Journal of Big Data FAQ - 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.

This statistic shows the importance of big data search technologies in organizations worldwide as of 2019. Around ** percent of respondents stated that Elasticsearch was critical or very important for their organization as of 2019.

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.

Big Data Analytics Tools Market Outlook

The global big data analytics tools market size was valued at approximately USD 45.5 billion in 2023 and is expected to reach around USD 120.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.4% during the forecast period. The growth of this market can be attributed to the increasing adoption of advanced analytics tools across various sectors to harness the power of big data.

One of the primary growth factors driving the big data analytics tools market is the rapid digitization across industries. Organizations are generating massive volumes of data through various sources such as social media, sensors, and transactional databases. The need to analyze this data and derive actionable insights to drive business decisions is propelling the demand for big data analytics tools. These tools enable organizations to gain a competitive edge, improve operational efficiency, and enhance customer experience by providing accurate and timely insights.

Another significant factor contributing to the market growth is the increasing adoption of AI and machine learning technologies. Integrating these advanced technologies with big data analytics tools has revolutionized the way data is analyzed and interpreted. AI-driven analytics enables predictive and prescriptive insights that help organizations in strategic planning and decision-making processes. Furthermore, the advent of advanced algorithms and computational capabilities has made it possible to process and analyze vast datasets in real-time, further boosting the market growth.

The proliferation of the Internet of Things (IoT) is also a major driver for the big data analytics tools market. With the increasing number of connected devices, a massive amount of data is being generated every second. Big data analytics tools are essential for managing and analyzing this data to derive meaningful insights. IoT data analytics helps in improving operational efficiencies, optimizing resource utilization, and enhancing product and service offerings. The integration of IoT with big data analytics tools is creating new opportunities for businesses to innovate and grow.

From a regional perspective, North America holds a significant share in the big data analytics tools market due to the early adoption of advanced technologies and the presence of major industry players. The region's robust IT infrastructure and high investment in research and development activities further accelerate market growth. Europe follows closely, with significant investments in big data projects and stringent data protection regulations driving the demand for analytics tools. The Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rising digital transformation initiatives and increasing adoption of big data technologies across various industries.

Component Analysis

The big data analytics tools market by component is segmented into software and services. The software segment dominates the market and is expected to continue its dominance throughout the forecast period. The software segment includes various types of analytics tools such as data discovery, data visualization, data mining, and predictive analytics software. These tools are essential for analyzing large datasets and extracting valuable insights. The growing need for data-driven decision-making and the increasing complexity of data are driving the demand for advanced analytics software.

On the other hand, the services segment is also witnessing significant growth. This segment includes professional services such as consulting, implementation, and support & maintenance services. Organizations often require expert assistance in deploying and managing big data analytics tools. Consulting services help businesses in selecting the right analytics tools and creating a robust data strategy. Implementation services ensure the seamless integration of analytics tools into existing IT infrastructure, while support & maintenance services provide ongoing technical assistance to ensure optimal performance. The increasing complexity of big data projects and the need for specialized skills are driving the growth of the services segment.

The integration of cloud-based analytics tools is also contributing to the growth of the software and services segments. Cloud-based solutions offer scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. The ability to access analytics tools on-demand and pay for only wh

This statistic displays the results of a survey on the apps and search engines tracking online users in Italy in 2016, broken down by type. During the survey period, it was found that seven in ten respondents thought that search engines could clearly understand the lifestyle and interests of its users.

Five files, one of which is a ZIP archive, containing data that support the findings of this study. PDF file "IA screenshots CSU Libraries search config" contains screenshots captured from the Internet Archive's Wayback Machine for all 24 CalState libraries' homepages for years 2017 - 2019. Excel file "CCIHE2018-PublicDataFile" contains Carnegie Classifications data from the Indiana University Center for Postsecondary Research for all of the CalState campuses from 2018. CSV file "2017-2019_RAW" contains the raw data exported from Ex Libris Primo Analytics (OBIEE) for all 24 CalState libraries for calendar years 2017 - 2019. CSV file "clean_data" contains the cleaned data from Primo Analytics which was used for all subsequent analysis such as charting and import into SPSS for statistical testing. ZIP archive file "NonparametricStatisticalTestsFromSPSS" contains 23 SPSS files [.spv format] reporting the results of testing conducted in SPSS. This archive includes things such as normality check, descriptives, and Kruskal-Wallis H-test results.

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.

AI-Powered Cognitive Search Market Outlook

The global AI-powered cognitive search market size is projected to grow from $2.35 billion in 2023 to $10.45 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 18.1% over the forecast period. This impressive growth is driven by the increasing demand for advanced data analytics tools and the need for enhanced customer experience across various industries. The integration of artificial intelligence (AI) technology in search algorithms has significantly improved the ability to retrieve relevant information, thus fueling the market expansion.

One of the primary growth factors for the AI-powered cognitive search market is the exponential increase in data generation across industries. With the advent of big data, organizations are accumulating vast amounts of unstructured data, which traditional search methods struggle to manage effectively. AI-powered cognitive search leverages machine learning, natural language processing (NLP), and other AI technologies to analyze and index this data, allowing organizations to derive actionable insights and make data-driven decisions. This capability is particularly valuable in sectors such as healthcare, BFSI, and IT, where the rapid retrieval of relevant information can significantly impact operational efficiency and customer satisfaction.

Furthermore, the growing emphasis on personalized customer experiences is propelling the adoption of AI-powered cognitive search solutions. Modern consumers expect quick and accurate responses to their queries, and businesses are increasingly recognizing the need to enhance their search functionalities to meet these expectations. By implementing AI-powered cognitive search, companies can provide more relevant search results and recommendations, thereby improving customer engagement and loyalty. This trend is especially prominent in the retail and e-commerce sectors, where personalized interactions can drive higher conversion rates and revenues.

Additionally, advancements in AI technologies, such as deep learning and NLP, are continuously enhancing the capabilities of cognitive search solutions. These technologies enable search systems to understand the context and intent behind user queries, leading to more accurate and relevant search results. As a result, organizations are investing heavily in AI research and development to stay competitive in the market. The ongoing innovation in AI-powered cognitive search tools is expected to create new growth opportunities and drive market expansion over the forecast period.

The regional outlook for the AI-powered cognitive search market indicates significant growth across various geographies. North America currently holds the largest market share, primarily due to the presence of leading technology companies and high adoption rates of AI technologies. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by the increasing digital transformation initiatives and the rising demand for advanced analytics solutions in countries like China and India. Europe and Latin America are also anticipated to experience substantial growth, supported by the growing awareness of AI benefits and the increasing investments in AI infrastructure.

Component Analysis

The AI-powered cognitive search market can be segmented by components into software and services. The software segment is expected to hold the largest market share, driven by the increasing adoption of AI-based search solutions across various industries. These software solutions incorporate advanced algorithms and AI technologies such as machine learning and NLP to enhance search accuracy and relevance. The continuous advancements in AI technologies are further boosting the capabilities of cognitive search software, enabling them to provide more sophisticated and intuitive search experiences.

Within the software segment, several sub-segments can be identified, including enterprise search software, cognitive search platforms, and industry-specific search solutions. Enterprise search software is designed to cater to the needs of large organizations, providing comprehensive search capabilities across diverse data sources. Cognitive search platforms, on the other hand, offer more specialized functionalities, often tailored to specific use cases or industries. Industry-specific search solutions are customized to address the unique requirements of sectors such as healthcare, retail, and BFSI, enhancing their ability to retrieve relevant information quickly and accurately.

Dataset for the large experiment used in the paper entitled: "Sequence Diagram Refactoring using Single and Hybridized Algorithms"

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Marine Big Data Market Outlook

According to our latest research, the global marine big data market size reached USD 2.61 billion in 2024 and is projected to expand at a robust CAGR of 17.4% from 2025 to 2033, reaching an estimated USD 10.44 billion by 2033. The primary growth factor driving this market is the surging adoption of advanced analytics and artificial intelligence for marine applications, enabling real-time decision-making and improved operational efficiency across the maritime sector.

The explosive growth of the marine big data market is underpinned by the increasing digital transformation across the maritime industry. As shipping companies, port operators, and marine research institutions embrace digital technologies, the volume, variety, and velocity of marine data have surged dramatically. This data deluge, generated from satellite imagery, remote sensors, Automatic Identification Systems (AIS), and underwater drones, necessitates robust big data platforms for storage, processing, and analytics. The integration of Internet of Things (IoT) devices and the proliferation of smart ships have further fueled the demand for comprehensive data management solutions, enabling stakeholders to derive actionable insights, optimize routes, reduce fuel consumption, and enhance safety protocols. Additionally, the growing focus on environmental sustainability and regulatory compliance has led to a rising need for advanced analytics to monitor marine ecosystems and comply with international maritime standards.

Another key growth factor for the marine big data market is the increasing emphasis on maritime safety and security. Governments and defense agencies worldwide are leveraging big data analytics to enhance situational awareness, monitor illegal activities such as piracy and smuggling, and ensure the safety of maritime assets. The ability to analyze vast datasets in real time allows for proactive risk assessment and rapid response to emerging threats. Furthermore, the integration of big data with geospatial intelligence and machine learning algorithms has enabled predictive analytics for accident prevention, vessel tracking, and search-and-rescue operations. This trend is particularly pronounced in regions with high maritime traffic, such as the Asia Pacific and North America, where the need for advanced surveillance and monitoring capabilities is paramount.

The burgeoning demand for sustainable fisheries and aquaculture management is also contributing significantly to the market’s expansion. Fisheries and aquaculture sectors are increasingly utilizing big data solutions to monitor fish stocks, optimize harvesting schedules, and ensure regulatory compliance. By analyzing data from sonar, underwater cameras, and environmental sensors, stakeholders can make informed decisions to prevent overfishing, protect endangered species, and maintain ecosystem balance. Moreover, the adoption of big data analytics in marine research institutions has accelerated scientific discoveries related to oceanography, climate change, and marine biodiversity. This collaborative approach between commercial entities, government agencies, and research organizations is fostering innovation and driving the growth of the marine big data market.

From a regional perspective, Asia Pacific is emerging as the most dynamic market for marine big data, driven by rapid industrialization, expanding maritime trade, and significant investments in port infrastructure. Countries such as China, Japan, and South Korea are at the forefront of adopting advanced marine analytics to enhance operational efficiency and environmental stewardship. North America remains a key contributor, owing to its robust defense expenditure, technological advancements, and strong presence of leading market players. Europe is also witnessing substantial growth, propelled by stringent environmental regulations and a mature maritime industry. Meanwhile, the Middle East & Africa and Latin America are gradually embracing marine big data solutions, supported by government initiatives and international collaborations aimed at bolstering maritime security and resource management.

Big data is data that is so voluminous and complex that traditional data-processing application software is inadequate to deal with it in its entirety. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. Big data is vital for companies of all stripes, as it allows them to understand their market better, understand their customers better, identify potential areas of opportunity and uncover internal waste and inefficiency. Read More

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

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

Explore the historical Whois records related to big-data-central.com (Domain). Get insights into ownership history and changes over time.

Big Data Analytics in Retail market will be growing at a CAGR of 23.49% during 2025 to 2033.

The Search as a Service (SaaS) market is experiencing robust growth, driven by the increasing demand for scalable, cost-effective, and AI-powered search solutions across various industries. The market's expansion is fueled by the rising adoption of cloud computing, the proliferation of big data, and the need for enhanced customer experiences through improved search functionality. Businesses are increasingly outsourcing their search infrastructure and management to SaaS providers to leverage their expertise and avoid the complexities of maintaining in-house solutions. This shift is particularly evident in e-commerce, media, and enterprise applications where effective search is crucial for user engagement and revenue generation. We estimate the 2025 market size at $15 billion, based on observed growth in related cloud services and the expanding adoption of SaaS models. A conservative Compound Annual Growth Rate (CAGR) of 18% is projected for the period 2025-2033, leading to a market value exceeding $60 billion by 2033. This growth is tempered by potential restraints such as data security concerns, vendor lock-in risks, and the need for robust integration with existing systems. Key players like Amazon Web Services, IBM, Google, and Microsoft are at the forefront of this market, constantly innovating with advanced algorithms, enhanced personalization features, and improved analytics capabilities. The market is segmented by deployment model (cloud, on-premise), industry vertical (e-commerce, media, healthcare), and functionalities (e.g., visual search, semantic search). The competitive landscape is highly dynamic, with ongoing mergers and acquisitions, strategic partnerships, and the continuous development of new features to maintain a competitive edge. Geographic growth is expected to be particularly strong in regions with rapidly developing digital economies and expanding internet penetration, like Asia-Pacific and Latin America. However, regulatory hurdles and data privacy regulations in certain regions could act as localized constraints.

The Intelligent Semantic Data Service market is experiencing robust growth, driven by the increasing need for organizations to extract actionable insights from rapidly expanding data volumes. The market's complexity necessitates sophisticated solutions that go beyond traditional data analytics, focusing on understanding the meaning and context of data. This demand is fueled by advancements in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), which power semantic analysis engines. Key players like Google, IBM, Microsoft, Amazon, and others are heavily investing in this space, developing and deploying powerful solutions that cater to various industries, from finance and healthcare to retail and manufacturing. The market's projected Compound Annual Growth Rate (CAGR) suggests a significant expansion over the forecast period (2025-2033). We estimate the 2025 market size to be approximately $15 billion, based on industry reports and observed growth trajectories in related AI segments. This figure is expected to reach approximately $35 billion by 2033. Several factors contribute to this growth, including the rising adoption of cloud-based solutions, the need for improved data governance, and a growing emphasis on data-driven decision-making. However, the market also faces certain restraints. High implementation costs, the need for specialized expertise, and data security concerns can hinder widespread adoption. Furthermore, the market is characterized by a relatively high barrier to entry, favoring established players with significant R&D capabilities. Nevertheless, the potential benefits of unlocking the true value of unstructured data through intelligent semantic analysis are compelling enough to drive continued investment and innovation in this rapidly evolving market. Segmentation within the market is likely based on deployment type (cloud, on-premise), service type (data enrichment, knowledge graph creation, semantic search), and industry vertical. The geographic distribution shows a strong concentration in North America and Europe, followed by a steady growth in the Asia-Pacific region, driven by increasing digitalization efforts.

Insurance is a highly data-intensive industry, making it extremely important for insurers to manage large volumes of data from both traditional and non-traditional sources. As a result, many insurers are spending significant amounts on building Big Data capabilities, finding that Big Data analytics can have an impact on nearly all insurance processes, from product development to claims management. The objective should be to identify areas where Big Data can make a difference and create value for both insurers and the insured. Technology vendors are also aware of Big Data’s ability to deliver business insights. Simplification, cost reduction and revenue growth are the three key drivers behind Big Data’s adoption in the insurance industry. Read More

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phe-nomena, measures that might serve an important role as leading indicators in forecasts and now-casts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies for variables following gamma and spatially auto-correlated distributions, and random walks. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress towards accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from study authors.