pour les entreprises, car cela leur permet de mieux comprendre comment les utilisateurs d'un service se comportent, ce qui les rend mieux à même de répondre aux attentes des utilisateurs.

Digital Battlefield Market size was valued at USD 43.4 Billion in 2024 and is projected to reach USD 144.67 Billion by 2031, growing at a CAGR of 16.24% during the forecast period 2024-2031.

Global Digital Battlefield Market Drivers

The emergence of paradigms for modern warfare: Information-centric operations, connectivity, and digitization are becoming hallmarks of modern combat. Because of this, armed forces all around the world are spending money on digital battlefield technologies to improve their skills in things like situational awareness, command and control, and precision targeting.

Information Technology Advancements: The creation of complex digital battlefield systems is being fueled by the quick growth of information technology, which includes big data analytics, cloud computing, cybersecurity, artificial intelligence, and machine learning. These technologies improve decision-making and operational performance by allowing armed personnel to gather, process, analyze, and disseminate large amounts of data in real time.

Growing Security Challenges and Threats: The need for digital battlefield solutions is being fueled by the changing nature of security threats, such as terrorism, cyber warfare, asymmetric warfare, and regional conflicts. In order to keep one step ahead of their opponents, safeguard important resources, and retain strategic supremacy in combat, military organizations look to technology.

Integration of Network-Centric Warfare: In order to attain information dominance and operational agility, network-centric warfare concepts highlight the significance of networked systems, sensors, and platforms. Digital battlefield solutions provide smooth communication and coordination between different domains and echelons of the military by facilitating the integration and interoperability of disparate military equipment.

Big ideas in brief is a book. It was written by Ian Crofton and published by Quercus in 2011.

Modeler's Manifesto: Self-Situating Appendix

13 semi-structured interviews were conducted with librarians, archivists and digital content managers who work in in public institutions and commercial companies in Europe, North America and Australasia. Interviews were conducted between February and December 2018 in accordance with ethics procedures at Loughborough University (29 January 2018) and University College London (21st May 2018). After interviews were completed a transcript was written up and interviewees were invited to comment on or submit corrections to the transcripts. We then analysed the interviews according to an inductive, thematic approach. The transcribed interviews were destroyed in line with ethics requirements.Newspapers were the first big data for a mass audience. Their dramatic expansion over the nineteenth century created a global culture of abundant, rapidly circulating information. The significance of the newspaper, however, has largely been defined in metropolitan and national terms in scholarship of the period. The collecting and digitization by local institutions further situated newspapers within a national context. "Oceanic Exchanges: Tracing Global Information Networks in Historical Newspaper Repositories, 1840-1914" (OcEx) brings together leading efforts in computational periodicals research from the US, Mexico, Germany, the Netherlands, Finland and the UK to examine patterns of information flow across national and linguistic boundaries in nineteenth century newspapers by linking digitized newspaper corpora currently siloed in national collections. OcEx seeks to break through the conceptual, institutional, and political barriers which have limited working with big humanities data by bringing together historical newspaper experts from different countries and disciplines around common questions; by actively crossing the national boundaries that have previously separated digitized newspaper corpora through computational analysis; and by illustrating the global connectedness of nineteenth-century newspapers in ways hidden by typical national organizations of digital cultural heritage. We propose to coordinate the efforts of this six-nation team to: build classifiers for textual and visual similarity of related newspaper passages; create a networked ontology of different genres, forms, and textual elements that emerged during the nineteenth century; model and visualise textual migration and viral culture; model and visualise conceptual migration and translation of texts across regional, national, and linguistic boundaries; analyze the sensitivity and generality of results; release public collections. For scholars of nineteenth-century periodicals and intellectual history, OcEx uncovers the ways that the international was refracted through the local as news, advice, vignettes, popular science, poetry, fiction, and more. By revealing the global networks through which texts and concepts traveled, OcEx creates an abundance of new evidence about how readers around the world perceived each other through the newspaper. These insights may reshape the assumptions that underpin research by scholars in comparative literature, translation studies, transnational and intellectual history, and beyond. Computational linguistics provides building blocks (recognizing translation, paraphrasing, text reuse) that can enable scholarly investigations, with both historical and contemporary implications. At the same time, such methods raise fundamental questions regarding the validity and reliability of their results (such as the effects of OCR-related noise, or imperfect comparability of corpora). Finally, by linking research across large-scale digital newspaper collections, OcEx will offer a model for national libraries and other data custodians that host large-scale data for digital scholarship. The project will test the accessibility and interoperability of emerging and well established newspaper digitisation efforts and output clear recommendations for structuring such development in future.

Correction of out-of-service rate based on the previous day’s mobile terminal location data.

This video explains the concept behind Geoscience Australia's Data Cube, a new way of organising, analysing and managing the large amounts of data collected from Earth Observation Satellites (EOS) …Show full descriptionThis video explains the concept behind Geoscience Australia's Data Cube, a new way of organising, analysing and managing the large amounts of data collected from Earth Observation Satellites (EOS) studies over time. The Data Cube facilitates efficient data analysis and enables users to interrogate Australia's EOS data from the past and present. It is hoped that the Data Cube will become a useful tool used by remote sensing scientists and data analysts to extract information to support for informing future decision-making and policy development within Australia.

The dataset was compiled to examine the use of ChatGPT 3.5 in educational settings, particularly for creating and personalizing concept maps.
The data has been organized into three folders: Maps, Texts, and Questionnaires. The Maps folder contains the graphical representation of the concept maps and the PlanUML code for drawing them in Italian and English. The Texts folder contains the source text used as input for the map's creation The Questionnaires folder includes the students' responses to the three administered questionnaires.

Data Analysis Software Market size was valued at USD 79.15 Billion in 2024 and is projected to reach USD 176.57 Billion by 2031, growing at a CAGR of 10.55% during the forecast period 2024-2031.

Global Data Analysis Software Market Drivers

The market drivers for the Data Analysis Software Market can be influenced by various factors. These may include:

Technological Developments: The need for more advanced data analysis software is being driven by the quick development of data analytics technologies, such as machine learning, artificial intelligence, and big data analytics.
Growing Data Volume: To extract useful insights from massive datasets, powerful data analysis software is required due to the exponential expansion of data generated from multiple sources, including social media, IoT devices, and sensors.
Business Intelligence Requirements: To obtain a competitive edge, organisations in all sectors are depending more and more on data-driven decision-making processes. This encourages the use of data analysis software to find strategic insights by analysing and visualising large, complicated datasets.
Regulatory Compliance: In order to maintain compliance and safeguard sensitive data, firms must invest in data analysis software with strong security capabilities. Examples of these rules and compliance requirements are the CCPA and GDPR.
Growing Need for Real-time Analytics: Companies are under increasing pressure to make decisions quickly, which has led to a growing need for real-time analytics capabilities provided by sophisticated data analysis tools. These skills allow organisations to react quickly to market changes and gain insights.
Cloud Adoption: As a result of the transition to cloud computing infrastructure, businesses of all sizes are adopting cloud-based data analysis software since it gives them access to scalable and affordable data analysis solutions.
The emergence of predictive analytics is being driven by the need for data analysis tools with sophisticated predictive modelling and forecasting skills. Predictive analytics is being used to forecast future trends, customer behaviour, and market dynamics.
Sector-specific Solutions: Businesses looking for specialised analytics solutions to handle industry-specific opportunities and challenges are adopting more vertical-specific data analysis software, which is designed to match the particular needs of sectors like healthcare, finance, retail, and manufacturing.

Ce graphique illustre le niveau d'adoption du Big Data* par les entreprises françaises en 2016. D 'après cette étude, près de 30 % des entreprises interrogées en étaient au stade d'apprentissage du concept.

Censo de Población: Population by year of arrival in Spain, year of arrival in the province, sex, age (big groups) and nationality (Spanish/foreign). Annual. Provinces.