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.

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.

USAGE OF DISSIMILARITY MEASURES AND MULTIDIMENSIONAL SCALING FOR LARGE SCALE SOLAR DATA ANALYSIS Juan M Banda, Rafal Anrgyk ABSTRACT: This work describes the application of several dissimilarity measures combined with multidimensional scaling for large scale solar data analysis. Using the first solar domain-specific benchmark data set that contains multiple types of phenomena, we investigated combination of different image parameters with different dissimilarity measure sin order to determine which combination will allow us to differentiate our solar data within each class and versus the rest of the classes. In this work we also address the issue of reducing dimensionality by applying multidimensional scaling to our dissimilarity matrices produced by the previously mentioned combination. By applying multidimensional scaling we can investigate how many resulting components are needed in order to maintain a good representation of our data (in an artificial dimensional space) and how many can be discarded in order to economize our storage costs. We present a comparative analysis between different classifiers in order to determine the amount of dimensionality reduction that can be achieved with said combination of image parameters, similarity measure and multidimensional scaling.

Big Data Infrastructure Market Outlook

The global Big Data Infrastructure market size was valued at approximately $98 billion in 2023 and is projected to grow to around $235 billion by 2032, exhibiting a compound annual growth rate (CAGR) of about 10.1% during the forecast period. This impressive growth can be attributed to the increasing demand for big data analytics across various sectors, which necessitates robust infrastructure capable of handling vast volumes of data effectively. The need for real-time data processing has also been a significant driver, as organizations seek to harness data to gain competitive advantages, improve operational efficiencies, and enhance customer experiences.

One of the primary growth factors driving the Big Data Infrastructure market is the exponential increase in data generation from digital sources. With the proliferation of connected devices, social media, and e-commerce, the volume of data generated daily is staggering. Organizations are realizing the value of this data in gaining insights and making informed decisions. Consequently, there is a growing demand for infrastructure solutions that can store, process, and analyze this data effectively. Additionally, developments in cloud computing have made big data technology more accessible and affordable, further fueling market growth. The ability to scale resources on-demand without significant upfront capital investment is particularly appealing to businesses.

Another critical factor contributing to the growth of the Big Data Infrastructure market is the advent of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies require sophisticated data management solutions capable of handling complex and large-scale data sets. As industries across the spectrum from healthcare to manufacturing integrate these technologies into their operations, the demand for capable infrastructure is scaling correspondingly. Moreover, regulatory requirements around data management and security are prompting organizations to invest in reliable infrastructure solutions to ensure compliance and safeguard sensitive information.

The role of data analytics in shaping business strategies and operations has never been more pertinent, driving organizations to invest in Big Data Infrastructure. Businesses are keenly focusing on customer-centric approaches, understanding market trends, and innovating based on data-driven insights. The ability to predict trends, consumer behavior, and potential challenges offers a significant strategic advantage, further pushing the demand for robust data infrastructure. Additionally, strategic partnerships between technology providers and enterprises are fostering an ecosystem conducive to big data initiatives.

From a regional perspective, North America currently holds the largest share in the Big Data Infrastructure market, driven by the early adoption of advanced technologies and the presence of major technology companies. The region's strong digital economy and a high degree of IT infrastructure sophistication are further bolstering its market position. Europe is expected to follow suit, with significant investments in data infrastructure to meet regulatory standards and drive digital transformation. The Asia Pacific region, however, is anticipated to witness the highest growth rate, attributed to rapid digitalization, the proliferation of IoT devices, and increasing awareness of the benefits of big data analytics among businesses. Other regions like Latin America and the Middle East & Africa are also poised for growth, albeit at a relatively moderate pace, as they continue to embrace digital technologies.

Component Analysis

In the realm of Big Data Infrastructure, the component segment is categorized into hardware, software, and services. The hardware segment consists of the physical pieces needed to store and process big data, such as servers, storage devices, and networking equipment. This segment is crucial because the efficiency of data processing depends significantly on the capabilities of these physical components. With the rise in data volumes, there’s an increased demand for scalable and high-performance hardware solutions. Organizations are investing heavily in upgrading their existing hardware to ensure they can handle the data influx effectively. Furthermore, the development of advanced processors and storage systems is enabling faster data processing and retrieval, which is critical for real-time analytics.

The software segment of Big Data Infrastructure encompasses analytics soft

Summary

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put in a lot of effort to collect large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

Data record

The data were organized according to the Brain-Imaging-Data-Structure (BIDS) Specification version 1.7.0 and can be accessed from the OpenNeuro public repository (accession number: XXX). In short, raw data of each subject were stored in “sub-

Stimulus images The stimulus images for different fMRI experiments are deposited in separate folders: “stimuli/imagenet”, “stimuli/coco”, “stimuli/prf”, and “stimuli/floc”. Each experiment folder contains corresponding stimulus images, and the auxiliary files can be found within the “info” subfolder.

Raw MRI data Each participant folder consists of several session folders: anat, coco, imagenet, prf, floc. Each session folder in turn includes “anat”, “func”, or “fmap” folders for corresponding modality data. The scan information for each session is provided in a TSV file.

Preprocessed volume data from fMRIprep The preprocessed volume-based fMRI data are in subject's native space, saved as “sub-

Preprocessed surface-based data from ciftify The preprocessed surface-based data are in standard fsLR space, saved as “sub-

Brain activation data from surface-based GLM analyses The brain activation data are derived from GLM analyses on the standard fsLR space, saved as “sub-

Next Generation Data Center Market Outlook

The global next generation data center market is projected to reach a market size of USD 120 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.3% from USD 40 billion in 2023. This significant growth is driven by the increasing adoption of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) which demand robust and scalable data center infrastructure. The expanding digital economy and the exponential growth in data generation are also key factors propelling the market forward. Moreover, the surge in cloud computing and the growing demand for data storage and management solutions are further contributing to the market's expansion.

One of the primary growth factors for the next generation data center market is the increasing reliance on cloud services across various sectors. Organizations are rapidly migrating their applications and data to the cloud to leverage its scalability, flexibility, and cost-efficiency. This trend is driving the demand for cloud-based data centers that can handle significant amounts of data and support advanced computing workloads. Additionally, the proliferation of big data analytics is fueling the need for data centers that can efficiently store, process, and analyze vast volumes of data, thus accelerating market growth.

Another major driver of the market is the rise of edge computing, which necessitates the deployment of data centers closer to data sources to reduce latency and improve performance. Edge data centers enable real-time data processing and support applications that require low-latency connectivity, such as autonomous vehicles, smart cities, and industrial automation. As the adoption of edge computing grows, so does the need for next generation data centers that can provide the necessary infrastructure and capabilities. Furthermore, the advancements in networking technologies like 5G are expected to enhance the performance and connectivity of data centers, thereby boosting market growth.

The concept of a Mega Data Center is becoming increasingly relevant in today's data-driven world. These facilities are designed to handle vast amounts of data and provide the necessary infrastructure to support large-scale cloud and internet services. Mega Data Centers are characterized by their ability to scale rapidly and manage extensive workloads, making them essential for major technology companies and service providers. As the demand for cloud computing and data-intensive applications continues to grow, the development of Mega Data Centers is expected to play a crucial role in meeting these needs. Their strategic locations and advanced technologies enable them to offer unparalleled performance, reliability, and efficiency, further driving the growth of the next generation data center market.

Energy efficiency and sustainability are also key factors influencing the growth of the next generation data center market. With increasing concerns about the environmental impact of data centers, there is a growing emphasis on designing and operating energy-efficient facilities. Innovations in cooling solutions, power management, and renewable energy integration are enabling data centers to reduce their carbon footprint and operational costs. This focus on sustainability is driving the adoption of next generation data centers that are designed to be more energy-efficient and environmentally friendly, further propelling market growth.

In terms of regional outlook, North America is expected to dominate the next generation data center market during the forecast period, owing to the presence of major technology companies and a high adoption rate of advanced technologies. The region's well-established IT infrastructure and supportive government initiatives for data center development are also contributing to its market leadership. Meanwhile, the Asia Pacific region is anticipated to witness the highest growth rate due to the rapid digital transformation, increasing internet penetration, and expanding cloud services market in countries like China and India. Europe is also projected to experience substantial growth, driven by stringent data protection regulations and the increasing focus on sustainability in data center operations.

Data Center Renovation is an emerging trend as organizations seek to modernize their existing infrastructu

The batch compute market is experiencing robust growth, driven by the increasing need for processing large datasets in various industries. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors. The rise of big data analytics and the proliferation of artificial intelligence (AI) and machine learning (ML) applications necessitate powerful, cost-effective solutions for large-scale data processing, making batch compute a critical infrastructure component. Furthermore, cloud computing's continued adoption lowers the barrier to entry for organizations of all sizes, enabling access to scalable and on-demand batch compute resources. The increasing adoption of cloud-native architectures and serverless computing further contributes to market growth. However, the market also faces challenges. Data security and privacy concerns remain a significant hurdle, requiring robust security measures to protect sensitive information processed through batch compute systems. The complexity of managing and optimizing batch workloads can also pose a challenge, demanding specialized expertise and efficient workflow management tools. Competition among major players like Amazon, Alibaba, Microsoft, Tencent, Google, Huawei, Esri, and BMC is intense, leading to price pressures and the constant need for innovation. Nevertheless, the overall outlook remains positive, with continued growth expected as more industries embrace data-driven decision-making and adopt advanced analytical techniques.

Hadoop Related Software Market Outlook

The global Hadoop related software market size is projected to increase from USD 30 billion in 2023 to approximately USD 89 billion by 2032, reflecting a robust CAGR of 12.8%. The remarkable growth in this market can be attributed to the escalating volumes of data being generated across various sectors, prompting the need for efficient data storage, processing, and analysis solutions.

One of the main growth factors driving the Hadoop related software market is the exponential increase in data generation from multiple sources, such as IoT devices, social media, and enterprise applications. Organizations are increasingly relying on big data analytics to gain insights and make data-driven decisions, which has propelled the demand for Hadoop-based solutions. Additionally, the integration of advanced technologies like artificial intelligence and machine learning with Hadoop software has further fueled market growth by enabling more sophisticated data analysis capabilities.

Another significant factor contributing to the market's expansion is the cost-effectiveness and scalability offered by Hadoop solutions. Traditional data warehousing solutions often come with high costs and limited scalability. In contrast, Hadoop provides a more affordable and flexible framework for storing and processing large datasets, making it an attractive option for businesses of all sizes. Moreover, the open-source nature of Hadoop software reduces licensing costs, which is particularly beneficial for small and medium enterprises (SMEs).

Furthermore, the growing adoption of cloud-based services has positively impacted the Hadoop related software market. Cloud deployments of Hadoop solutions offer enhanced flexibility, faster deployment times, and reduced infrastructure costs. As more organizations migrate their data and applications to the cloud, the demand for cloud-based Hadoop solutions has surged. This trend is expected to continue, driven by the increasing need for remote data access and real-time analytics.

Regionally, North America is expected to dominate the Hadoop related software market, accounting for a significant share of the global revenue. The region's technological advancements, coupled with the presence of major market players, have facilitated swift adoption of Hadoop solutions. Additionally, the Asia Pacific region is projected to witness substantial growth, driven by the increasing digitalization initiatives and rising investments in big data technologies in countries like China and India.

Component Analysis

The Hadoop related software market is segmented into two primary components: software and services. The software segment includes various Hadoop distributions, tools, and platforms that enable data storage, processing, and analysis. This segment has seen considerable growth due to the rising demand for robust data management solutions. Companies are increasingly adopting Hadoop software to handle large-scale data operations efficiently. Key software offerings include Hadoop Distributed File System (HDFS), MapReduce, and Hadoop YARN, which together provide a comprehensive framework for big data applications.

In the services segment, the market encompasses consulting, implementation, support, and maintenance services. As organizations grapple with the complexities of deploying and managing Hadoop environments, the need for specialized services has become more pronounced. Consulting services help organizations strategize their big data initiatives, while implementation services ensure the seamless integration of Hadoop solutions into existing IT infrastructures. Additionally, support and maintenance services play a crucial role in ensuring the smooth operation and optimization of Hadoop ecosystems.

The software segment is expected to maintain a dominant position in the market due to the continuous advancements in Hadoop technologies and the introduction of new tools and platforms. However, the services segment is also poised for significant growth, driven by the increasing demand for expertise in managing Hadoop implementations. As more organizations adopt Hadoop solutions, the need for professional services to support these deployments is likely to rise.

Moreover, the integration of Hadoop software with other advanced technologies, such as machine learning and artificial intelligence, is creating new opportunities within the software segment. These integrations enable more sophisticated data analysis and predictive modeling, enhancing the v

This study investigates the extent to which data science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity.results.tar.gz: Extracted data for each project, including raw logs of all detected code violations.notebooks_out.tar.gz: Tables and figures generated by notebooks.source_code_anonymized.tar.gz: Anonymized source code (at time of publication) to identify, clone, and analyse the projects. Also includes Jupyter notebooks used to produce figures in the paper.The latest source code can be found at: https://github.com/a2i2/mining-data-science-repositoriesPublished in ESEM 2020: https://doi.org/10.1145/3382494.3410680Preprint: https://arxiv.org/abs/2007.08978

Cloud Based Big Data Market Outlook

The global market size for Cloud Based Big Data was valued at approximately USD 45 billion in 2023 and is projected to reach around USD 285 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.3% during the forecast period. This rapid expansion is driven by the increasing adoption of cloud technologies across various sectors, the rising need for data analytics, and advancements in artificial intelligence and machine learning algorithms that require robust big data platforms.

One primary growth factor for the Cloud Based Big Data market is the exponential increase in data generation from various sources such as social media, IoT devices, and enterprise applications. As data continues to proliferate, organizations are compelled to seek efficient and scalable solutions for data storage, processing, and analysis. Cloud-based platforms provide the necessary infrastructure and tools to manage such vast amounts of data, making them indispensable for modern businesses. Additionally, the flexibility and scalability of cloud solutions enable organizations to handle peak loads dynamically, further driving their adoption.

Another significant factor contributing to market growth is the substantial cost savings associated with cloud-based solutions. Traditional on-premise big data infrastructure requires significant capital investment in hardware and software, as well as ongoing maintenance costs. In contrast, cloud-based solutions operate on a pay-as-you-go model, allowing organizations to scale their resources up or down based on demand. This economic advantage is particularly appealing to small and medium enterprises (SMEs) that may lack the financial resources to invest in large-scale infrastructure.

Furthermore, the integration of advanced data analytics capabilities with cloud platforms is revolutionizing how organizations derive insights from their data. Cloud-based big data solutions now come equipped with machine learning, artificial intelligence, and data visualization tools that enable real-time analytics and decision-making. These advanced capabilities are transforming industries by providing actionable insights that drive business growth, enhance customer experiences, and optimize operations. The continuous improvement and innovation in these technologies are significant drivers of market expansion.

Big Data Consulting services are becoming increasingly vital as organizations strive to harness the full potential of their data. These services offer expert guidance on implementing big data strategies, selecting the right technologies, and optimizing data processes to align with business goals. By leveraging Big Data Consulting, companies can navigate the complexities of data management, ensuring that they not only store and process data efficiently but also derive actionable insights. This expertise is particularly crucial in today's rapidly evolving digital landscape, where staying competitive requires a deep understanding of data-driven decision-making.

From a regional perspective, North America holds a significant share of the Cloud Based Big Data market due to the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation in countries like China and India, coupled with government initiatives promoting cloud adoption, is propelling the market in this region. Additionally, the growing awareness of the benefits of big data analytics among enterprises in this region is further fueling market growth.

Component Analysis

The Cloud Based Big Data market can be segmented by component into two primary categories: Software and Services. Software solutions encompass a wide range of tools and applications designed for data storage, processing, analysis, and visualization. These include big data platforms, data integration tools, business intelligence software, and advanced analytics applications. The demand for these software solutions is driven by the need for efficient data management and the ability to derive actionable insights from vast datasets. Innovations in machine learning and AI integrated within these software solutions are further enhancing their capabilities and attractiveness to enterprises.

Services, on the other hand, include various support and maintenance services, consulting

To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

High Performance Data Analytics (HPDA) Market Outlook

The global High Performance Data Analytics (HPDA) market size was valued at approximately USD 43.5 billion in 2023 and is projected to reach around USD 105.9 billion by 2032, growing at a CAGR of 10.3% from 2024 to 2032. The primary growth factors for this market include the increasing volume of complex data generated across various industries, advancements in AI and machine learning, and the growing need for real-time data analytics solutions.

The rapid and exponential increase in data generation is one of the foremost growth drivers for the HPDA market. Organizations across various sectors are generating massive amounts of data that need to be processed and analyzed to gain actionable insights. This deluge of data has necessitated the adoption of high-performance data analytics to handle large-scale data processing, leading to enhanced decision-making and operational efficiencies. Additionally, the growing adoption of the Internet of Things (IoT) devices contributes significantly to the data influx, further driving the demand for HPDA solutions.

Another critical growth factor is the technological advancements in artificial intelligence (AI) and machine learning (ML). These technologies require advanced data analytics capabilities to train, validate, and implement AI and ML models effectively. HPDA provides the computational power and sophisticated algorithms needed to analyze vast datasets quickly and accurately, making it indispensable for AI-driven organizations. Furthermore, the integration of AI and HPDA is helping industries like healthcare, finance, and manufacturing to innovate and improve their service offerings, thus propelling market growth.

The need for real-time data analytics is also propelling the HPDA market forward. In today's fast-paced business environment, organizations require immediate insights to stay competitive. Real-time data analytics enables businesses to monitor operations, detect anomalies, and make informed decisions instantaneously. HPDA offers the computational speed and efficiency necessary to perform real-time analytics on large datasets, thus meeting the growing demand from sectors such as BFSI, retail, and IT and telecommunications for real-time data processing capabilities.

Regionally, North America holds a significant share of the HPDA market due to its advanced technological infrastructure and high adoption rate of data analytics solutions. The presence of leading HPDA solution providers and substantial investments in research and development further bolster the market in this region. Meanwhile, Asia-Pacific is anticipated to witness the highest growth rate during the forecast period, driven by the rapid digital transformation in countries like China, India, and Japan. The increasing number of small and medium enterprises (SMEs) and their growing inclination towards adopting advanced data analytics solutions are also contributing to the market's growth in this region.

Component Analysis

The HPDA market is segmented into hardware, software, and services, each playing a crucial role in the ecosystem. Hardware components include high-performance servers, storage systems, and networking components designed to handle large-scale data processing. The hardware segment is pivotal as it provides the necessary computational power and storage capabilities required for processing vast datasets. With advancements in hardware technology, such as the development of GPUs and specialized processors for data analytics, the efficiency and speed of data processing have significantly improved, making hardware a critical component of HPDA solutions.

In addition to hardware, software plays an equally important role in the HPDA market. This segment includes analytics software, data management software, and visualization tools that facilitate the analysis and interpretation of large datasets. Advanced analytics software incorporates machine learning algorithms, statistical tools, and data mining techniques to extract valuable insights from complex data. The software segment is experiencing rapid growth due to the increasing demand for sophisticated analytics tools that can handle big data and provide real-time insights. Continuous advancements in software capabilities, such as enhanced data visualization and user-friendly interfaces, are driving the adoption of HPDA solutions across various industries.

The services segment encompasses consulting, implementation, and maintenance services essential for the effective deployment and operation of HP

LiDAR

Big Data Analytics in Retail Market Outlook

The global big data analytics in retail market size was valued at approximately USD 5.9 billion in 2023 and is projected to reach USD 25.8 billion by 2032, growing at an impressive CAGR of 17.8% during the forecast period. This substantial growth is primarily driven by the increasing inclination of retailers towards understanding consumer behavior, optimizing pricing strategies, and enhancing customer experience through data-driven insights.

The first significant growth factor in the market is the proliferation of e-commerce platforms and digital transformation. With the rising penetration of the internet and smartphones, e-commerce has seen an unprecedented surge, compelling traditional brick-and-mortar retailers to adopt big data analytics. This digital shift has provided retailers with vast amounts of data on customer preferences, purchasing behavior, and trends, which, when analyzed effectively, can lead to personalized marketing, improved inventory management, and ultimately, higher sales and customer satisfaction. Additionally, technological advancements such as AI, machine learning, and IoT integration are further propelling the adoption of big data analytics in the retail sector.

Another critical factor is the intensifying competition in the retail industry. With numerous players vying for customer attention and loyalty, retailers are increasingly turning to big data analytics to gain a competitive edge. By leveraging analytics, retailers can optimize their supply chains, manage inventories more efficiently, and predict future trends. This not only helps in reducing operational costs but also ensures that the right products are available at the right time, enhancing customer satisfaction and loyalty. Furthermore, the ability to harness data for predictive analytics enables retailers to anticipate market demands and adjust their strategies proactively.

The third growth driver is the increasing focus on customer-centric strategies. Modern consumers demand personalized shopping experiences, and retailers are using big data analytics to meet these expectations. By analyzing customer data, retailers can segment their audience, tailor their marketing efforts, and create personalized promotions and recommendations. This level of customization not only enhances the shopping experience but also boosts conversion rates and customer retention. Additionally, big data analytics aids in understanding customer feedback and preferences, enabling retailers to refine their products and services continuously.

From a regional perspective, North America is expected to dominate the big data analytics in the retail market, owing to the presence of major retail chains, advanced technological infrastructure, and high adoption rates of digital solutions. Europe is also anticipated to witness significant growth due to the rising e-commerce sector and increasing investment in digital transformation initiatives by retail companies. Meanwhile, the Asia Pacific region is projected to exhibit the highest CAGR during the forecast period, driven by rapid urbanization, a growing middle-class population, and expanding internet penetration, particularly in emerging economies like China and India.

Component Analysis

The big data analytics in the retail market can be segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share, mainly due to the increasing demand for advanced analytics tools and platforms that enable retailers to process and analyze vast amounts of data. Retailers are investing heavily in software solutions that provide real-time insights, predictive analytics, and customer behavior analysis, which are crucial for making informed business decisions. Advanced software tools powered by artificial intelligence and machine learning are also gaining traction, as they offer more accurate and actionable insights.

The hardware segment, while smaller compared to software, plays a vital role in the overall market. The need for robust IT infrastructure, including servers, storage devices, and networking equipment, is paramount for the effective implementation of big data analytics. Retailers are increasingly focusing on enhancing their IT capabilities to support large-scale data processing and storage. The advent of edge computing is also contributing to the growth of the hardware segment, as it allows for faster data processing and improved efficiency.

The services segment is another critical component, encompassing consulting, implement

Underpinning data for "Large-scale silicon quantum photonics implementing arbitrary two-qubit processing"

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

Big Data Storage Solutions Market Outlook

The Big Data Storage Solutions market is projected to witness substantial growth, with the market size valued at approximately $68 billion in 2023 and expected to reach around $150 billion by 2032, growing at a CAGR of 9.2% over the forecast period. This impressive growth trajectory is driven by the increasing volume of digital data generated across various sectors, necessitating advanced storage solutions. The proliferation of data from IoT devices, social media, and enterprise databases is a significant growth factor, as organizations are keen to harness this data for insights and competitive advantage, thus driving the demand for robust storage solutions that can ensure data integrity and accessibility.

One of the primary growth factors contributing to this market's expansion is the exponential increase in data generation from a wide array of sources, including the Internet of Things (IoT), social media platforms, and enterprise applications. As companies continue to realize the value of big data analytics in driving business decisions, there is a heightened demand for efficient storage solutions that not only accommodate vast volumes of data but also ensure its security and integrity. Cloud-based solutions are particularly gaining traction due to their scalability, cost-effectiveness, and ability to support remote work environments, which have become increasingly prevalent in the post-pandemic world. This shift towards cloud solutions is further supported by advancements in cloud technologies, such as edge computing and hybrid cloud setups, which offer flexibility and improved data processing capabilities.

Another significant growth driver is the increasing adoption of artificial intelligence and machine learning technologies across various industries. These technologies rely on large datasets to train and refine algorithms, necessitating efficient storage solutions capable of handling large-scale data operations. Industries such as healthcare, finance, and retail are leveraging AI and machine learning to optimize processes, enhance customer experiences, and make informed decisions, thereby propelling the demand for big data storage solutions. Additionally, regulatory compliance requirements concerning data storage and protection, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), are compelling organizations to invest in solutions that ensure data privacy and security, further boosting the market.

The growing digital transformation initiatives across sectors are also playing a pivotal role in market growth. Enterprises are increasingly adopting digital technologies to improve operational efficiencies and customer engagement, leading to an uptick in data generation and the subsequent need for advanced storage solutions. The rise in e-commerce platforms, online services, and digital payment systems has significantly contributed to data proliferation, requiring robust and scalable storage solutions that can manage large volumes of data while ensuring quick retrieval times and data durability. This trend is likely to continue as more businesses embrace digital transformation strategies to remain competitive in a rapidly evolving market landscape.

Regionally, North America currently dominates the Big Data Storage Solutions market due to its early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, driven by rapid digitalization, an increasing number of connected devices, and the expansion of IT infrastructure. Countries like China and India are at the forefront of this growth surge, supported by governmental initiatives promoting digital infrastructure development and an increasing focus on smart city projects. Europe also shows significant potential, with a strong emphasis on data privacy and security, driving demand for innovative storage solutions.

Component Analysis

The Big Data Storage Solutions market is divided into three primary components: hardware, software, and services, each playing a crucial role in the architecture and implementation of storage solutions. The hardware component comprises physical storage devices such as hard disks, solid-state drives, and network-attached storage (NAS) systems, which form the backbone of data storage infrastructures. With the increasing generation of unstructured data, there is a growing demand for high-capacity and high-performance storage devices. Innovations in storage technologies, such as the development of faster and more rel

The Data Processing and Hosting Services industry has transformed over the past decade, with the growth of cloud computing creating new markets. Demand surged in line with heightened demand from banks and a rising number of mobile connections across Europe. Many companies regard cloud computing as an innovative way of reducing their operating costs, which has led to the introduction of new services that make the sharing of data more efficient. Over the five years through 2025, revenue is expected to hike at a compound annual rate of 4.3% to €113.5 billion, including a 5.6% jump in 2025. Industry profit has been constrained by pricing pressures between companies and regions. Investments in new-generation data centres, especially in digital hubs like Frankfurt, London, and Paris, have consistently outpaced available supply, underlining the continent’s insatiable appetite for processing power. Meanwhile, 5G network roll-outs and heightened consumer expectations for real-time digital services have made agile hosting and robust cloud infrastructure imperative, pushing providers to invest in both core and edge data solutions. Robust growth has been fuelled by rapid digitalisation, widespread cloud adoption, and exploding demand from sectors such as e-commerce and streaming. Scaling cloud infrastructure, driven by both established giants, like Amazon Web Services (AWS), Microsoft Azure and Google Cloud and nimble local entrants, has allowed the industry to keep pace with unpredictable spikes in online activity and increasingly complex data needs. Rising investment in data centre capacity and the proliferation of high-availability hosting have significantly boosted operational efficiency and market competitiveness, with revenue growth closely tracking the boom in cloud and streaming services across the continent. Industry revenue is set to grow moving forward as European businesses incorporate data technology into their operations. Revenue is projected to boom, growing at a compound annual rate of 10.3% over the five years through 2030, to reach €185.4 billion. Growth is likely to be assisted by ongoing cloud adoption, accelerated 5G expansion, and soaring investor interest in hyperscale and sovereign data centres. Technical diversification seen in hybrid cloud solutions, edge computing deployments, and sovereign clouds, will create significant opportunities for incumbents and disruptors alike. Pricing pressures, intensified by global hyperscalers’ economies of scale and assertive licensing strategies, will pressurise profit, especially for smaller participants confronting rising capital expenditure and compliance costs.

According to Cognitive Market Research, the global GPU Database market size will be USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

This file contains the sources that were used to create the feature comparison in "Piveau: A Large-scale Open Data Management Platform based on Semantic Web Technologies".