Journal of Big Data Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. All articles published by the Journal of Big Data are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. As authors of articles published in the Journal of Big Data you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines.

Data Mining and Modeling Market Outlook

The global data mining and modeling market size was valued at approximately $28.5 billion in 2023 and is projected to reach $70.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This remarkable growth can be attributed to the increasing complexity and volume of data generated across various industries, necessitating robust tools and techniques for effective data analysis and decision-making processes.

One of the primary growth factors driving the data mining and modeling market is the exponential increase in data generation owing to advancements in digital technology. Modern enterprises generate extensive data from numerous sources such as social media platforms, IoT devices, and transactional databases. The need to make sense of this vast information trove has led to a surge in the adoption of data mining and modeling tools. These tools help organizations uncover hidden patterns, correlations, and insights, thereby enabling more informed decision-making and strategic planning.

Another significant growth driver is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. Data mining and modeling are critical components of AI and ML algorithms, which rely on large datasets to learn and make predictions. As businesses strive to stay competitive, they are increasingly investing in AI-driven analytics solutions. This trend is particularly prevalent in sectors such as healthcare, finance, and retail, where predictive analytics can provide a substantial competitive edge. Moreover, advancements in big data technologies are further bolstering the capabilities of data mining and modeling solutions, making them more effective and efficient.

The burgeoning demand for business intelligence (BI) and analytics solutions is also a major factor propelling the market. Organizations are increasingly recognizing the value of data-driven insights in identifying market trends, customer preferences, and operational inefficiencies. Data mining and modeling tools form the backbone of sophisticated BI platforms, enabling companies to transform raw data into actionable intelligence. This demand is further amplified by the growing importance of regulatory compliance and risk management, particularly in highly regulated industries such as banking, financial services, and healthcare.

From a regional perspective, North America currently dominates the data mining and modeling market, owing to the early adoption of advanced technologies and the presence of major market players. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation initiatives and increasing investments in AI and big data technologies. Europe also holds a significant market share, supported by stringent data protection regulations and a strong focus on innovation.

Component Analysis

The data mining and modeling market by component is broadly segmented into software and services. The software segment encompasses various tools and platforms that facilitate data mining and modeling processes. These software solutions range from basic data analysis tools to advanced platforms integrated with AI and ML capabilities. The increasing complexity of data and the need for real-time analytics are driving the demand for sophisticated software solutions. Companies are investing in custom and off-the-shelf software to enhance their data handling and analytical capabilities, thereby gaining a competitive edge.

The services segment includes consulting, implementation, training, and support services. As organizations strive to leverage data mining and modeling tools effectively, the demand for professional services is on the rise. Consulting services help businesses identify the right tools and strategies for their specific needs, while implementation services ensure the seamless integration of these tools into existing systems. Training services are crucial for building in-house expertise, enabling teams to maximize the benefits of data mining and modeling solutions. Support services ensure the ongoing maintenance and optimization of these tools, addressing any technical issues that may arise.

The software segment is expected to dominate the market throughout the forecast period, driven by continuous advancements in te

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 Data Mining Tools Market size was valued at USD 1.01 USD billion in 2023 and is projected to reach USD 1.99 USD billion by 2032, exhibiting a CAGR of 10.2 % during the forecast period. The growing adoption of data-driven decision-making and the increasing need for business intelligence are major factors driving market growth. Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times. Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information. Recent developments include: May 2023 – WiMi Hologram Cloud Inc. introduced a new data interaction system developed by combining neural network technology and data mining. Using real-time interaction, the system can offer reliable and safe information transmission., May 2023 – U.S. Data Mining Group, Inc., operating in bitcoin mining site, announced a hosting contract to deploy 150,000 bitcoins in partnership with major companies such as TeslaWatt, Sphere 3D, Marathon Digital, and more. The company is offering industry turn-key solutions for curtailment, accounting, and customer relations., April 2023 – Artificial intelligence and single-cell biotech analytics firm, One Biosciences, launched a single cell data mining algorithm called ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities., May 2022 – Europe-based Solarisbank, a banking-as-a-service provider, announced its partnership with Snowflake to boost its cloud data strategy. Using the advanced cloud infrastructure, the company can enhance data mining efficiency and strengthen its banking position.. Key drivers for this market are: Increasing Focus on Customer Satisfaction to Drive Market Growth. Potential restraints include: Requirement of Skilled Technical Resources Likely to Hamper Market Growth. Notable trends are: Incorporation of Data Mining and Machine Learning Solutions to Propel Market Growth.

Big Data Tools Market Outlook

The global Big Data Tools market size is anticipated to grow from USD 31.5 billion in 2023 to USD 103.5 billion by 2032, at a compound annual growth rate (CAGR) of 14.5%. This robust growth can be attributed to several key factors, including the increasing volume of data generated across various industries, advancements in data analytics technologies, and the growing demand for data-driven decision-making. The proliferation of IoT devices, the rise of artificial intelligence, and the emphasis on enhancing customer experience further drive the expansion of the Big Data Tools market worldwide.

The exponential increase in data generation is one of the foremost drivers of the Big Data Tools market. With the rise of digital transformation initiatives, industries are generating massive amounts of data every second. From social media interactions to transactional data and from IoT sensors to operational data, the volume, variety, and velocity of data have escalated to unprecedented levels. Organizations are increasingly recognizing the potential of leveraging this data to gain actionable insights, optimize operations, and drive business growth, thus fueling the demand for advanced Big Data tools and technologies.

Another significant growth factor is the technological advancements in data analytics and machine learning. Big Data tools have evolved from traditional data warehousing and analytics platforms to sophisticated solutions incorporating artificial intelligence and machine learning. These advancements enable organizations to perform predictive and prescriptive analytics, uncover hidden patterns, and make data-driven decisions with greater accuracy and speed. The continuous innovation and integration of advanced technologies into Big Data tools are propelling their adoption across various sectors.

The increasing emphasis on enhancing customer experience is also driving the Big Data Tools market. Businesses are leveraging Big Data analytics to gain deeper insights into customer behavior, preferences, and sentiment. By analyzing this data, organizations can personalize their offerings, improve customer engagement, and deliver superior experiences. In sectors such as retail, banking, and healthcare, the ability to understand and predict customer needs has become a competitive differentiator, leading to significant investments in Big Data tools to achieve these objectives.

Data Mining Tools play a pivotal role in the Big Data landscape by enabling organizations to extract valuable insights from vast datasets. These tools are designed to sift through large volumes of data, identify patterns, and uncover relationships that might not be immediately apparent. By leveraging advanced algorithms and statistical techniques, Data Mining Tools help businesses make informed decisions, optimize processes, and enhance strategic planning. As the volume of data continues to grow exponentially, the demand for robust and efficient Data Mining Tools is on the rise, driving innovation and competition in the market. Companies are increasingly investing in these tools to gain a competitive edge and unlock the full potential of their data assets.

From a regional perspective, North America is expected to dominate the Big Data Tools market, primarily due to the presence of leading technology companies, early adoption of advanced analytics solutions, and significant investments in data-driven initiatives. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitalization of economies, increasing internet penetration, and the burgeoning e-commerce sector are driving the demand for Big Data tools in this region. Additionally, governments in countries like China and India are promoting data analytics and AI, further boosting the market's growth prospects.

Component Analysis

The Big Data Tools market is segmented by component into software and services. The software segment includes various types of Big Data platforms and analytics tools. These software solutions are designed to handle, process, and analyze large volumes of structured and unstructured data. Key offerings within this segment include data storage solutions, data processing frameworks, data visualization tools, and advanced analytics software. The continuous innovation in software capabilities, such as real-time data analytics and AI integration, is driving the growth of this segment.

Global Big Data and Analytics market size is expected to reach $226.31 billion by 2029 at 14.6%, segmented as by analytics tools, dashboard & data visualization, self-service tools, data mining & warehousing, reporting , other analytics tools

The global Data Mining Software market is experiencing robust growth, driven by the increasing need for businesses to extract valuable insights from massive datasets. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The burgeoning adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large enterprises and SMEs. Furthermore, advancements in machine learning and artificial intelligence algorithms are enhancing the accuracy and efficiency of data mining processes, leading to better decision-making across various sectors like finance, healthcare, and marketing. The rise of big data analytics and the increasing availability of affordable, high-powered computing resources are also significant contributors to market growth. However, the market faces certain challenges. Data security and privacy concerns remain paramount, especially with the increasing volume of sensitive information being processed. The complexity of data mining software and the need for skilled professionals to operate and interpret the results present a barrier to entry for some businesses. The high initial investment cost associated with implementing sophisticated data mining solutions can also deter smaller organizations. Nevertheless, the ongoing technological advancements and the growing recognition of the strategic value of data-driven decision-making are expected to overcome these restraints and propel the market toward continued expansion. The market segmentation reveals a strong preference for cloud-based solutions, reflecting the industry's trend toward flexible and scalable IT infrastructure. Large enterprises currently dominate the market share, but SMEs are rapidly adopting data mining software, indicating promising future growth in this segment. Geographic analysis shows that North America and Europe are currently leading the market, but the Asia-Pacific region is poised for significant growth due to increasing digitalization and economic expansion in countries like China and India.

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Big Data Spending Market In Healthcare Sector Size 2024-2028

The big data spending market in healthcare sector size is forecast to increase by USD 6.99 billion at a CAGR of 10% between 2023 and 2028.

In the healthcare sector, the adoption of big data analytics is on the rise, driven by the need to enhance business efficiency and deliver superior customer experiences. Big data spending in this industry is influenced by several trends, including the increasing use of data visualization tools for decision-making, the integration of data from various sources, and the implementation of cloud solutions for data storage. However, challenges persist, such as ensuring data quality and maintaining data privacy and security. To address these challenges, continuous intelligence and real-time data processing are becoming essential. By leveraging advanced data analytics techniques, healthcare organizations can gain valuable insights from their data, leading to informed decision-making and improved patient care.

What will be the Size of the Big Data Spending Market In Healthcare Sector During the Forecast Period?

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The healthcare sector is witnessing a significant shift towards data-driven operations, fueled by the increasing volume, velocity, and variety of data. According to recent market research, big data spending in the healthcare industry is projected to grow at a steady pace. Data management is a critical aspect of this trend, with healthcare organizations investing in solutions to handle structured, semi-structured, and unstructured data. Data analytics tools, such as machine learning and predictive modeling, are increasingly being used to derive insights from this data. One of the primary applications of big data in healthcare is in consumer behavior analysis. By studying consumer data, healthcare providers can gain a better understanding of patient needs and preferences, leading to improved customer experience. Additionally, data-driven insights can aid in fraud detection, ensuring the integrity of healthcare services. Another area where big data is making a significant impact is in last-mile delivery. By analyzing patient data, healthcare providers can optimize delivery of care, ensuring timely and effective treatment.
Additionally, Natural language processing (NLP) is another area of investment, enabling healthcare organizations to extract valuable insights from unstructured data sources such as clinical notes and patient feedback. Data quality and data integration are also key focus areas, with healthcare providers investing in solutions to ensure data accuracy and seamless data flow between systems. Cloud solutions are increasingly being adopted for data storage and data visualization, offering scalability, flexibility, and cost savings. Decision-making is also being revolutionized through continuous intelligence, enabling real-time insights and improved risk management. Data talent is another area of investment, with healthcare organizations recognizing the need for skilled professionals to manage and analyze data. Security is also a top priority, with healthcare providers investing in solutions to protect sensitive patient data.
In addition, big data spending in the healthcare sector is on the rise, driven by the need to manage and analyze large volumes of data to improve patient care, optimize operations, and enhance the customer experience. Investments are being made in data management, analytics, cloud solutions, and talent development, among other areas. The implications of this trend are far-reaching, with the potential to transform the way healthcare is delivered and managed.

How is this Big Data Spending In Healthcare Sector Industry segmented and which is the largest segment?

The big data spending in healthcare sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Service

  Services
  Software


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Service Insights

The services segment is estimated to witness significant growth during the forecast period.

In the healthcare sector, the adoption of big data strategies has become essential for organizations to gain valuable business insights. Data mining and analysis from data warehouses are crucial for identifying trends, enhancing patient care, and discovering new opportunities. However, with the increasing use of data comes privacy concerns and the need for strong security measures. Professional services from big data analytics companies play a significant role in addressing these challenges. These services include data mining, data analysis, and

The Data Mining and Modeling market is experiencing robust growth, driven by the exponential increase in data volume and the rising need for businesses to extract actionable insights for strategic decision-making. The market, estimated at $25 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $75 billion by 2033. This growth is fueled by several key factors, including the increasing adoption of cloud-based data mining solutions, the development of sophisticated analytical tools capable of handling big data, and the growing demand for predictive analytics across diverse sectors such as finance, healthcare, and retail. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the capabilities of data mining and modeling tools, enabling more accurate predictions and deeper insights. The market is segmented by various deployment models (cloud, on-premise), analytical techniques (regression, classification, clustering), and industry verticals. The major restraints on market growth include the high cost of implementation and maintenance of data mining and modeling solutions, the scarcity of skilled professionals proficient in advanced analytical techniques, and concerns about data privacy and security. However, these challenges are being gradually addressed through the development of user-friendly tools, the emergence of specialized training programs, and the increasing adoption of robust security measures. The competitive landscape is characterized by a mix of established players like SAS and IBM, along with several specialized providers like Symbrium, Coheris, and Expert System. These companies are constantly innovating to enhance their offerings and cater to the evolving needs of businesses across various industries. The market's trajectory indicates a promising future driven by ongoing technological advancements and the increasing importance of data-driven decision-making in a rapidly evolving business environment.

The global market for data mining tools is experiencing robust growth, projected to reach $882.8 million in 2025. While the provided CAGR is missing, considering the rapid advancements in artificial intelligence, machine learning, and big data analytics, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 15%. This signifies a significant expansion of the market, driven by the increasing need for businesses to extract valuable insights from massive datasets for improved decision-making, enhanced operational efficiency, and competitive advantage. Key drivers include the rising adoption of cloud-based data mining solutions, the proliferation of big data, and growing investments in advanced analytics capabilities across various sectors like healthcare, finance, and retail. Furthermore, the continuous development of sophisticated algorithms and user-friendly interfaces is making data mining accessible to a wider range of users, fueling market growth. The market is highly competitive, with established players like IBM, SAS Institute, Oracle, and Microsoft alongside emerging innovative companies like H2O.ai and Dataiku vying for market share. The segmentation of the market is diverse, encompassing various deployment models (cloud, on-premise), application types (predictive modeling, customer segmentation, fraud detection), and industry verticals. While restraints such as the high cost of implementation and the need for specialized skills can hinder wider adoption, the overall market outlook remains positive. The predicted CAGR of 15% suggests the market will likely exceed $3 billion by 2033, driven by continued technological innovation, increasing data volumes, and the growing recognition of data mining's crucial role in achieving business success in an increasingly data-driven world.

Journal of Big Data CiteScore 2024-2025 - ResearchHelpDesk - The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. All articles published by the Journal of Big Data are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. As authors of articles published in the Journal of Big Data you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines.

Data Mining Software Market Outlook

The global data mining software market size was valued at USD 7.2 billion in 2023 and is projected to reach USD 15.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. This growth is driven primarily by the increasing adoption of big data analytics and the rising demand for business intelligence across various industries. As businesses increasingly recognize the value of data-driven decision-making, the market is expected to witness substantial growth.

One of the significant growth factors for the data mining software market is the exponential increase in data generation. With the proliferation of internet-enabled devices and the rapid advancement of technologies such as the Internet of Things (IoT), there is a massive influx of data. Organizations are now more focused than ever on harnessing this data to gain insights, improve operations, and create a competitive advantage. This has led to a surge in demand for advanced data mining tools that can process and analyze large datasets efficiently.

Another driving force is the growing need for personalized customer experiences. In industries such as retail, healthcare, and BFSI, understanding customer behavior and preferences is crucial. Data mining software enables organizations to analyze customer data, segment their audience, and deliver personalized offerings, ultimately enhancing customer satisfaction and loyalty. This drive towards personalization is further fueling the adoption of data mining solutions, contributing significantly to market growth.

The integration of artificial intelligence (AI) and machine learning (ML) technologies with data mining software is also a key growth factor. These advanced technologies enhance the capabilities of data mining tools by enabling them to learn from data patterns and make more accurate predictions. The convergence of AI and data mining is opening new avenues for businesses, allowing them to automate complex tasks, predict market trends, and make informed decisions more swiftly. The continuous advancements in AI and ML are expected to propel the data mining software market over the forecast period.

Regionally, North America holds a significant share of the data mining software market, driven by the presence of major technology companies and the early adoption of advanced analytics solutions. The Asia Pacific region is also expected to witness substantial growth due to the rapid digital transformation across various industries and the increasing investments in data infrastructure. Additionally, the growing awareness and implementation of data-driven strategies in emerging economies are contributing to the market expansion in this region.

Text Mining Software is becoming an integral part of the data mining landscape, offering unique capabilities to analyze unstructured data. As organizations generate vast amounts of textual data from various sources such as social media, emails, and customer feedback, the need for specialized tools to extract meaningful insights is growing. Text Mining Software enables businesses to process and analyze this data, uncovering patterns and trends that were previously hidden. This capability is particularly valuable in industries like marketing, customer service, and research, where understanding the nuances of language can lead to more informed decision-making. The integration of text mining with traditional data mining processes is enhancing the overall analytical capabilities of organizations, allowing them to derive comprehensive insights from both structured and unstructured data.

Component Analysis

The data mining software market is segmented by components, which primarily include software and services. The software segment encompasses various types of data mining tools that are used for analyzing and extracting valuable insights from raw data. These tools are designed to handle large volumes of data and provide advanced functionalities such as predictive analytics, data visualization, and pattern recognition. The increasing demand for sophisticated data analysis tools is driving the growth of the software segment. Enterprises are investing in these tools to enhance their data processing capabilities and derive actionable insights.

Within the software segment, the emergence of cloud-based data mining solutions is a notable trend. Cloud-based solutions offer several advantages, including s

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. 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. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

The global Data Mining Tools market, valued at $612.4 million in 2025, is projected to experience robust growth, driven by the increasing volume and variety of data generated across industries and the rising need for extracting actionable insights. The Compound Annual Growth Rate (CAGR) of 6.7% from 2025 to 2033 signifies a substantial expansion, propelled by several key factors. The burgeoning adoption of cloud-based data mining tools offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of these tools, enabling more sophisticated analytics and predictive modeling. Specific application areas like BFSI (Banking, Financial Services, and Insurance), Healthcare and Life Sciences, and Telecom and IT are significant contributors to market growth, fueled by the need for risk management, personalized medicine, and customer relationship management respectively. While data security and privacy concerns represent a potential restraint, the overall market outlook remains positive, driven by continuous technological innovations and increasing digitalization across industries. The market segmentation reveals a preference for cloud-based solutions over on-premises deployments, reflecting the growing demand for flexible and scalable analytics infrastructure. Leading players like IBM, SAS Institute, and Oracle are consolidating their market share through strategic partnerships and continuous product development. However, the emergence of agile and specialized data mining startups is also intensifying competition. Geographic distribution shows strong growth in North America and Europe, driven by early adoption of advanced analytics techniques. However, the Asia-Pacific region is expected to emerge as a significant growth driver in the coming years due to increasing digitalization and government initiatives promoting data-driven decision-making. The historical period (2019-2024) likely saw a similar growth trajectory, setting the stage for the forecasted expansion during 2025-2033. The continued integration of data mining tools with other business intelligence platforms is expected to further fuel market expansion.

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

The database includes three datasets. All of them were extracted from a dataset published by X (Twitter Transparency Websites) that includes tweets from malicious accounts trying to manipulate public opinion in the Kingdom of Saudi Arabia. Although the propagandist tweets were published by malicious accounts, as X (Twitter) stated, the tweets at their level were not classified as propaganda or not. Propagandists usually mix propaganda and non-propaganda tweets in an attempt to hide their identities. Therefore, it was necessary to classify their tweets as propaganda or not, based on the propaganda technique used. Since the datasets are very large, we annotated a sample of 2,100 tweets. The datasets are made up of 16,355,558 tweets from propagandist users focused on sports and banking topics.

Feature selection is an important technique for data mining before a machine learning algorithm is applied. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features can be used for prediction. We attempt to tackle this challenge by studying sparsity regularization and truncation techniques. Specifically, this article addresses two different tasks of online feature selection: (1) learning with full input where an learner is allowed to access all the features to decide the subset of active features, and (2) learning with partial input where only a limited number of features is allowed to be accessed for each instance by the learner. We present novel algorithms to solve each of the two problems and give their performance analysis. We evaluate the performance of the proposed algorithms for online feature selection on several public datasets, and demonstrate their applications to real-world problems including image classification in computer vision and microarray gene expression analysis in bioinformatics. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques.Related Publication: Hoi, S. C., Wang, J., Zhao, P., & Jin, R. (2012). Online feature selection for mining big data. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (pp. 93-100). ACM. http://dx.doi.org/10.1145/2351316.2351329 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2402/ Wang, J., Zhao, P., Hoi, S. C., & Jin, R. (2014). Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. http://dx.doi.org/10.1109/TKDE.2013.32 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2277/

The open-source big data tools market is experiencing robust growth, driven by the increasing need for scalable, cost-effective data management and analysis solutions across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume and velocity of data generated across industries, from banking and finance to manufacturing and government, necessitate powerful and adaptable tools. Secondly, the cost-effectiveness and flexibility of open-source solutions compared to proprietary alternatives are major drawcards, especially for smaller organizations and startups. The ease of customization and community support further enhance their appeal. Growth is also being propelled by technological advancements such as the development of more sophisticated data analytics tools, improved cloud integration, and increased adoption of containerization technologies like Docker and Kubernetes for deployment and management. The market's segmentation across application (banking, manufacturing, etc.) and tool type (data collection, storage, analysis) reflects the diverse range of uses and specialized tools available. Key restraints to market growth include the complexity associated with implementing and managing open-source solutions, requiring skilled personnel and ongoing maintenance. Security concerns and the need for robust data governance frameworks also pose challenges. However, the growing maturity of the open-source ecosystem, coupled with the emergence of managed services providers offering support and expertise, is mitigating these limitations. The continued advancements in artificial intelligence (AI) and machine learning (ML) are further integrating with open-source big data tools, creating synergistic opportunities for growth in predictive analytics and advanced data processing. This integration, alongside the ever-increasing volume of data needing analysis, will undoubtedly drive continued market expansion over the forecast period.

The Big Data Analytics market is experiencing robust growth, driven by the increasing volume of data generated across various industries and the need for deriving actionable insights. The market, estimated at $150 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $450 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based analytics solutions offers scalability, cost-effectiveness, and enhanced accessibility. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are significantly boosting the analytical capabilities of big data platforms, enabling more sophisticated predictive modeling and real-time insights. Government initiatives promoting data-driven decision-making in various sectors also contribute to market growth. However, challenges remain, including the need for skilled professionals to manage and interpret complex data sets, concerns regarding data security and privacy, and the high initial investment costs associated with implementing big data solutions. Segment-wise, the cloud-based segment is anticipated to dominate the market due to its inherent advantages, while the on-premise deployment model continues to hold a significant share, catering to specific industry requirements. Key players like IBM, Oracle, Microsoft, and SAP are actively investing in research and development, expanding their product portfolios, and forging strategic partnerships to maintain their competitive edge. The competitive landscape is characterized by both established technology vendors and emerging startups, leading to continuous innovation and increased market dynamism. The geographic distribution shows strong growth in North America and Europe, driven by high technological adoption and the presence of major market players. However, Asia-Pacific is emerging as a key region for future expansion, fueled by increasing digitalization and government investments in infrastructure. The market's future trajectory suggests that ongoing technological advancements, coupled with increasing data volumes, will continue to propel its expansion.