Dataset Overview

This dataset contains augmented software architecture diagrams specifically focused on cloud service components from major cloud providers (AWS, Azure, GCP). The dataset was developed as part of a FIAP POS Tech Hackathon project for training computer vision models to detect and classify cloud architecture components in technical diagrams.

Dataset Contents

• 8k+ augmented images (PNG format) with corresponding Pascal VOC XML annotations
• Cloud service components from AWS, Azure, and GCP platforms
• 87 unique cloud service types including:
  • AWS services: EC2, S3, RDS, DynamoDB, Lambda, API Gateway, CloudFront, etc.
  • Azure services: Databricks, Storage, Managed Database, Load Balancer, etc.
  • GCP services: (various Google Cloud Platform components)

Data Augmentation Process

The dataset was created using a sophisticated augmentation pipeline that applies multiple transformations while preserving bounding box annotations:

Augmentation Techniques Applied:

• Brightness and contrast adjustments (±20%)
• Rotation (±15 degrees)
• Gaussian blur (sigma: 0.0-2.0)
• Gaussian noise (variance: 10-50)
• Elastic transformations (alpha: 1, sigma: 50)
• Grid distortions (num_steps: 5, distort_limit: 0.3)
• Optical distortions (distort_limit: 0.1, shift_limit: 0.1)

Quality Control:

• Relevance filtering: Only "High" relevance components were selected for augmentation
• Bounding box preservation: All transformations maintain accurate object detection annotations
• Format compatibility: Pascal VOC XML format for seamless integration with popular ML frameworks

Technical Specifications

• Image Format: PNG
• Annotation Format: Pascal VOC XML
• Augmentation Ratio: 10 variations per original image
• Image Categories: Cloud architecture components and services
• Bounding Box Accuracy: Preserved through geometric transformations

Use Cases

This dataset is ideal for: - Object detection in software architecture diagrams - Cloud service recognition in technical documentation - Automated diagram analysis tools - Computer vision research in technical domain - Training custom models for architecture diagram parsing

Dataset Structure

dataset_augmented/
├── image_xpto.png   # Augmented PNG images
├── image_xpto.xml   # Pascal VOC XML files

Machine Learning Applications

Perfect for training: - YOLO object detection models - Faster R-CNN for precise component detection - Custom CNN architectures for diagram analysis - Multi-class classification models

Quality Assurance

• All images maintain visual quality after augmentation
• Bounding boxes are accurately transformed with image modifications
• Consistent naming convention for easy batch processing
• Validated XML structure for error-free training

High level architecture diagram.

Open Architecture Security Data Platform Market Outlook

According to our latest research, the Open Architecture Security Data Platform market size reached USD 8.9 billion in 2024 at a robust growth rate, underpinned by the increasing demand for interoperable and scalable security solutions. The market is projected to expand at a CAGR of 14.2% from 2025 to 2033, reaching a forecasted value of USD 28.3 billion by 2033. This growth trajectory is largely driven by the rising sophistication of cyber threats and a global shift towards integrated, flexible security frameworks that can seamlessly accommodate evolving enterprise needs.

The primary growth factors fueling the Open Architecture Security Data Platform market include the escalating complexity and frequency of cyberattacks across industries, compelling organizations to adopt advanced, open, and modular security infrastructures. Traditional monolithic security systems are increasingly being replaced by open architecture platforms that enable seamless integration with diverse security tools and data sources. This transition is critical for organizations aiming to achieve real-time threat detection, rapid incident response, and comprehensive compliance management. The proliferation of cloud-based applications, IoT devices, and remote workforces has further intensified the need for platforms capable of ingesting, normalizing, and analyzing heterogeneous security data at scale.

Another significant driver is the global regulatory landscape, which is evolving rapidly to address the growing risks associated with digital transformation. Stringent data protection regulations such as GDPR, CCPA, and other regional mandates are pushing enterprises to adopt security data platforms that offer robust compliance management and auditable workflows. Open architecture solutions are particularly well-suited to this environment, as they provide the flexibility to integrate with compliance tools, automate reporting, and ensure data integrity across disparate environments. This regulatory pressure is especially pronounced in sectors like BFSI, healthcare, and government, where data sensitivity is paramount.

The increasing adoption of advanced analytics, artificial intelligence, and machine learning within security operations is another catalyst for market expansion. Open architecture security data platforms are uniquely positioned to leverage these technologies, enabling organizations to derive actionable insights from vast volumes of security telemetry. By supporting interoperability with a wide array of analytics engines and data lakes, these platforms empower security teams to proactively identify threats, streamline incident response, and reduce mean time to detect and remediate breaches. As enterprises continue to prioritize security automation and intelligence-driven defense strategies, the demand for flexible, open, and extensible security data platforms will only intensify.

From a regional perspective, North America currently dominates the Open Architecture Security Data Platform market due to its advanced cybersecurity ecosystem, high concentration of large enterprises, and early adoption of open security frameworks. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, expanding regulatory requirements, and increasing investments in cybersecurity infrastructure. Europe also represents a significant market, driven by strict data protection laws and a strong focus on privacy and compliance. Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions ramp up their cybersecurity capabilities to counter rising threat levels and meet regulatory mandates.

Component Analysis

The Component segment of the Open Architecture Security Data Platform market is divided into software, hardware, and services, each playing a pivotal role in delivering comprehensive security solutions. Software remains the largest contributor, comprising advanced security analytics engines, data integration tools, and orchestration platforms that form the backbone of open architecture security frameworks. These software solutions are designed to provide modularity, interoperability, and scalability, allowing organizations to seamlessly integrate third-party tools and adapt to evolving security requirements. As cyber threats grow more sophisticated, the demand for feature-rich, customizable software platforms

This paper introduces a novel reasoning methodology, in combination with appropriate models and measurements (data) to perform accurately and expeditiously expert troubleshooting for complex military and industrial processes. This automated troubleshooting tool is designed to support the maintainer/ repairman by identifying and locating faulty system components. The enabling technologies build upon a Model Based Reasoning paradigm and a Dynamic Case Based Reasoning method acting as the intelligent database. A case study employs a helicopter Intermediate Gearbox as the application domain to illustrate the efficacy of the approach.

Architecture Data

## Overview

Architecture Data is a dataset for object detection tasks - it contains Architecture annotations for 2,015 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This database is a smaller version of the larger database on Modern Architecture, where each of the over 100.000k image files was preprocessed to be of size 128x128px. It is separated into two main categories: Public Buildings (Banks, Hospitals) and Private Appartments. Each image is a .jpg file. All the information about each picture is stored within its title.

Additionally for each building there are multiple different images (Example) of different aspects of the building. These include * Interior Photography * Exterior Photography * Architectual Drawings * ...

Inspiration

The datasets lends itself for trying out various Image Recognition and Classification algorithms, i.e. classifying between appartments and public housings.

Disclaimer: All data is to be used only for private/research purposes.

Open Architecture Security Data Platform Market Outlook

According to our latest research, the Open Architecture Security Data Platform market size reached USD 6.9 billion in 2024, reflecting a robust surge in adoption across critical industries. The market is poised to expand at a CAGR of 14.2% during the forecast period, with the global market projected to attain USD 20.7 billion by 2033. This remarkable growth is primarily driven by escalating cybersecurity threats, greater regulatory compliance requirements, and the need for scalable, interoperable security solutions that can adapt to rapidly evolving IT environments.

The surge in cyberattacks and sophisticated threat vectors has been a primary catalyst for the rapid growth of the Open Architecture Security Data Platform market. As organizations increasingly digitize their operations and migrate to hybrid and multi-cloud environments, the attack surface has expanded considerably. Open architecture platforms provide the flexibility to integrate disparate security tools and data sources, enabling real-time threat detection, streamlined incident response, and comprehensive analytics. This capability is vital as modern enterprises require holistic visibility and control over their security posture, making open architecture solutions an indispensable asset in combating advanced persistent threats and zero-day vulnerabilities.

Another significant growth driver is the intensifying regulatory landscape across industries such as BFSI, healthcare, and government. Regulations like GDPR, HIPAA, and CCPA are compelling organizations to implement robust data security and compliance management frameworks. Open Architecture Security Data Platforms facilitate seamless data aggregation, audit trails, and reporting mechanisms, simplifying compliance processes and mitigating the risk of hefty fines. Furthermore, as businesses expand globally, the need for scalable security solutions that can be tailored to regional compliance requirements becomes increasingly critical, further boosting demand for open, interoperable security data platforms.

The proliferation of cloud computing and digital transformation initiatives has also played a pivotal role in accelerating market growth. Enterprises are seeking security platforms that can operate seamlessly across on-premises, private cloud, and public cloud environments. Open architecture solutions offer the agility to integrate with both legacy and next-generation security tools, ensuring consistent policy enforcement and threat intelligence sharing across heterogeneous infrastructures. This adaptability is particularly attractive to organizations undergoing mergers, acquisitions, or rapid expansion, as it allows for the consolidation and rationalization of security operations without disrupting business continuity.

From a regional perspective, North America continues to dominate the Open Architecture Security Data Platform market, owing to its mature cybersecurity ecosystem, high adoption of advanced technologies, and stringent regulatory frameworks. However, Asia Pacific is witnessing the fastest growth, fueled by rapid digitization, increasing cyber threats, and government-led cybersecurity initiatives. Europe follows closely, driven by its strong focus on data privacy and compliance. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions invest in modernizing their security infrastructure to address evolving threat landscapes and regulatory demands.

Component Analysis

The Open Architecture Security Data Platform market is segmented by component into Software, Hardware, and Services, each playing a crucial role in shaping the overall market landscape. The software segment commands the largest share, driven by the growing need for advanced security analytics, threat intelligence, and compliance management tools that can integrate seamlessly with existing IT infrastructure. Organizations are increasingly investing in modul

Software-Architecture I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl. I have included following topics: Architectural Frameworks Architectural Patterns for Reliability Architectural Patterns for Scalability Architectural Patterns Architectural Quality Attributes Architectural Testing Architectural Views Architectural Decision-Making Advanced Research Cloud-Based Architectures Component-Based… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Software-Architecture.

Satellite design encompasses a multitude of steps from concept to flight. Mission specification to flight can take several years, depending on the scope, requirements and budget of the mission. The process also requires a wide range of design and management tools, with limited consistency data interchange capability, and a lack of coherency. Detailing the relationships between the satellite configuration, inventory control systems, life cycle management, design, analysis and test data is difficult at best. No tool exists that meets these needs for the general satellite design, system engineering and integration process. Sci_Zone is proposing our innovative Satellite Design Automation architecture SatBuilder Designer, in conjunction with the OpenSAT open database architecture to meet this need. OpenSAT seamlessly integrates existing detail design tools with SatBuilder Designer, as well as databases tracking requirements, components and inventory, with the final configuration of the satellite. SatBuilder Designer, an AI based toolset, provides for rapid design via design wizards and integration to existing design tools; it provides coherency between a range of applications and data sets. OpenSAT stores and distributes supporting satellite design, configuration, mission and test data from a centralized database server and can distribute the data across multiple platforms and via the internet.

This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element (CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.

Success.ai’s B2B Leads Data for Architecture, Planning, and Design Experts in Europe provides verified access to professionals shaping the built environment. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, verified contact details, and firmographic data for architects, urban planners, interior designers, and more. Whether your objective is to market products, recruit talent, or explore industry trends, Success.ai ensures your data is accurate, enriched, and continuously updated.

Why Choose Success.ai’s B2B Leads Data for Architecture, Planning & Design Experts? Comprehensive Professional Profiles

Access verified profiles of architects, urban planners, landscape designers, and project managers in Europe. AI-driven validation ensures 99% accuracy, optimizing outreach efforts and minimizing bounce rates. Focused Coverage Across Europe

Includes professionals from major architectural firms, design studios, and urban planning organizations. Covers key markets like the UK, Germany, France, Italy, and Scandinavia. Continuously Updated Dataset

Real-time updates ensure your data remains relevant, reflecting changes in roles, organizations, and professional achievements. Tailored for Architectural Insights

Enriched profiles include professional histories, areas of specialization, certifications, and firmographic details for a deeper understanding of your audience. Data Highlights: 700M+ Verified LinkedIn Profiles: Gain access to a global network of architecture and design professionals. 170M+ Enriched Profiles: Includes work emails, phone numbers, and decision-maker insights for targeted communication. Industry-Specific Segmentation: Target professionals in architecture, urban planning, interior design, and landscape architecture with precision filters. Region-Specific Data: Focus on European design hubs, including London, Paris, Berlin, and Copenhagen. Key Features of the Dataset: Architecture and Design Professional Profiles

Identify and connect with architects, project managers, urban planners, and design experts leading major projects. Engage with professionals driving trends in sustainable building, smart cities, and innovative design. Detailed Firmographic Data

Leverage insights into company sizes, project scales, geographic reach, and specialization areas. Customize your approach to align with the needs of architectural firms, urban planning agencies, or independent designers. Advanced Filters for Precision Targeting

Refine searches by region, design specialty (residential, commercial, landscape), or years of experience. Tailor campaigns to address industry challenges such as sustainability, urbanization, or heritage conservation. AI-Driven Enrichment

Enhanced datasets provide actionable details for personalized campaigns, highlighting certifications, awards, and key projects. Strategic Use Cases: Marketing Products and Services

Promote building materials, design software, or urban planning tools to architects, designers, and planners. Engage with professionals managing construction, sustainability initiatives, or smart city developments. Collaboration and Partnerships

Identify architects, urban planners, and design studios for collaborative projects, competitions, or design innovations. Build partnerships with firms focused on sustainability, green architecture, and cutting-edge urban design. Recruitment and Talent Acquisition

Target HR professionals and architectural firms seeking designers, project managers, and urban planning specialists. Simplify hiring for roles requiring creative and technical expertise. Market Research and Trend Analysis

Analyze shifts in urban development, design trends, and sustainable construction practices across Europe. Use insights to refine product development and marketing strategies tailored to the architectural sector. Why Choose Success.ai? Best Price Guarantee

Access industry-leading B2B Leads Data at unmatched pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified architectural data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted efforts and maximize engagement with architecture and design professionals. Customizable Solutions

Tailor datasets to specific architectural segments, regions, or roles to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified profiles of architectural and design professionals to refine targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of qualified professionals, scaling your outreach efficiently. Success.ai’s B2B Leads Data for Architecture, Planning & Design Experts positions you to connect with the creative minds shaping Europe’s...

as security blocks

This application will be used to collect attributes relating to applications currently being used within the Social Security Administration (SSA).

The documents contained in this dataset reflect NASA's comprehensive IT policy in compliance with Federal Government laws and regulations.

Directory of NYC High School programs

Describes the architecture of ByzGen's blockchain backed data platform

City of San José is committed to an open, honest, and effective government and strives to consistently meet the community’s expectations for excellent services in a positive and timely manner, and in full view of the public. With the advancement in information technologies and the increasing ability to share data more easily across multiple platforms and online, appropriate leveraging of these tools to make information accessible and usable by the public can help improve public service delivery and fuel entrepreneurship and innovation. The Open Data Portal serves as means to implement the City’s Open Data Policy and Open Data Community Architecture which is intended to help the City better utilize its data. Open Data is an important component of this commitment; through making its data publicly available and easily accessible, the City will empower the community to engage with government on a new level and stimulate new ideas, new services, and new economic opportunities. In addition, Open Data will provide a new platform to increase the sharing of information among City departments, improving the City’s ability to deliver services to the community efficiently and effectively. To help achieve these outcomes. To get started please go to the OpenGov Open Data Training page.

Enterprise design pattern documents that provide references to the use of enterprise capabilities that will enable the VA to access and exchange data securely through the use of Enterprise Shared Services (ESS) and open standards.

Digital Bridge Open Architecture Market Outlook

According to our latest research, the global Digital Bridge Open Architecture market size reached USD 3.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.7% projected through the forecast period. By 2033, the market is expected to reach USD 9.5 billion, driven by the increasing demand for flexible, scalable, and interoperable digital infrastructure solutions worldwide. The growth of this market is primarily fueled by the rapid digital transformation initiatives across industries, the proliferation of cloud-native applications, and the need for seamless integration between legacy and next-generation systems.

One of the key growth factors propelling the Digital Bridge Open Architecture market is the accelerating adoption of open standards and modular architectures by enterprises and telecom operators. Organizations are increasingly recognizing the value of vendor-agnostic solutions that promote interoperability, reduce vendor lock-in, and enable the seamless integration of disparate hardware and software components. As digital ecosystems become more complex, the requirement for open architecture frameworks that can bridge traditional and modern IT environments is intensifying. This trend is particularly evident in sectors such as telecommunications and data centers, where agility, scalability, and cost optimization are paramount. The ongoing shift towards open-source technologies and collaborative innovation is further amplifying the relevance of Digital Bridge Open Architecture in meeting the evolving needs of digital businesses.

Another significant driver of market expansion is the exponential growth of data traffic and the rise of distributed computing environments. With the proliferation of IoT devices, edge computing, and 5G networks, enterprises are facing unprecedented challenges in managing, processing, and securing vast volumes of data across geographically dispersed locations. Digital Bridge Open Architecture enables organizations to build unified, scalable, and resilient digital infrastructures that can seamlessly connect cloud, edge, and on-premises resources. This capability is crucial for supporting emerging use cases such as real-time analytics, AI-driven automation, and mission-critical applications that demand high availability and low latency. Furthermore, the flexibility offered by open architecture solutions allows enterprises to future-proof their IT investments and adapt to rapidly changing technological landscapes.

Additionally, the increasing emphasis on digital sovereignty, data privacy, and regulatory compliance is shaping the adoption of Digital Bridge Open Architecture solutions. Governments and enterprises are seeking architectures that provide greater control over data flows, enable secure cross-border data exchanges, and facilitate compliance with evolving regulatory frameworks. Open architecture models empower organizations to implement customizable security policies, leverage best-in-class security tools, and maintain visibility across complex hybrid and multi-cloud environments. This is particularly relevant in highly regulated sectors such as finance, healthcare, and government, where data governance and risk management are top priorities. As a result, the demand for secure and compliant open architecture frameworks is expected to surge in the coming years.

From a regional perspective, North America currently leads the Digital Bridge Open Architecture market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the presence of major technology vendors, early adoption of advanced digital infrastructure, and significant investments in cloud, AI, and 5G technologies. Asia Pacific is emerging as the fastest-growing market, driven by rapid digitalization, expanding telecom networks, and government initiatives to modernize IT infrastructure. Europe is also witnessing steady growth, supported by strong regulatory frameworks and a focus on digital sovereignty. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, fueled by increasing investments in telecom and data center modernization. These regional dynamics underscore the global momentum behind Digital Bridge Open Architecture adoption.

Component Analysis

The Digital Bridge Open Architecture market is segmented by component into hardware, software, and services, each playing a pivotal role

This is a microservices dataset. For an exclusive explanation, please take a look at the paper and at the online appendix: https://github.com/darioamorosodaragona-tuni/Microservices-DatasetIn particular, this file contains all the projects labeled as:- Is it a microservices?: Yes | Uknown- Archived: Yes | NoCopyright:Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). MSR ’24, April 15–16, 2024, Lisbon, Portugal © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0587-8/24/04 https://doi.org/10.1145/3643991.3644890