This is analytical proofs and raw data for research article, “The Role of Protection Motivation in the Adoption of Personal Cloud Storage Service”. The original article aimed to investigate how the threat of data loss influences an individual’s intention to adopt cloud-enabled storage service as protection against data loss. This article includes analytical proofs, psychometric details of the measures and measurement items, analytic tables-related to the original article and raw data. Files included are as follows.

○ File 1 - Title: Details of prior studies (2009 to 2019) on the adoption of cloud-enabled storage at individual level - Description: This file presents a review of twenty-three studies (2009 to 2019) that focused on
the adoption of cloud-enabled storage service at the individual level. ○ File 2 - Title: Details of prior on applications of PMT in IS and IT areas - Description: This file presents a review of forty-seven studies (2009 to 2019) of PMT in IS/IT research areas. ○ File 3 - Title: Measurement items - Description: This file reports psychometric details of the measures and measurement items used in the original research article. ○ File 4 - Title: Sample characteristics - Description: This file reports the demographic characteristics of the respondents. ○ File 5 - Title: raw data for empirical analytics - Description: This file contains raw data for the original study: 392 samples were used for its final analysis. This data were collected through an online survey in South Korea.

This dataset is collected from standardized project information on multiple popular desktop cloud solutions. This dataset allows researchers and developers to compare the performance metrics of different desktop cloud products based on real data and evaluate the strengths and weaknesses. This dataset contains test results of different vendor products under different hardware configurations and network conditions. All data is strictly desensitized. We hope to promote the development of desktop cloud technology through data sharing, and we welcome community feedback to improve the dataset.

Cloud Computing Market Growth | Industry Analysis, Size & Forecast Report

Dataset updated: Jun 27, 2024

Dataset authored and provided by: Mordor Intelligence

License: https://www.mordorintelligence.com/privacy-policy

Time period covered: 2019 - 2029

Area covered: Global

Variables measured: CAGR, Market size, Market share analysis, Global trends, Industry forecast

Description: The Cloud Computing Market size is estimated at USD 0.68 trillion in 2024, and is expected to reach USD 1.44 trillion by 2029, growing at a CAGR of 16.40% during the forecast period (2024-2029).

Report Attribute

Quantitative Units: Revenue in USD Billion, Volumes in Units, Pricing in USD

Regions and Countries Covered:

Industry Segmentation Covered:

By Cloud Computing: IaaS, SaaS, PaaS

By End-User: IT and Telecom, BFSI, Retail and Consumer Goods, Manufacturing, Healthcare, Media and Entertainment

Market Players Covered: Amazon Web Services, Google LLC, Microsoft Corporation, Alibaba Cloud, and Salesforce

The cloud-based database security market is experiencing robust growth, projected to reach $6.5 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 14.7% from 2025 to 2033. This expansion is fueled by the increasing adoption of cloud computing, the rising volume of sensitive data stored in cloud databases, and the growing sophistication of cyber threats. Organizations across various sectors, including BFSI (Banking, Financial Services, and Insurance), retail, government, healthcare, IT and telecom, and manufacturing, are increasingly reliant on cloud-based databases, making robust security paramount. The market is segmented by deployment model (public, private, hybrid) and end-user vertical, reflecting the diverse application and security needs across industries. Drivers include stringent data privacy regulations like GDPR and CCPA, compelling organizations to invest heavily in secure cloud database solutions. Furthermore, the increasing prevalence of cloud-native applications and the shift towards serverless architectures are driving demand for specialized security solutions tailored to these environments. While the market is growing rapidly, challenges remain, including the complexity of managing security across multiple cloud environments and the ongoing evolution of cyber threats requiring continuous adaptation of security measures. The competitive landscape is characterized by a mix of established technology vendors and specialized cybersecurity firms. Major players like IBM, McAfee, Oracle, and Amazon Web Services are leveraging their existing infrastructure and expertise to offer comprehensive cloud database security solutions. Smaller, specialized vendors are focusing on niche areas like data loss prevention and encryption, creating a dynamic and innovative market. Geographical growth is expected to be widespread, with North America currently holding a significant market share due to early adoption and a robust technology infrastructure. However, regions like Asia-Pacific are projected to experience rapid growth in the coming years, driven by increasing digitalization and cloud adoption. The continued expansion of cloud computing, coupled with escalating cyber threats and increasingly stringent regulatory requirements, positions the cloud-based database security market for sustained and significant growth throughout the forecast period. Recent developments include: April 2023: Accenture and Google Cloud announced expanding their global partnership to assist businesses in better protecting critical assets and strengthening security against persistent cyber threats. Jointly, they are providing the technology and security expertise to the organization's trusted infrastructure required to build robust security programs and maintain confidence in their readiness., March 2023: IBM and Cohesity collaborated to address organizations' increased data security and resiliency in hybrid cloud environments. Integrating data protection, cyber resilience, and data management capabilities from both companies, IBM launched the IBM Storage Defender solution, including Cohesity's data protection as an integral part of the offering. IBM Storage Defender is designed to support AI and event monitoring across various storage platforms through a single glass pane to protect organizations' data layers from risks like ransomware, sabotage, and human error., March 2023 - Mastercard acquired cloud-based cybersecurity company Baffin Bay Networks to assist businesses in dealing with the increasingly challenging nature of cyberattacks. Baffin Bay Networks, based in Sweden, adds to Mastercard's multi-layered approach to cybersecurity and assists in stopping attacks while mitigating exposure to risk across the ecosystem. The acquisition further strengthens Mastercard's broader service offerings and value beyond the payment transaction.. Key drivers for this market are: Increasing Volumes of Data Being Generated from Information-Escalated Applications is Driving the Market Growth. Potential restraints include: Loss of Control over Data Location Hinders the Market. Notable trends are: Healthcare End-user Vertical is Expected to Hold Significant Market Share.

Database Private Cloud Market Outlook

The global database private cloud market size was valued at USD 12.5 billion in 2023 and is projected to reach USD 38.2 billion by 2032, growing at a CAGR of 13.2% during the forecast period. The rapid growth in cloud adoption, driven by the need for scalable and flexible database solutions, is a significant factor contributing to this market expansion. Companies are increasingly leveraging private cloud solutions to enhance data security, comply with regulatory requirements, and achieve efficient data management, which further fuels market growth.

One of the primary growth factors in the database private cloud market is the increasing demand for data security and compliance. Organizations across various sectors have stringent regulations regarding data protection, which necessitates the use of private cloud solutions that offer enhanced security features. Moreover, with the rise in cyber threats and data breaches, companies are prioritizing robust security measures, thereby driving the adoption of private cloud databases. The ability to maintain data sovereignty and control over sensitive information is a compelling reason for businesses to shift towards private cloud solutions.

The growing volume of data generated by enterprises is another critical factor propelling the market. In the era of big data, organizations are dealing with massive amounts of information that need to be stored, managed, and analyzed efficiently. Private cloud databases offer scalable storage solutions that can handle large datasets, making them an attractive option for businesses. Additionally, the integration of advanced technologies such as artificial intelligence and machine learning with private cloud databases enables organizations to derive actionable insights from their data, further driving market growth.

Moreover, the cost-efficiency and flexibility offered by private cloud solutions are significant drivers of market expansion. Unlike traditional on-premises databases, private cloud databases allow businesses to scale resources up or down based on demand, leading to cost savings and improved operational efficiency. The pay-as-you-go pricing model associated with private cloud services enables organizations to manage their IT budgets more effectively. This financial flexibility is especially beneficial for small and medium enterprises (SMEs), which often have limited resources but need robust database solutions to support their growth.

Regionally, North America holds a dominant position in the database private cloud market, attributed to the early adoption of cloud technologies and the presence of major cloud service providers in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing investments in IT infrastructure, coupled with the rising adoption of cloud computing by enterprises in countries like China and India, are driving the market in this region. The growing focus on digital transformation and the need for advanced data management solutions are also contributing to the market's regional growth.

Deployment Model Analysis

The database private cloud market can be segmented based on the deployment model into public cloud, private cloud, and hybrid cloud. Each deployment model offers unique advantages and is chosen based on specific organizational needs and preferences. The private cloud deployment model, in particular, is gaining traction due to its ability to provide dedicated resources, enhanced security, and compliance with regulatory standards. Organizations with sensitive data or those operating in highly regulated industries prefer private cloud solutions to ensure data privacy and control.

The public cloud deployment model, while offering scalability and cost-efficiency, may not always meet the stringent security and compliance requirements of certain organizations. However, it remains a popular choice for companies seeking to reduce IT infrastructure costs and leverage the benefits of cloud computing without significant upfront investments. The hybrid cloud model, which combines the best of both public and private clouds, is increasingly being adopted by organizations looking for a balanced approach to cloud deployment. This model allows businesses to utilize public cloud resources for non-sensitive operations while keeping critical data and applications on private cloud infrastructure.

In terms of market share, the private cloud deployment model is expected to lead, driven by the growing emphasis on data

We present a large-scale anomaly detection dataset collected from IBM Cloud's Console over approximately 4.5 months. This high-dimensional dataset captures telemetry data from multiple data centers, specifically designed to aid researchers in developing and benchmarking anomaly detection methods in large-scale cloud environments. It contains 39,365 entries, each representing a 5-minute interval, with 117,448 features/attributes, as interval_start is used as the index. The dataset includes detailed information on request counts, HTTP response codes, and various aggregated statistics. The dataset also includes labeled anomaly events identified through IBM's internal monitoring tools, providing a comprehensive resource for real-world anomaly detection research and evaluation.

File Descriptions

• location_downtime.csv - Details planned and unplanned downtimes for IBM Cloud data centers, including start and end times in ISO 8601 format.
• unpivoted_data.parquet - Contains raw telemetry data with 413 million+ rows, covering details like location, HTTP status codes, request types, and aggregated statistics (min, max, median response times).
• anomaly_windows.csv - Ground truth for anomalies, listing start and end times of recorded anomalies, categorized by source (Issue Tracker, Instant Messenger, Test Log).
• pivoted_data_all.parquet - Pivoted version of the telemetry dataset with 39,365 rows and 117,449 columns, including aggregated statistics across multiple metrics and intervals.
• demo/demo.[ipynb|html]: This demo file provides examples of how to access data in the Parquet files, available in Jupyter Notebook (.ipynb) and HTML (.html) formats, respectively.

Further details of the dataset can be found in Appendix B: Dataset Characteristics of the paper titled "Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset." Sample code for training anomaly detectors using this data is provided in this package.

When using the dataset, please cite it as follows:

@misc{islam2024anomaly,
   title={Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset}, 
   author={Mohammad Saiful Islam and Mohamed Sami Rakha and William Pourmajidi and Janakan Sivaloganathan and John Steinbacher and Andriy Miranskyy},
   year={2024},
   eprint={2411.09047},
   archivePrefix={arXiv},
   url={https://arxiv.org/abs/2411.09047}
}

In 2025, the survey underscores the continued dominance of data warehouses within cloud storage solutions. With ** percent of respondents utilizing them, it is evident that businesses value the extensive analytical capabilities and structured data organization that data warehouses provide. This trend signals a persistent need for centralized repositories for data storage and analytics to support strategic decision-making. DBaaS relational databases gain traction The survey reveals a rising preference for database-as-a-service (DBaaS) relational databases. This shift suggests that organizations are increasingly seeking the scalability, ease of maintenance, and reduced administrative overhead that DBaaS solutions provide. People are using DBaaS more because they want to make databases easier and spend more time on important things. Oracle leads this segment among the database management systems providers. PaaS adoption accelerates A key finding from the survey is the marked increase in the adoption of cloud providers' platform-as-a-service (PaaS) offerings. This trend speaks volumes about a broader transformation; companies are decisively moving towards cloud-based platforms for their data needs. PaaS provides convenient building blocks for application development and deployment, accelerating innovation and time-to-market.

According to Cognitive Market Research, the global cloud database and DBaaS marketsize will be USD 21.9 billion in 2024 and will increase at a compound annual growth rate (CAGR) of 21.6% from 2024 to 2031. Market Dynamics of Cloud Database and DBaaS Market Key Drivers for Cloud Database and DBaaS Market Mobile and IoT Adoption - The rise of mobile and IoT technologies fuels demand for cloud databases and DBaaS solutions. Data generation surges as mobile usage skyrockets and IoT devices flourish, necessitating scalable, accessible storage options. Cloud databases offer flexibility and scalability to accommodate these dynamic workloads while enabling seamless integration with mobile and IoT applications. The shift towards digital transformation initiatives also amplifies the need for agile, cloud-native database solutions to support modernization efforts across industries. Automated administration reduces operational complexity, which drives the cloud database and DBaaS market's expansion in the years ahead. Key Restraints for Cloud Database and DBaaS Market Compatibility issues with existing systems hinder the adoption of the cloud database and DBaaS in the industry. The market also faces significant difficulties related to data migration challenges that hinder adoption and scalability.. Introduction of the Cloud Database and DBaaS Market Cloud databases and Database-as-a-Service (DBaaS) offer scalable and managed storage solutions where data is hosted and accessed over the internet. Market drivers for these services include the imperative for scalability to accommodate growing data volumes, cost efficiencies achieved through a shift from capital to operational expenditure, enhanced accessibility enabling collaboration and innovation from any location, heightened demand for robust security features to address data privacy concerns, simplified management through automated administration, and elasticity to handle fluctuating workloads seamlessly. These drivers collectively address modern business needs for flexibility, cost-effectiveness, security, and performance. As organizations increasingly depend on data as a strategic asset, cloud databases, and DBaaS solutions provide the agility and efficiency required to meet evolving demands while leveraging the benefits of cloud computing infrastructure.

Enterprise Database Market Outlook

The enterprise database market size is projected to see significant growth over the coming years, with a valuation of USD 91.5 billion in 2023, and is expected to reach USD 171.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This growth is driven by the increasing demand for efficient data management solutions across various industries and the rise in digital transformation initiatives that require robust database systems. The growth factors include advancements in cloud computing, the growing need for real-time data analytics, and the integration of artificial intelligence and machine learning in data management.

One of the primary growth factors in the enterprise database market is the increasing adoption of cloud-based solutions. Organizations are rapidly moving towards cloud environments due to their scalability, cost-effectiveness, and flexibility. Cloud databases offer better accessibility and reduced infrastructure costs, making them an attractive option for businesses of all sizes. Additionally, with the proliferation of data generated from various sources such as social media, IoT devices, and online transactions, the need for scalable and efficient data storage solutions is more critical than ever. Cloud-based databases provide the requisite infrastructure to handle this data surge efficiently, further propelling market growth.

Another significant driver for the enterprise database market is the rise of big data analytics. As businesses strive to harness the power of data for insights and decision-making, the demand for robust database systems capable of handling large volumes of data has intensified. Enterprises are looking for databases that not only store data but also enable advanced analytics to derive actionable insights. This trend is particularly prevalent in industries like retail, healthcare, and BFSI, where data-driven decisions can lead to improved customer experiences, better risk management, and optimized operations. The integration of artificial intelligence and machine learning with enterprise databases is further enhancing their capabilities, allowing for predictive analytics and automating data processing tasks.

The growing emphasis on data security and compliance is also contributing to the expansion of the enterprise database market. With the increasing incidences of data breaches and stringent regulatory requirements, organizations are prioritizing secure database solutions that offer robust data protection measures. Databases with built-in security features such as encryption, access control, and regular auditing are in high demand. Furthermore, industry-specific compliance standards like GDPR in Europe and HIPAA in the US are driving businesses to invest in databases that ensure compliance and mitigate the risk of penalties, thus fueling market growth.

Regionally, North America is expected to dominate the enterprise database market due to the presence of major technology companies and early adoption of advanced technologies. The Asia Pacific region, however, is anticipated to witness the fastest growth rate during the forecast period, driven by rapid industrialization, the proliferation of SMEs, and increasing investments in digital infrastructure by countries like China, India, and Japan. The growing focus on smart cities and digital transformation initiatives in these countries is further boosting the demand for enterprise databases. Europe also holds a significant share of the market, with widespread adoption of cloud technologies and heightened focus on data privacy and security driving market expansion.

Industrial Databases play a crucial role in the enterprise database market, particularly as industries undergo digital transformation. These databases are designed to manage and process large volumes of industrial data generated from various sources such as manufacturing processes, supply chain operations, and IoT devices. The ability to handle real-time data analytics and provide actionable insights is essential for industries aiming to optimize operations and enhance productivity. As industries continue to adopt smart manufacturing practices, the demand for industrial databases that offer scalability, reliability, and integration with advanced technologies like AI and machine learning is on the rise. This trend is expected to contribute significantly to the growth of the enterprise database market, as businesses seek to leverage data for competitive advantage and operational efficiency.

The Cloud Database And DBaaS Market size was valued at USD 17.30 billion in 2023 and is projected to reach USD 49.79 billion by 2032, exhibiting a CAGR of 16.3 % during the forecasts period. This burgeoning market is primarily driven by the widespread adoption of cloud-based solutions, the escalating demand for data management and analytics, and the increasing need for cost-effective and scalable database management solutions. Cloud databases and Database as a Service (DBaaS) revolutionize data management by offering scalable and flexible solutions without the overhead of physical infrastructure. These services provide organizations with on-demand access to databases, reducing administrative burdens and allowing rapid deployment. Cloud databases leverage the elasticity of cloud computing to scale resources based on demand, ensuring optimal performance and cost-efficiency.

Relational Databases Software Market Outlook

The global relational databases software market size is projected to expand from an estimated $50 billion in 2023 to approximately $85 billion by 2032, growing at a compound annual growth rate (CAGR) of 6%. The primary drivers of this growth include the increasing reliance on data-driven decision-making processes, the surge in big data analytics, and the proliferation of cloud computing technologies. As organizations across various sectors accumulate vast amounts of data, the requirement for efficient data management and storage solutions becomes critical, further propelling the market's expansion.

One of the major growth factors driving the relational databases software market is the exponential increase in data generation from various sources, such as social media, IoT devices, and enterprise applications. With the advent of technologies like machine learning and artificial intelligence, the need to store, retrieve, and analyze massive datasets in real-time has become paramount. Relational databases software offers a structured way to manage data, providing quick access and robust querying capabilities, which are essential for leveraging data insights to drive business strategies.

Another significant growth factor is the widespread adoption of cloud computing. Cloud-based relational database solutions offer numerous advantages over traditional on-premises systems, such as scalability, flexibility, cost-effectiveness, and ease of maintenance. Many organizations are migrating their data management systems to the cloud to benefit from these advantages. Cloud vendors like Amazon Web Services, Microsoft Azure, and Google Cloud are continually enhancing their database offerings, adding advanced features to attract more customers, thereby fueling market growth.

The increasing trend toward digital transformation across various industries also contributes to the market's growth. As businesses strive to stay competitive in the digital age, they are investing heavily in modernizing their IT infrastructure, including their database management systems. Relational databases software enables organizations to handle complex transactions and support high-volume operations efficiently. This capability is particularly crucial for sectors such as banking and finance, healthcare, and retail, where data integrity and availability are critical for operations.

Regionally, North America currently holds the largest market share due to the early adoption of advanced technologies and the presence of major market players. Europe follows closely, with significant investments in digital transformation initiatives. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the rapid technological advancements, increasing internet penetration, and the growing number of small and medium enterprises in countries like China and India. Governments in these regions are also promoting digital initiatives, further boosting market growth.

Deployment Mode Analysis

The relational databases software market is segmented by deployment mode into on-premises and cloud-based solutions. The on-premises segment, traditionally the dominant mode, involves deploying the database software within an organization's own IT infrastructure. This deployment mode offers stringent control over data security and compliance, making it a preferred choice for industries with critical data privacy concerns, such as banking and government sectors. Despite a gradual shift towards cloud solutions, on-premises deployments continue to be relevant due to these security advantages.

However, the cloud-based deployment mode is experiencing rapid growth and is expected to dominate the market by 2032. Cloud databases offer unparalleled scalability and flexibility, allowing organizations to scale their database capacity up or down based on demand. This elasticity is particularly beneficial for businesses with variable workloads, such as e-commerce platforms during peak shopping seasons. Additionally, cloud databases significantly reduce the need for heavy upfront capital expenditure in IT infrastructure, as they operate on a subscription or pay-as-you-go model, which is financially appealing to many enterprises.

Another factor contributing to the rise of cloud-based databases is the continuous innovation by leading cloud service providers. Companies like Amazon Web Services, Google Cloud Platform, and Microsoft Azure are integrating advanced features such as a

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

The datasets in this collection are in Zarr format and hosted by various cloud-based computing providers.
See dataset documentation for links to example Jupyter notebooks. The notebooks show how to process the data in parallel within the cloud using Python-based tools.

Cloud-Based Sensory Substitution Dataset Bank Market Outlook

According to our latest research, the global market size for the Cloud-Based Sensory Substitution Dataset Bank Market reached USD 412.7 million in 2024, with a robust CAGR of 18.5% projected during the forecast period. By 2033, the market is anticipated to reach USD 1,964.2 million, reflecting the accelerating adoption of cloud-based solutions and the growing investment in sensory substitution technologies worldwide. The primary growth factor for this market is the increasing demand for advanced assistive technologies and the rapid evolution of cloud infrastructure, enabling global accessibility and scalability for sensory substitution datasets.

The growth of the Cloud-Based Sensory Substitution Dataset Bank Market is driven by several critical factors, foremost among them being the escalating prevalence of sensory impairments across the globe. With an aging population and rising incidences of vision and hearing loss, there is a pressing need for innovative assistive technologies that can bridge sensory gaps. Cloud-based dataset banks enable researchers and developers to access, train, and validate sensory substitution algorithms more efficiently, fostering rapid advancements in healthcare and assistive technology. The integration of artificial intelligence and machine learning with these datasets is further accelerating the development of more intuitive and effective sensory substitution devices, creating new opportunities for both established players and emerging startups in the market.

Another significant growth driver is the proliferation of cloud computing and the increasing digitalization of healthcare and research infrastructures. Cloud-based platforms offer unparalleled scalability, flexibility, and cost-effectiveness, allowing organizations to store, process, and share large volumes of sensory substitution data securely and efficiently. This has led to a surge in collaborative research initiatives, with academic institutions, hospitals, and technology companies leveraging shared datasets to drive innovation. The adoption of cloud-based sensory substitution dataset banks is also being propelled by favorable government policies and funding initiatives aimed at fostering accessibility and inclusivity for individuals with sensory disabilities, further stimulating market expansion.

The market is also benefiting from the growing emphasis on personalized medicine and user-centric assistive technologies. As the demand for customized sensory substitution solutions rises, cloud-based dataset banks are playing a pivotal role by providing diverse, high-quality data that supports the development of tailored devices and applications. This trend is particularly pronounced in the healthcare and education sectors, where sensory substitution technologies are being integrated into therapeutic interventions and learning environments. Additionally, the increasing awareness and acceptance of sensory substitution devices among end-users, coupled with ongoing advancements in hardware and software components, are contributing to sustained market growth.

From a regional perspective, North America currently leads the Cloud-Based Sensory Substitution Dataset Bank Market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of leading technology companies, robust healthcare infrastructure, and significant investments in research and development. Europe is witnessing substantial growth due to strong government support and a thriving academic research ecosystem, while Asia Pacific is emerging as a lucrative market driven by rapid digitalization, rising healthcare expenditure, and increasing awareness of sensory substitution technologies. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a slower pace, as infrastructure development and adoption rates continue to improve.

Component Analysis

<br

This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].

The logs are provided in JSON format with the following attributes in the first level:

• id: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.
• time: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.
• uid: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.
• uidType: Specifier for uid, which is either the user name or IP address for logged out users.
• type: The action carried out by the user, e.g., file_accessed.
• params: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.
• isLocalIP: Optional flag for event origin, which is either internal (true) or external (false).
• role: Optional user role: consulting, administration, management, sales, technical, or external.
• location: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.

In the following data sample, the first object depicts a successful user login (see type: login_successful) and the second object depicts a file access (see type: file_accessed) from a remote location:

{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}

{"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}

The data set was generated at the premises of Huemer Group, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called hBOX enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project Nextcloud with interfaces and functionalities tailored to the requirements of customers.

The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our github repo.

Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.

If you use the dataset, please cite the following publication:

[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. "A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing". 2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022), December 17-20, 2022, Osaka, Japan. IEEE. [PDF]

The US data processing and hosting services industry is navigating a dynamic environment marked by rising demands and revolutionary trends. As digitalization accelerates, data centers have evolved from simple infrastructure to essential strategic assets. These hubs now power services ranging from cloud computing to advanced data analytics. In 2025, the data processing and hosting service market includes giants like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Industry revenue currently sits at $383.8 billion, growing robustly at a CAGR of 9.2% over the past five years, including a 6.2% surge in 2025 alone. Alongside leading tech firms, smaller specialized providers cater to sectors like healthcare, financial services and government agencies with precision-placed data storage solutions. Emerging trends significantly influence the evolution of the US data processing and hosting services industry. Prominent among these is edge computing, a decentralized approach that locates data centers closer to end-user devices. Along with AI and modern data centers, these innovations aim to reduce latency and enhance application performance by minimizing resource usage in data transmission, thereby promoting broader adoption of cloud computing. Despite this transformative growth, the US data processing and hosting services industry faces significant hurdles, including a skill gap, escalating energy costs and escalating cybersecurity threats. This scarcity has heightened the focus on software automation, leading many facilities to implement AI solutions. Though offshoring trends lead to lost business for many participants, this activity is limited and the industry still benefits from strong demand, leading to rising profit. The industry is projected to grow at a CAGR of 2.4% to $431.4 billion by 2030. The future holds a mix of challenges and opportunities for the industry. Strategic investments in human capital and advanced technologies will distinguish industry leaders from laggards. Compliance with evolving data sovereignty and privacy regulations will determine local market competitiveness. Continuous innovation is expected to drive this progress, solidifying data centers' roles as pivotal components shaping the digital landscape ahead.

The global cloud database market, particularly the MongoDB segment, is experiencing robust growth, fueled by the increasing adoption of cloud-native applications and the demand for flexible, scalable database solutions. The market's Compound Annual Growth Rate (CAGR) is estimated to be in the high teens (let's assume 18% for illustrative purposes), indicating a significant expansion from its 2025 value (estimated at $25 billion, based on general market trends and competitor analysis). Key drivers include the rising preference for NoSQL databases due to their scalability and agility in handling unstructured data, the increasing popularity of DevOps and Agile methodologies, and the expansion of cloud computing infrastructure globally. Furthermore, the growing adoption of microservices architectures is creating a demand for databases that can seamlessly integrate with cloud-based platforms, propelling MongoDB's growth within this ecosystem. Companies like Microsoft, Amazon Web Services, and Google are heavily investing in their cloud platforms, further supporting the market expansion. This competitive landscape promotes innovation and ensures wider accessibility of MongoDB. However, challenges remain. Security concerns associated with cloud databases remain a significant restraint, necessitating robust security measures and compliance certifications to build consumer trust. Integration complexities with legacy systems and the need for skilled professionals to manage cloud databases present hurdles for widespread adoption. Nevertheless, the significant advantages offered by MongoDB, especially in areas such as real-time analytics and data flexibility, are expected to offset these restraints and fuel continued market expansion throughout the forecast period (2025-2033). Regional variations exist, with North America and Europe expected to maintain dominant market shares due to early adoption and mature cloud infrastructure. Emerging markets in Asia-Pacific are demonstrating rapid growth potential, driven by increasing digitalization and government initiatives.

In 2024, enterprise spending on cloud infrastructure services amounted to *** billion U.S. dollars, a growth of ** billion U.S. dollars compared to the previous year. The growing market for cloud infrastructure services is driven by organizations' demand for modern networking, storage, and databases solutions. Increased spending on cloud services, mainly on platform as a service The platform as a service (PaaS) segment, which includes analytics, database, and internet of things (IoT) has the highest growth rate within the cloud infrastructure services market. The managed private cloud services share declined in comparison. Infrastructure as a service (IaaS) remained relatively steady, with companies like Amazon Web Services and Microsoft dominating the market. However, software as a service (SaaS) is not included, which itself continues to experience growth in end-user spending worldwide. Data center spending declined in 2020 Enterprise spending on data center hardware and software, on the other hand, began to slightly decline after several years of steady growth. Data center hardware and software encompasses spending on servers, networking, storage, and security software. Because data centers store proprietary or sensitive data, sites are secured by specific software. This includes splitting networks into security zones, for example. Other methods for ensuring security are using tools to scan applications and code before deployment to spot malware or vulnerabilities.

The global cloud database MySQL market is experiencing robust growth, driven by increasing adoption of cloud-based solutions across various industries. The market's expansion is fueled by several factors, including the scalability and cost-effectiveness of cloud databases, the rising demand for data-driven decision-making, and the growing need for robust and secure data management solutions. While precise figures are unavailable from the provided information, a reasonable estimation, based on the prevalence of MySQL in the broader cloud database market and considering growth trends in similar sectors, suggests a market size of approximately $15 billion in 2025. Assuming a conservative Compound Annual Growth Rate (CAGR) of 18% (reflecting moderate growth within a maturing but still expanding market), the market is projected to reach approximately $35 billion by 2033. This growth trajectory is influenced by the ongoing digital transformation across enterprises, the burgeoning adoption of DevOps methodologies, and the expanding capabilities of MySQL itself, particularly in areas like performance optimization and enhanced security features. This growth is not uniform across all regions. North America and Europe are expected to maintain significant market shares due to early adoption and mature technological infrastructure. However, faster growth is anticipated in Asia-Pacific and other emerging markets, driven by increasing digitalization and infrastructure investments. The competitive landscape is highly dynamic, with major players like Microsoft, Amazon Web Services, Google, and Oracle vying for market dominance. Smaller, specialized cloud providers also play a crucial role, catering to niche market requirements and fostering innovation. However, challenges remain, such as data security concerns, vendor lock-in, and the complexity associated with migrating existing on-premise databases to the cloud. Overcoming these hurdles will be crucial for sustained market growth in the years to come.

Building more in-house datacenters to backup explosively growing scientific datasets is neither cost-effective nor in line with government green initiative. Cloud computing is emerging as a viable platform for data storage, collaboration and disaster recovery. We are going to develop a suite of "backup-to-cloud" tools that allows user to backup scientific datasets and applications into the cloud, and use cloud storage as a distribution platform. Our tool is optimized under technical and economical constraints posed by common cloud storage. We use both public and private cloud platforms to conduct feasibility study from performance, security, scalability and cost perspectives.