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...

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.

In a network model, all individual nodes belonging to a particular node type share the respective attributes, and likewise all edges belonging to the same edge type share attributes of that type.

This dataset includes 100.000 images of modern architecture. 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.

A dataset composed of 8 software engineering projects collected (and refined) from the Software Engineering – Laboratory of Advanced Programming” course at Sapienza University of Rome for master students in Engineering in Computer Science.

The dataset comprises folders for each project. Each folder contains:

• input.txt file with the description of the system and the list of user stories,
• DataMetrics.json with the performance characteristics of the project. An example of DataMetrics format is provided below.
  

  [
   {
    "set_id": 1,
    "set_name": "auth client",
    "user_stories": [1, 2, 3, 4],
    "links": [2, 3, 5],
    "db": "true"
   },
   ...
  ]

  The json is represented by an array of dictionaries, each relative to a set, characterized by a set_id and a set_name, grouping user stories (identified by their numerical identifier in user_stories). Each dictionary also contains links and db keys to indicate other sets that have a related context and the need for a backend service to store or retrieve data, respectively. From an architectural point of view, user stories that belong to linked sets can be fulfilled by the same container and the sets of user stories that are required to store or retrieve data must be fulfilled by a container hosting a database microservice.

• Student_Doc.md with the student (entire) documentation (solution) of the project,
• source code of the developed project.

The dataset is under continuous updating. Each academic year it will be enriched with new projects.

If you want to contribute, please contact us.

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

Steam Dataset 2025: Multi-Modal Gaming Analytics Platform

The first multi-modal Steam dataset with semantic search capabilities. 239,664 applications collected from official Steam Web APIs with PostgreSQL database architecture, vector embeddings for content discovery, and comprehensive review analytics.

Made by a lifelong gamer for the gamer in all of us. Enjoy!🎮

GitHub Repository https://github.com/vintagedon/steam-dataset-2025

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28514182%2F4b7eb73ac0f2c3cc9f0d57f37321b38f%2FScreenshot%202025-10-18%20180450.png?generation=1760825194507387&alt=media" alt=""> 1024-dimensional game embeddings projected to 2D via UMAP reveal natural genre clustering in semantic space

What Makes This Different

Unlike traditional flat-file Steam datasets, this is built as an analytically-native database optimized for advanced data science workflows:

☑️ Semantic Search Ready - 1024-dimensional BGE-M3 embeddings enable content-based game discovery beyond keyword matching

☑️ Multi-Modal Architecture - PostgreSQL + JSONB + pgvector in unified database structure

☑️ Production Scale - 239K applications vs typical 6K-27K in existing datasets

☑️ Complete Review Corpus - 1,048,148 user reviews with sentiment and metadata

☑️ 28-Year Coverage - Platform evolution from 1997-2025

☑️ Publisher Networks - Developer and publisher relationship data for graph analysis

☑️ Complete Methodology & Infrastructure - Full work logs document every technical decision and challenge encountered, while my API collection scripts, database schemas, and processing pipelines enable you to update the dataset, fork it for customized analysis, learn from real-world data engineering workflows, or critique and improve the methodology

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28514182%2F649e9f7f46c6ce213101d0948c89e8ac%2F4_price_distribution_by_top_10_genres.png?generation=1760824835918620&alt=media" alt=""> Market segmentation and pricing strategy analysis across top 10 genres

What's Included

Core Data (CSV Exports): - 239,664 Steam applications with complete metadata - 1,048,148 user reviews with scores and statistics - 13 normalized relational tables for pandas/SQL workflows - Genre classifications, pricing history, platform support - Hardware requirements (min/recommended specs) - Developer and publisher portfolios

Advanced Features (PostgreSQL): - Full database dump with optimized indexes - JSONB storage preserving complete API responses - Materialized columns for sub-second query performance - Vector embeddings table (pgvector-ready)

Documentation: - Complete data dictionary with field specifications - Database schema documentation - Collection methodology and validation reports

Example Analysis: Published Notebooks (v1.0)

Three comprehensive analysis notebooks demonstrate dataset capabilities. All notebooks render directly on GitHub with full visualizations and output:

📊 Platform Evolution & Market Landscape

View on GitHub | PDF Export
28 years of Steam's growth, genre evolution, and pricing strategies.

🔍 Semantic Game Discovery

View on GitHub | PDF Export
Content-based recommendations using vector embeddings across genre boundaries.

🎯 The Semantic Fingerprint

View on GitHub | PDF Export
Genre prediction from game descriptions - demonstrates text analysis capabilities.

Notebooks render with full output on GitHub. Kaggle-native versions planned for v1.1 release. CSV data exports included in dataset for immediate analysis.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28514182%2F4079e43559d0068af00a48e2c31f0f1d%2FScreenshot%202025-10-18%20180214.png?generation=1760824950649726&alt=media" alt=""> *Steam platfor...

This dataset is about books. It has 1 row and is filtered where the book is Get Your Hands Dirty on Clean Architecture : a Hands-On Guide to Creating Clean Web Applications with Code Examples in Java. It features 7 columns including author, publication date, language, and book publisher.

Contains participant responses for the meaningful experiment conducted as part of the thesis entitled 'Isolating and Predicting Risks in Architectural Design' by Andrew Leigh.An on-line experiment was used to test hypothesis H5 (if Design Rule, Resource, and Use Case Containers are meaningful to architects and developers, they should help to better manage mitigations used in practice) and answer RQ2 in order to assess whether any of the three risk container types satisfy the final meaningful criterion M3 (help to better manage mitigations used in practice). The on-line experiment is based around an architecture review scenario for a toy architecture because performing an architecture review is an example of a mitigation that is commonly used in practice. The experiment provides qualitative and quantitative evidence about whether presenting an architecture as a series of container diagrams helps practitioners to locate error inducing flaws and impact changes during the review. Participants are randomly assigned to one of four different groups (control, Design Rule Container, Resource Container, and Use Case Container) and each group must undertake the review exercise using a series of container diagrams or a single control diagram according to their assigned group. Participants also answer an open question to capture their attitudes towards completing the exercise. Section 7.1 of the thesis describes the experiment designed to answer RQ2 in more detail. This csv file contains the participant responses collected during the experiment.

THE MISSION

The story behind the dataset is how to apply LSTM architecture to understand and apply multiple variables together to contribute more accuracy towards forecasting.

THE CONTENT

Air Pollution Forecasting The Air Quality dataset.

This is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China.

The data includes the date-time, the pollution called PM2.5 concentration, and the weather information including dew point, temperature, pressure, wind direction, wind speed and the cumulative number of hours of snow and rain. The complete feature list in the raw data is as follows:

No: row number year: year of data in this row month: month of data in this row day: day of data in this row hour: hour of data in this row pm2.5: PM2.5 concentration DEWP: Dew Point TEMP: Temperature PRES: Pressure cbwd: Combined wind direction Iws: Cumulated wind speed Is: Cumulated hours of snow Ir: Cumulated hours of rain We can use this data and frame a forecasting problem where, given the weather conditions and pollution for prior hours, we forecast the pollution at the next hour.

PokeDAO Mew-1A Training Dataset (Layered Architecture)

🧬 Project Mew-1A: The world's first AI training dataset specifically for Pokemon TCG pricing analysis, extracted from a production-ready layered database architecture.

  Dataset Description

This dataset contains 10,000 high-quality training examples extracted from PokeDAO's layered database containing 116,744 market listings across multiple marketplaces.

  Layered Architecture

The data is sourced from a… See the full description on the dataset page: https://huggingface.co/datasets/ChicoPanama/pokedao-mew1a-training-data-layered.

Open Digital Architecture Components Market Outlook

According to our latest research, the Open Digital Architecture Components market size reached USD 7.2 billion in 2024, reflecting robust momentum across digital transformation initiatives globally. The market is projected to grow at a CAGR of 14.7% from 2025 to 2033, reaching an estimated USD 23.9 billion by 2033. This impressive growth trajectory is fueled by the increasing adoption of open digital frameworks, which enable organizations to modernize legacy systems, enhance interoperability, and accelerate innovation in response to evolving business demands.

One of the primary growth drivers in the Open Digital Architecture Components market is the accelerating pace of digital transformation across industries such as telecommunications, BFSI, and healthcare. Organizations are under pressure to deliver seamless digital experiences, optimize operational efficiency, and quickly adapt to market changes. Open digital architecture facilitates the integration of diverse IT systems, supports cloud-native deployments, and enables real-time data analytics, all of which are critical for staying competitive in today's fast-evolving landscape. The rise of 5G, IoT, and artificial intelligence further amplifies the need for modular, scalable, and interoperable digital architectures, driving demand for advanced components such as API management, orchestration, and analytics.

Another significant factor propelling the market is the increasing focus on agility and cost efficiency. Traditional monolithic IT architectures are often rigid, expensive to maintain, and slow to adapt to changing requirements. In contrast, open digital architecture components provide a flexible, plug-and-play approach that allows organizations to innovate rapidly, reduce integration costs, and future-proof their technology investments. The proliferation of open standards and open-source solutions is lowering barriers to entry, enabling even small and medium enterprises (SMEs) to leverage sophisticated digital capabilities that were previously accessible only to large corporations. This democratization of technology is expanding the addressable market and fostering a vibrant ecosystem of solution providers and integrators.

Security and compliance considerations are also shaping the evolution of the Open Digital Architecture Components market. As organizations embrace hybrid and multi-cloud environments, the need for robust security frameworks, data management tools, and governance mechanisms becomes paramount. Open digital architectures enable organizations to implement consistent security policies, streamline regulatory compliance, and mitigate risks associated with data breaches and cyberattacks. The ability to seamlessly orchestrate security controls and monitor data flows across distributed environments is a key differentiator, particularly in highly regulated sectors such as finance and healthcare. Vendors are responding by embedding advanced security features and analytics capabilities into their offerings, further enhancing the value proposition of open digital architectures.

From a regional perspective, North America remains the largest and most mature market for Open Digital Architecture Components, driven by early adoption among leading telecommunications and IT service providers. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization initiatives, expanding 5G networks, and government investments in smart infrastructure. Europe is also witnessing significant growth, particularly in the BFSI and healthcare sectors, where regulatory requirements and digital innovation are converging to drive adoption. Latin America and the Middle East & Africa are gradually catching up, supported by increasing investments in digital infrastructure and a growing appetite for cloud-based solutions. Overall, the global landscape is characterized by dynamic regional trends, with each market presenting unique opportunities and challenges for solution providers.

Component Analy

This dataset belongs to the following dissertation: CoHLA: Rapid Co-simulation Construction. The dataset contains the source code for CoHLA and its libraries described in the dissertation, including the models that were used for the experiments. More information, resources and documentation on CoHLA can be found online: https://cohla.nl/DIRECTORIES- CoHLA: Contains the CoHLA libraries, documentation and sources for the DSL, including a sample project.- CoHLA-projects: Contains a number of projects for CoHLA that were used througout the disseration. These are grouped per chapter.- Lighting-DSL: Contains the sources for the specification and generation of a co-simulation of a smart lighting system as described in the dissertation and scalability papers. The README contains more information on the DSL.- Connector-DSL: Contains the sources for the Connector-DSL that was developed for connecting two components according to a specified protocol. Very basic documentation is included in the README.SHORT SUMMARYCyber-physical systems (CPSs) such as airplanes, cars and industrial production line robots are becoming ever more important in both industry and our everyday lives. These systems are highly complex systems that are constructed of many components from a variety of disciplines.Every discipline has its own development methods and tools and, in the end, all separately developed components should work together as one system. Using a model-based development approach, all disciplines develop component models using their own tools and development cycles.While these individual component models may be simulated to verify their behaviour, it is hard to simulate them together (co-simulation) to get a better understanding of the designed system as a whole.This dissertation introduces a domain-specific language called CoHLA that supports existing model-based methodologies for the design of CPSs to rapidly construct a co-simulation of the system under design. Using co-simulation during system design allows for early system analysis and the development of system-level features. By generating source code for the co-simulations, CoHLA decreases the time needed to develop and maintain a co-simulation.

This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions. It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit. In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.

NAMUR Open Architecture Gateway Market Outlook

According to our latest research, the global NAMUR Open Architecture Gateway market size reached USD 1.32 billion in 2024, reflecting a robust expansion driven by the increasing demand for seamless industrial connectivity and advanced process automation. The market is projected to grow at a CAGR of 13.8% from 2025 to 2033, with the market size anticipated to reach USD 4.09 billion by 2033. This remarkable growth is primarily fueled by the rising adoption of Industrial Internet of Things (IIoT) solutions, the need for enhanced data interoperability, and the ongoing digital transformation initiatives across multiple process industries.

A significant growth driver for the NAMUR Open Architecture Gateway market is the accelerating trend of digitalization in industrial environments. As industries such as oil & gas, chemicals, and pharmaceuticals increasingly shift towards smart manufacturing and Industry 4.0 paradigms, the need for open, interoperable, and scalable gateway solutions becomes paramount. NAMUR Open Architecture (NOA) gateways enable seamless integration of legacy systems with modern digital platforms, facilitating real-time data exchange, process optimization, and predictive maintenance. This capability is especially crucial in industries with complex automation infrastructures, where the ability to bridge operational technology (OT) and information technology (IT) domains can significantly enhance productivity, reduce downtime, and improve asset management.

Another pivotal factor propelling market growth is the increasing emphasis on cybersecurity and secure data management in industrial settings. As the volume and complexity of industrial data continue to surge, organizations are prioritizing secure and reliable data transmission across distributed assets. NAMUR Open Architecture Gateway solutions are designed with robust security protocols and compliance with industry standards, ensuring that sensitive operational data is protected against cyber threats and unauthorized access. This focus on security, combined with the gateways’ ability to support a wide range of industrial communication protocols, is driving widespread adoption, particularly in critical infrastructure sectors such as energy & utilities and water & wastewater management.

Furthermore, the rapid expansion of IIoT and the proliferation of connected devices are creating new opportunities for the NAMUR Open Architecture Gateway market. As enterprises deploy more sensors, actuators, and smart devices across their operations, the need for scalable, flexible, and vendor-agnostic gateway solutions becomes increasingly evident. NAMUR Open Architecture Gateways facilitate the integration of diverse devices and systems, enabling unified data management, remote monitoring, and advanced analytics. This capability not only enhances operational visibility but also supports the implementation of innovative business models, such as predictive maintenance and outcome-based services, further accelerating market growth.

From a regional perspective, Asia Pacific is emerging as a key growth engine for the NAMUR Open Architecture Gateway market, driven by rapid industrialization, significant investments in smart manufacturing, and supportive government initiatives promoting digital transformation. North America and Europe also hold substantial market shares, benefiting from early adoption of IIoT technologies, strong presence of leading solution providers, and a mature industrial infrastructure. Meanwhile, markets in Latin America and the Middle East & Africa are witnessing steady growth, fueled by modernization efforts in energy, utilities, and process industries. This global expansion underscores the critical role of NAMUR Open Architecture Gateways in enabling the next wave of industrial innovation and efficiency.

Component Analysis

The Component segment of the NAMUR Open Architecture Gateway marke

Dataset with the raw UNIX timestamps for start/stop time points for each sample of each evaluated configuration as well as produced plots.

ShizukaMcDonald_dataCompressed file containing 172 data matrices. The cell values represent the number of times the row individual 'beat' the column individual.ShizukaMcDonald_Data.zipMetadataThis .csv file includes citation information and some basic data for each data matrix.

Open Architecture Mission Computer Market Outlook

As per our latest research, the Open Architecture Mission Computer market size reached USD 2.68 billion globally in 2024, driven by the increasing need for modular and interoperable computing systems across defense and commercial sectors. The market is projected to expand at a robust CAGR of 7.2% from 2025 to 2033, reaching an estimated value of USD 5.04 billion by the end of the forecast period. This strong growth trajectory is primarily fueled by the rapid adoption of open architecture standards, which are enabling faster upgrades, enhanced mission flexibility, and cost efficiencies in mission-critical applications.

One of the foremost growth factors for the Open Architecture Mission Computer market is the rising demand for modular and scalable computing platforms in defense and aerospace. As military operations become increasingly complex and technology-driven, there is a critical need for mission computers that can be easily upgraded and integrated with a variety of sensors, avionics, and communication systems. Open architecture standards, such as the Modular Open Systems Approach (MOSA), are gaining prominence as they allow for the seamless integration of components from multiple vendors, reducing vendor lock-in and enabling rapid technological advancements. Furthermore, governments worldwide are mandating the use of open architecture systems in new defense procurements to enhance interoperability and future-proof their investments, significantly boosting the market’s growth prospects.

The commercial sector is also playing a pivotal role in the expansion of the Open Architecture Mission Computer market. With the proliferation of autonomous vehicles, smart aviation systems, and advanced maritime platforms, commercial operators are increasingly seeking mission computers that offer flexibility, reliability, and the ability to accommodate evolving operational requirements. Open architecture solutions are particularly attractive for commercial applications as they support cost-effective lifecycle management, facilitate compliance with international safety standards, and enable faster time-to-market for new products and services. This trend is further amplified by the growing investments in research and development by leading OEMs and technology firms, who are leveraging open standards to create innovative solutions tailored to both commercial and defense needs.

Another significant driver for the market is the accelerating pace of digital transformation in mission-critical environments. The integration of artificial intelligence, machine learning, and advanced analytics into mission computers is becoming increasingly common, necessitating open and flexible computing architectures that can support high-performance processing and rapid data fusion. These advancements are enabling defense forces and commercial operators to achieve superior situational awareness, faster decision-making, and enhanced operational effectiveness. Additionally, the shift towards network-centric operations and the need for real-time data sharing across platforms are further propelling the adoption of open architecture mission computers, as they provide the necessary interoperability and scalability to support next-generation missions.

From a regional perspective, North America continues to dominate the Open Architecture Mission Computer market, accounting for the largest share in 2024, owing to substantial defense spending, a strong presence of leading OEMs, and early adoption of open architecture standards. Europe and Asia Pacific are also witnessing significant growth, driven by increasing modernization initiatives, rising security threats, and growing investments in indigenous defense capabilities. The Middle East & Africa and Latin America, while currently smaller markets, are expected to offer substantial growth opportunities over the forecast period as governments in these regions ramp up their defense modernization programs and seek cost-effective, interoperable solutions.

FloorPlan Dataset

This dataset contains color-coded architectural floorplans for residential plots of 5 Marla, 10 Marla, and 20 Marla, with a focus on Ground Floor (GF) plans. Each image is annotated using a consistent color scheme to indicate different room types and structural elements — making it ideal for applications in AI training, architectural analysis, and layout understanding.

📁 Dataset Structure

FloorPlan_Metadata/ ├── data_JSON/ # Individual JSON metadata files per image └── floor_plan_metadata_consolidated.csv # Combined metadata for all images

Metadata_Generation/ # Scripts + logs used to generate metadata ├── 0_generate_metadata.ipynb ├── 1.0_generate_metadata.py ├── 1.1_generate_metadata.py └── Workflow.txt

dataset/ # 308 floorplan images in .png format ├── 10Marla_GF_FP_001_V01.png ├── 10Marla_GF_FP_002_V01.png └── ...

🖼️ Floorplan Images

• Format: .png
• Total: 308
• Sizes Covered: 5 Marla, 10 Marla, 20 Marla
• Level: Ground Floor only
• Naming Convention: PlotSize_Floor_FP_Index_Version.png
  • Example: 10Marla_GF_FP_002_V01.png

Each image is manually verified to maintain architectural accuracy and uniform labeling standards.

📊 Metadata Details

1. floor_plan_metadata_consolidated.csv

A CSV file containing all metadata entries for the floorplans in structured format.

Key fields: - file_name - plot_size (5, 10, or 20 Marla) - floor_level (GF) - num_bedrooms, num_bathrooms, num_kitchens, etc. - orientation, total_area, construction_year (if available)

2. data_JSON/

Each .json file contains metadata for a specific image — ideal for structured processing and integration into pipelines.

⚙️ Metadata Generation Scripts

Scripts used to generate the metadata from floorplan images are included in the Metadata_Generation/ folder:

• 0_generate_metadata.ipynb: Jupyter notebook version for visual walkthrough
• 1.0_generate_metadata.py, 1.1_generate_metadata.py: Python scripts for automated generation
• Workflow.txt: Execution guide for reproducibility

These tools allow you to modify or extend metadata extraction logic for your own projects.

🚀 Use Cases

• AI models for architectural layout parsing
• Floorplan segmentation and annotation
• Generative design systems
• Dataset for training scene understanding models (e.g., Detectron2, Mask R-CNN)
• Real estate visual analytics

📜 License

License: CC BY 4.0 (Attribution)

You are free to use, share, and adapt the data with proper credit to the dataset creator.

🙌 Acknowledgment

This dataset was compiled and shared to aid the research and development of intelligent systems in architecture, construction, and real estate technology. If you find it useful, consider citing or linking this Kaggle page.

📫 Contact

For suggestions, feedback, or collaboration opportunities, feel free to reach out:

• 🧠 Kaggle Profile: Muhammad Mazhar Rehan
• 💻 GitHub: github.com/MazharRehan

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.