The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027. What is Big data? Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. Big data analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

China Big Data Technology Investment Opportunities Market was valued at USD 45.2 Billion in 2023 and is projected to reach USD 95.6 Billion by 2031, growing at a CAGR of 9.8% from 2024 to 2031.

China Big Data Technology Investment Opportunities Market: Definition/Overview

Big data technology is defined as the complex ecosystem of tools, processes, and methodologies that are utilized to handle extremely large datasets. These technologies are designed to extract valuable insights from structured and unstructured data that is generated at unprecedented volumes. Furthermore, the applications of big data technology are seen across multiple sectors, where data is processed, analyzed, and transformed into actionable intelligence. Advanced analytics, artificial intelligence, and machine learning capabilities are integrated into these systems, through which deeper insights are enabled, and predictive capabilities are enhanced.

Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

This dataset is used in the research entitled "Review on Designing High-Performance K-Means Clustering for Big Data Processing," which investigates big data clustering using various parallel K-means techniques. The dataset includes four sub-datasets, each representing a different scenario. Each scenario demonstrates a distinct distribution of data points within a 2-dimensional feature space, including the ground truth. Furthermore, each scenario contains four data files with varying sizes of data points that follow the same distribution: 100K, 1M, 4M, and 32M data points (where M = million, K = thousand). The figures provided in the scenarios illustrate sample data point distributions.

Using this dataset is permitted when citing the previously mentioned paper after publication.

Big Data and Society Acceptance Rate - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

** percent of German managers from the chemical and pharmaceutical industries consider big data to already have a central meaning for their companies. The figures are based on a survey conducted in Germany in 2018.

ABSTRACT Companies are encouraged by the big data trend to experiment with advanced analytics and many turn to specialist consultancies to help them get started where they lack the necessary competences. We investigate the program of one such consultancy, Advectas - in particular the advanced analytics Jumpstart. Using qualitative techniques including semi structured interviews and content analysis we investigate the nature and value of the Jumpstart concept through five cases in different companies. We provide a definition, a process model and a set of thirteen best practices derived from these experiences, and discuss the distinctive qualities of this approach.

Discover the booming Web-Scale IT market, projected to reach $150 billion in 2025 and grow at a 15% CAGR. Explore key trends, drivers, restraints, and regional insights, including leading companies like Amazon, Google, and Microsoft. This comprehensive analysis covers segments like self-healing software, automation, and SDDC, offering invaluable market intelligence for informed strategic decisions.

Big Data and Society Impact Factor 2024-2025 - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

The Enterprise Data Storage Systems market is experiencing robust growth, driven by the exponential increase in data generated by businesses across various sectors. The market's expansion is fueled by several key factors, including the rising adoption of cloud computing, the increasing demand for big data analytics, and the growing need for robust data security and disaster recovery solutions. The shift towards hybrid cloud strategies, where on-premises and cloud storage solutions are integrated, further contributes to market expansion. Major players like Dell EMC, NetApp, and IBM continue to dominate the market, leveraging their established infrastructure and extensive customer base. However, newer entrants with innovative solutions, such as Nutanix and Pure Storage, are gaining significant traction, particularly in the areas of hyperconverged infrastructure and software-defined storage. Competition is intensifying, driving innovation and price reductions, ultimately benefiting end-users. The market is segmented by storage type (e.g., SAN, NAS, object storage), deployment model (on-premises, cloud), and industry vertical. While the market shows strong overall growth, challenges remain, including managing data complexity, ensuring data governance compliance, and addressing security threats in increasingly distributed environments. Looking ahead, the forecast period (2025-2033) anticipates continued growth, albeit at a potentially moderating CAGR compared to previous years. This moderation reflects a mature market with a consolidated player base. However, emerging technologies such as AI and machine learning are poised to significantly impact the market in the long term, driving demand for advanced analytics capabilities and specialized storage solutions. The adoption of edge computing will also influence market dynamics, creating opportunities for new storage solutions optimized for data processing at the network edge. Regional variations in growth will likely persist, with North America and Europe maintaining substantial market shares, while Asia-Pacific shows strong potential for future expansion. The continued focus on data sovereignty and regulatory compliance will also shape the market landscape in various regions.

This dataset is based on active measurements obeying RFC 2544 and RFC 6815, forming 150 tunneled virtual networks to be compared. The virtual networks were assembled using the VXLAN protocol in an IaaS within DevStack (ALL-IN-ONE) running on a VM in VirtualBox on an Ubuntu 20.4 operating system of a computer with an Intel i7 7500U processor, 2.9GHz clock, and 16GB of RAM. OpenStack-Neutron with virtual switch SDN (OpenvSwitch) was used to interconnect the VMs under analysis. The benchmarking tools iperf, VBoxManage, and lscpu (to capture the temperature of each core) were used for active measurements. In the experiments, two Guest VMs were utilized, with one serving as a traffic generator (TG) and the other as a device under test (DUT), but the DUT runs within a Docker container acting as a server. In the DUT, we varied the number of vCPUs (1,2,4), the amount of vRAM (512MB, 1GB, 2GB, 4GB, and 8GB), and the fifteen Linux TCP congestion control algorithms (BIC, BBR, CDG, CUBIC, DCTCP, ILLINOIS, HYBLA, HTCP, LP, NV, VEGAS, VENO, SCALABLE, WESTWOOD, and YEAH) making up the 150 decision-making units (DMU). The virtual networks (DMU) were ranked using the super-efficiency model of data envelopment analysis (DEA) with variable return to scale and input-oriented. The decision variables listed for ranking were: a) inputs – X1) TCP bandwidth fractal dimension; X2) core0 fractal dimension, X3) core1 fractal dimension, X4) core0 temperature average, and X5) core1 temperature average, and b) outputs– Y1) core 0 hurst, Y2) core 1 Hurst, Y3) TCP bandwidth average, and Y4) TCP bandwidth Hurst. All data per DMU were separated by a folder containing their respective time series. Thus, we conclude that the correct DMU was chosen to provide optimal virtual network services over time. All scripts used to extract decision variables are available in this dataset. Calculating fractal dimensions was madogram and the Hurst parameter was calculated using R/S.

Discover the booming data center equipment market, projected to reach $200 billion by 2025 with a 12% CAGR. Explore key drivers like cloud computing, IoT, and big data, alongside regional market shares and leading companies. This in-depth analysis unveils market trends and growth opportunities for 2025-2033.

Big data, with N × P dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, such as K-means or hierarchical clustering are popular methods for performing exploratory analysis on large datasets. Unfortunately, these methods are not always possible to apply to big data due to memory or time constraints generated by calculations of order P*N(N−1)2. To circumvent this problem, typically the clustering technique is applied to a random sample drawn from the dataset; however, a weakness is that the structure of the dataset, particularly at the edges, is not necessarily maintained. We propose a new solution through the concept of “data nuggets”, which reduces a large dataset into a small collection of nuggets of data, each containing a center, weight, and scale parameter. The data nuggets are then input into algorithms that compute methods such as principal components analysis and clustering in a more computationally efficient manner. We show the consistency of the data nuggets based covariance estimator and apply the methodology of data nuggets to perform exploratory analysis of a flow cytometry dataset containing over one million observations using PCA and K-means clustering for weighted observations. Supplementary materials for this article are available online.

The RAID Controller Chip market is booming, projected to reach $6.8B by 2033 at a CAGR of 12%. Driven by cloud computing, big data, and NVMe adoption, key players like Broadcom and Marvell are shaping this rapidly evolving landscape. Learn more about market trends, segment analysis, and future projections.

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455

ChinaHighCO is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level CO dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R²) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m^-3, and a mean absolute error (MAE) of 0.16 mg m^-3 on a daily basis.

If you use the ChinaHighCO dataset for related scientific research, please cite the corresponding reference (Wei et al., ACP, 2023):

• Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023

More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

The Storage Area Network (SAN) market is experiencing robust growth, projected to reach a market size of $7304.2 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 9.7% from 2019 to 2033. This expansion is driven by several key factors. The increasing adoption of cloud computing and virtualization necessitates efficient data storage and management solutions, fueling demand for SAN technologies. Furthermore, the burgeoning need for high-performance computing across various sectors, including finance, healthcare, and research, is a major catalyst. The rise of big data analytics and the growing volume of unstructured data also contribute significantly to this market's growth trajectory. Competition among established vendors such as Dell, HPE, Cisco, IBM, and NetApp, alongside emerging players like Pure Storage and Infinidat, is fostering innovation and driving down costs, making SAN solutions more accessible to a wider range of businesses. Looking ahead, several trends are shaping the future of the SAN market. Software-defined storage (SDS) solutions are gaining traction, offering greater flexibility and scalability. The integration of artificial intelligence (AI) and machine learning (ML) for automated storage management and predictive analytics is enhancing efficiency and reducing operational costs. Furthermore, the increasing adoption of NVMe (Non-Volatile Memory Express) technology promises faster data transfer speeds, improving overall performance. While the market faces some restraints, such as the complexity associated with SAN implementation and the need for skilled IT professionals, the overall outlook remains positive, indicating substantial growth opportunities for vendors throughout the forecast period. The continued demand for enhanced data management capabilities and the emergence of new technologies will drive this market's expansion well into 2033.

This dataset is adapted from raw data with fully anonymized results on the State Examination of Dutch as a Second Language. This exam is officially administred by the Board of Tests and Examinations (College voor Toetsen en Examens, or CvTE). See cvte.nl/about-cvte. The Board of Tests and Examinations is mandated by the Dutch government.

The article accompanying the dataset:

Schepens, Job, Roeland van Hout, and T. Florian Jaeger. “Big Data Suggest Strong Constraints of Linguistic Similarity on Adult Language Learning.” Cognition 194 (January 1, 2020): 104056. https://doi.org/10.1016/j.cognition.2019.104056.

Every row in the dataset represents the first official testing score of a unique learner. The columns contain the following information as based on questionnaires filled in at the time of the exam:

"L1" - The first language of the learner "C" - The country of birth "L1L2" - The combination of first and best additional language besides Dutch "L2" - The best additional language besides Dutch "AaA" - Age at Arrival in the Netherlands in years (starting date of residence) "LoR" - Length of residence in the Netherlands in years "Edu.day" - Duration of daily education (1 low, 2 middle, 3 high, 4 very high). From 1992 until 2006, learners' education has been measured by means of a side-by-side matrix question in a learner's questionnaire. Learners were asked to mark which type of education they have had (elementary, secondary, or tertiary schooling) by means of filling in for how many years they have been enrolled, in which country, and whether or not they have graduated. Based on this information we were able to estimate how many years learners have had education on a daily basis from six years of age onwards. Since 2006, the question about learners' education has been altered and it is asked directly how many years learners have had formal education on a daily basis from six years of age onwards. Possible answering categories are: 1) 0 thru 5 years; 2) 6 thru 10 years; 3) 11 thru 15 years; 4) 16 years or more. The answers have been merged into the categorical answer. "Sex" - Gender "Family" - Language Family "ISO639.3" - Language ID code according to Ethnologue "Enroll" - Proportion of school-aged youth enrolled in secondary education according to the World Bank. The World Bank reports on education data in a wide number of countries around the world on a regular basis. We took the gross enrollment rate in secondary schooling per country in the year the learner has arrived in the Netherlands as an indicator for a country's educational accessibility at the time learners have left their country of origin. "STEX_speaking_score" - The STEX test score for speaking proficiency. "Dissimilarity_morphological" - Morphological similarity "Dissimilarity_lexical" - Lexical similarity "Dissimilarity_phonological_new_features" - Phonological similarity (in terms of new features) "Dissimilarity_phonological_new_categories" - Phonological similarity (in terms of new sounds)

A few rows of the data:

"L1","C","L1L2","L2","AaA","LoR","Edu.day","Sex","Family","ISO639.3","Enroll","STEX_speaking_score","Dissimilarity_morphological","Dissimilarity_lexical","Dissimilarity_phonological_new_features","Dissimilarity_phonological_new_categories" "English","UnitedStates","EnglishMonolingual","Monolingual",34,0,4,"Female","Indo-European","eng ",94,541,0.0094,0.083191,11,19 "English","UnitedStates","EnglishGerman","German",25,16,3,"Female","Indo-European","eng ",94,603,0.0094,0.083191,11,19 "English","UnitedStates","EnglishFrench","French",32,3,4,"Male","Indo-European","eng ",94,562,0.0094,0.083191,11,19 "English","UnitedStates","EnglishSpanish","Spanish",27,8,4,"Male","Indo-European","eng ",94,537,0.0094,0.083191,11,19 "English","UnitedStates","EnglishMonolingual","Monolingual",47,5,3,"Male","Indo-European","eng ",94,505,0.0094,0.083191,11,19

The Data Center Networks market is booming, projected to reach $26.12B in 2025 with a 17.85% CAGR. Driven by cloud computing, big data, and 5G, this report analyzes market trends, key players (Cisco, Juniper, Arista), and regional growth. Discover insights into Ethernet switches, SANs, and future opportunities. Recent developments include: March 2023 - Arista Networks introduced the Arista WAN Routing System, which integrates three new networking offerings: enterprise-class routing platforms, carrier/cloud-neutral internet transit capabilities, and the CloudVision Pathfinder Service to simplify and enhance customer-wide area networks. Based on Arista's EOS routing capabilities, and CloudVision management, the Arista WAN Routing System bears the architecture, features, and platforms to modernize federated and software-defined wide area networks., March 2023 - Cisco Systems Inc. has announced its expanding its data centre footprint in India (Chennai) by Leveraging industry-leading network performance, the new and upgraded facilities will bring agile, highly resilient, high-capacity access closer to users, including large and small Indian enterprises from across industries. Key drivers for this market are: Increasing Utilization of Cloud Storage is Driving the Market Growth, Rising Need for Backup and Storage is Expanding the Market Demand; The Growth in Retail and E-Commerce is Anticipated to Drive the Market Demand. Potential restraints include: Increasing Utilization of Cloud Storage is Driving the Market Growth, Rising Need for Backup and Storage is Expanding the Market Demand; The Growth in Retail and E-Commerce is Anticipated to Drive the Market Demand. Notable trends are: The Growth in Retail and E-Commerce is Anticipated to Drive the Market Demand.