Big Data and Society CiteScore 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 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.

The Hadoop Big Data Analytics Market is segmented Solution (Data Discovery and Visualization (DDV), Advanced Analytics (AA)) End-User Industry (BFSI, Retail, IT and Telecom, Healthcare and Life Sciences, Manufacturing, Media and Entertainment), and Geography (North America (United States, Canada), Europe (United Kingdom, Germany), Asia Pacific (China, Japan), Latin America, Middle East, and Africa).The market sizes and forecasts are provided in terms of value (USD billion) for all the above segments.

The Big Data Analytics in the Manufacturing Industry Report is Segmented by End-User Industry (Semiconductor, Aerospace, Automotive, And Other End-User Industries), Application (Condition Monitoring, Quality Management, Inventory Management, And Other Applications), And Geography (North America, Europe, Asia-pacific, And Latin America). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

In terms of value, the global storage in big data market is expected to expand at a CAGR of 20.4% over the forecast period (2016 – 2026) and is expected to be valued at US$ 61.44 Bn by 2026 end.

As of March 2024, there were a reported 5,381 data centers in the United States, the most of any country worldwide. A further 521 were located in Germany, while 514 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These centers can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

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

Indonesia Big Data Analytics Software Market Analysis The Indonesia Big Data Analytics Software market is poised to witness substantial growth over the forecast period of 2025-2033, with a CAGR of 9.35%. In 2025, the market stood at a value of USD 43.15 million and is projected to reach a remarkable value by 2033. This growth is primarily driven by the increasing adoption of digital technologies, the proliferation of data-intensive applications, and the growing need for businesses to make data-driven decisions. Key trends shaping the market include the rising popularity of cloud-based big data analytics solutions, the emergence of advanced analytics techniques such as machine learning and artificial intelligence, and the growing awareness of data privacy and security concerns. Despite these positive factors, the market faces challenges such as the lack of skilled professionals in data analytics, the high cost of implementation, and the complexities associated with managing and integrating large volumes of data. Prominent players in the market include Teradata, SAS, SAP, Tableau Software, and IBM Corporation, among others. Market Size and Growth The Indonesia Big Data Analytics Software Market is projected to grow from USD 235.6 million in 2023 to USD 1,159.1 million by 2029, exhibiting a CAGR of 24.3% during the forecast period. This growth can be attributed to the increasing adoption of big data analytics solutions by organizations to enhance their decision-making, improve operational efficiency, and gain a competitive advantage. Recent developments include: June 2024: Indosat Ooredoo Hutchison (Indosat) and Google Cloud expanded their long-term alliance to accelerate Indosat’s transformation from telco to AI Native TechCo. The collaboration will combine Indosat’s vast network, operational, and customer datasets with Google Cloud’s unified AI stack to deliver exceptional experiences to over 100 million Indosat customers and generative AI (GenAI) solutions for businesses across Indonesia. These include geospatial analytics and predictive modeling, real-time conversation analysis, and back-office transformation. Indosat’s early adoption of an AI-ready data analytics platform exemplifies its forward-thinking approach., June 2024: Palo Alto Networks launched a new cloud facility in Indonesia, catering to the rising demand for local data residency compliance. The move empowers organizations in Indonesia with access to Palo Alto Networks' Cortex XDR advanced AI and analytics platform that offers a comprehensive security solution by unifying endpoint, network, and cloud data. With this new infrastructure, Indonesian customers can ensure data residency by housing their logs and analytics within the country.. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Notable trends are: Small and Medium Enterprises to Hold Major Market Share.

The need for advanced analytical approaches to provide HPDA solutions is driving the market growth of High Performance Data Analytics (HPDA). According to the analyst from Verified Market Research, The High Performance Data Analytics (HPDA) Market is estimated to reach a valuation of USD 597.06 Billion over the forecast period 2031, by subjugating around USD 113.23 Billion in 2023.

The adoption of an open-source framework for big data analytics is driving market growth. This surge in demand enables the market to grow at a CAGR of 23.1% from 2024 to 2031.

High Performance Data Analytics (HPDA) Market: Definition/ Overview

HPDA refers to big data analytics that uses High-Performance Computing (HPC) techniques. Big data analytics has always relied on high-performance computing (HPC), but as data grows exponentially, new forms of high-performance computing will be required to access previously unimaginable volumes of data. The combination of big data analytics and high-performance computing is called “high-performance data analytics.” High-performance data analytics is the process of quickly finding insights from large data sets by running powerful analytical tools in parallel on high-performance computing systems.

Furthermore, high-performance data analytics infrastructure is a rapidly expanding market for government and commercial organizations that need to combine high-performance computing with data-intensive analysis. For complex modeling and simulations, big data analytics techniques like Hadoop and Spark have long required high-performance computing, which they lack.

The Latin America Big Data Analytics Market Report is Segmented by Organization Size (Small and Medium Scale, and Large-Scale Organizations), End-User Vertical (IT & Telecom, BFSI, Retail & Consumer Goods, Manufacturing, Healthcare & Life Sciences, Government, and Other End-User Verticals), and Country. The Report Offers the Market Size in Value Terms in (USD) for all the Abovementioned Segments.

This statistic depicts the revenue generated by the big data services market in the Asia Pacific (excluding Japan) from 2012 to 2014, as well as a forecast of revenue from 2015 to 2017. In 2014, revenues associated with the big data services market in the Asia Pacific amounted to 290 million U.S. dollars. 'Big data' refers to data sets that are too large or too complex for traditional data processing applications. Additionally, the term is often used to refer to the technologies that enable predictive analytics or other methods of extracting value from data.

The global Big Data Security market was valued at USD 11.79 billion in 2025 and is projected to grow at a CAGR of 14.81% from 2025 to 2033. Increasing adoption of cloud computing and proliferation of big data across various industry verticals are key factors driving the growth of the market. Growing concerns regarding data breaches and stringent government regulations to protect sensitive data are also fueling the demand for big data security solutions. Key trends shaping the market include the emergence of artificial intelligence (AI) and machine learning (ML) in big data security, increasing adoption of cloud-based security solutions, and growing focus on data privacy and compliance. The market is highly competitive, with established players such as Symantec Corporation, Fortinet, Check Point Software Technologies Ltd., IBM, and Hewlett Packard Enterprise (HPE) dominating the landscape. These companies are investing heavily in research and development to enhance their product offerings and strengthen their market position. Regional markets such as North America and Europe are expected to remain dominant throughout the forecast period due to the presence of well-established IT infrastructure and stringent data protection regulations. Asia Pacific is also expected to witness significant growth as businesses in the region increasingly adopt big data technologies and prioritize data security. Recent developments include: March 2024, On behalf of its clients, Telefónica Tech UK&I is pleased to announce the introduction of the cutting-edge cyber security services brand known as "NextDefense." This brand will assist customers in achieving a safe digital future. The term "NextDefense" refers to the next generation of Managed Security Services (MSS), which Telefónica Tech provides from its global network of Security Operations Centers (SOCs). This new generation of MSS incorporates advanced capabilities that are in line with the shifting threat landscape, emerging technologies, and the requirement for proactive security., The 'NextDefense' solution, which Telefónica Tech now provides in the United Kingdom and Ireland, is equipped with proprietary threat information, cutting-edge technology, and automation-driven standardized processes. This is made possible by Telefónica Tech's significant size and worldwide cyber experience. Telefónica Tech maintains a worldwide network of service operations centers (SOCs) that spans the United Kingdom, Europe, and the Americas. These SOCs are responsible for supporting the 6,300 specialists and more than 4,000 certifications that it has in third-party technology. This consists of a Security Operations Center (SOC) located in Belfast, which offers crucial on-shore capabilities to Telefónica Tech UK&I by means of a facility that has been approved for security and is supported by worldwide resources., In order to anticipate and guard against new attacks, 'NextDefense' makes use of modern data sources, Big Data, and Artificial Intelligence (Machine Learning) methods. As a result, it is an essential component in the current cyber security scene. Through the implementation of this new service, Telefónica Tech UK&I is able to transform security operations by utilizing data and artificial intelligence, as well as by making extensive use of Security Orchestration, Automation, and Response (SOAR). This allows for the automation of cyber-attack prevention and response, the strengthening of security measures, the improvement of the overall security posture, the protection of customers from cyber threats, and the extraction of valuable information from the best available cyber intelligence.. Potential restraints include: Lack Of Data Security Awareness, Lack Of Security Expertise And Skilled Personnel. Notable trends are: Data security is in high demand in the manufacturing sector and is driving market growth.

• Global Big Data Analytics in Healthcare Market

Global Big Data Analytics in Healthcare Market is estimated to be valued US$ XX.X million in 2019. The report on Big Data Analytics in Healthcare Market provides qualitative as well as quantitative analysis in terms of market dynamics, competition scenarios, opportunity analysis, market growth, etc. for the forecast year up to 2029. The global big data analytics in healthcare market is segmented on the basis of type, application, and geography.

In 2019, the North America market is valued US$ XX.X million and the market share is estimated X.X%, and it is expected to be US$ XX.X million and X.X% in 2029, with a CAGR X.X% from 2020 to 2029. Read More

The Big Data Consulting Market Report is Segmented by Service Type (Strategic Consulting, Implementation Services, Analytics and Insights, Managed Services, Training and Support), Deployment Model (On-Premise, Cloud-Based, Hybrid), Organization Size (Small and Medium Enterprises (SMEs), Large Enterprises), Application (Customer Analytics, Operational Analytics, Risk and Fraud Management, Supply Chain Management, Marketing and Sales Analytics, Predictive Maintenance, Financial Analytics, Other Applications), and Geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The Big Data Analysis Software market is experiencing robust growth, driven by the increasing volume of data generated across various sectors and the rising need for actionable insights. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The widespread adoption of cloud-based solutions offers scalability, cost-effectiveness, and accessibility, accelerating market penetration. Furthermore, the growing demand for real-time analytics across industries like banking, manufacturing, and government is a major driver. Specific trends include the increasing integration of AI and machine learning into analytics platforms, enhancing predictive capabilities and automating processes. However, challenges remain, such as data security concerns, the complexity of implementing and managing big data solutions, and the skills gap in data science expertise. These factors represent potential restraints on market growth, though ongoing technological advancements and increased investment in data literacy initiatives are mitigating these issues. The market is segmented by deployment type (cloud-based and on-premises) and application (banking, manufacturing, consultancy, government, and others), with cloud-based solutions dominating due to their inherent advantages. The competitive landscape is highly dynamic, featuring both established technology giants like Google, Amazon, and IBM, alongside specialized software providers such as Rohde & Schwarz and Qlucore. The diversity of players indicates a wide range of solutions catering to diverse needs and market segments. Regional growth is expected to be diverse, with North America and Europe maintaining substantial market shares due to early adoption and advanced technological infrastructure. However, rapidly developing economies in Asia-Pacific and the Middle East & Africa are poised for significant growth, presenting lucrative opportunities for market expansion. The forecast period (2025-2033) anticipates continued market expansion, driven by technological innovations, increasing data volumes, and growing adoption across various industries and geographies. The market's long-term prospects remain positive, indicating a significant return on investment for businesses involved in its development and implementation.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define semantic models of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology. Methods The data here consists of, and was collected from:

Station metadata, in StationXML format, acquired from IRIS DMC using the fdsnws-station webservice (https://service.iris.edu/fdsnws/station/1/). Earthquake event data, in NDK format, acquired from the Global Centroid-Moment Tensor (GCMT) catalog webservice (https://www.globalcmt.org) [1,2]. Earthquake event data, in CSV format, acquired from the USGS earthquake catalog webservice (https://doi.org/10.5066/F7MS3QZH) [3].

The format of the data is described in the README. In addition, a complete description of the StationXML, NDK, and USGS file formats can be found at https://www.fdsn.org/xml/station/, https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/allorder.ndk_explained, and https://earthquake.usgs.gov/data/comcat/#event-terms, respectively. Also provided are conversions from NDK and StationXML file formats into JSON format. References: [1] Dziewonski, A. M., Chou, T. A., & Woodhouse, J. H. (1981). Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. Journal of Geophysical Research: Solid Earth, 86(B4), 2825-2852. [2] Ekström, G., Nettles, M., & Dziewoński, A. M. (2012). The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes. Physics of the Earth and Planetary Interiors, 200, 1-9. [3] U.S. Geological Survey, Earthquake Hazards Program, 2017, Advanced National Seismic System (ANSS) Comprehensive Catalog of Earthquake Events and Products: Various, https://doi.org/10.5066/F7MS3QZH.

The Big Data Infrastructure market, valued at $3.52 billion in 2025, is projected to experience robust growth, driven by the increasing volume of data generated across various sectors and the rising need for efficient data storage, processing, and analysis. The Compound Annual Growth Rate (CAGR) of 5.72% from 2025 to 2033 indicates a significant expansion of this market, fueled by several key factors. The growing adoption of cloud-based solutions for big data management offers scalability and cost-effectiveness, contributing substantially to market expansion. Furthermore, the increasing demand for real-time analytics and business intelligence across industries like finance, healthcare, and retail is a major driver. Advanced analytics techniques, such as machine learning and artificial intelligence, are further boosting the demand for sophisticated big data infrastructure. While data security and privacy concerns pose a restraint, the market's growth trajectory suggests that innovative solutions and robust regulations will mitigate these challenges. Market segmentation reveals significant opportunities across storage, server, and networking solutions, with each segment expected to witness considerable growth throughout the forecast period. North America currently holds a substantial market share, driven by early adoption and technological advancements; however, Asia-Pacific is poised for rapid growth due to increasing digitalization and infrastructure investments. Competitive landscape analysis reveals a mix of established tech giants and innovative startups, each vying for market share through strategic partnerships, acquisitions, and the development of cutting-edge technologies. The dominance of key players like Amazon, Microsoft, and Google in cloud-based solutions is a defining characteristic of the market's competitive landscape. However, specialized companies offering niche solutions are carving out valuable market segments. The market is characterized by intense competition, with companies constantly innovating to offer better performance, cost-effectiveness, and security features. Future growth will depend on technological advancements, particularly in areas like edge computing and the Internet of Things (IoT), which will generate even larger volumes of data needing efficient management. The expansion of 5G networks and the increasing adoption of AI/ML further contribute to the market's promising future. Despite potential challenges like economic fluctuations and evolving data privacy regulations, the long-term outlook for the Big Data Infrastructure market remains positive, driven by the undeniable need for efficient and scalable data management solutions in a data-driven world.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

ChinaHighO₃ 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 big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and emission inventory) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the Big Data Level 2 (L2) daily 0.1 degree (≈ 10 km) gridded full-coverage ground-level maximum 8-hour average (MDA8) O₃ products in China (CHAP_O3_D10K) from 2013 to 2020. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R²) of 0.87 and a root-mean-square error (RMSE) of 17.10 µg m^-3 on a daily basis.

If you use the ChinaHighO₃ dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2022). Note that this dataset is continuously updated, and if you need a longer period or higher temporal-resolution (e.g., daily, monthly) data, please contact the first author.

Our manuscript is just accepted in Remote Sensing of Environment and all the data will be made publicly available online once the paper is published.