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Testing web APIs automatically requires generating input data values such as addressess, coordinates or country codes. Generating meaningful values for these types of parameters randomly is rarely feasible, which means a major obstacle for current test case generation approaches. In this paper, we present ARTE, the first semantic-based approach for the Automated generation of Realistic TEst inputs for web APIs. Specifically, ARTE leverages the specification of the API under test to extract semantically related values for every parameter by applying knowledge extraction techniques. Our approach has been integrated into RESTest, a state-of-the-art tool for API testing, achieving an unprecedented level of automation which allows to generate up to 100\% more valid API calls than existing fuzzing techniques (30\% on average). Evaluation results on a set of 26 real-world APIs show that ARTE can generate realistic inputs for 7 out of every 10 parameters, outperforming the results obtained by related approaches.

Card Random Number Generator Market Outlook

The global card random number generator market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 3.8 billion by 2032, expanding at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This growth is driven by the increasing demand for secure and fair gaming experiences, as well as the rising need for robust security mechanisms in financial transactions. The rapid digitalization and expansion of online gaming platforms further fuel the market's growth, offering numerous opportunities for advancements in random number generation technology.

One of the primary growth factors for the card random number generator market is the booming online gaming industry. As gaming platforms strive to provide fair and transparent gaming environments, the demand for sophisticated random number generators is surging. These generators ensure that card shuffling and other game mechanics are unpredictable and free from tampering, enhancing user trust and engagement. Additionally, advancements in cryptographic techniques have expanded the application of random number generators in secure online transactions, protecting user data and financial information from cyber threats.

The financial sector also plays a significant role in propelling the growth of the card random number generator market. Financial institutions rely on random number generators for various applications, including secure encryption, authentication processes, and transaction verification. As the frequency and sophistication of cyber-attacks increase, the need for advanced security solutions becomes more critical. Random number generators provide an essential layer of security, ensuring that sensitive information remains protected against fraudulent activities and unauthorized access.

Technological advancements, particularly in quantum computing, are another crucial driver of market growth. The development of quantum random number generators (QRNGs) promises unprecedented levels of randomness and security, making them highly attractive for use in critical applications such as cryptography, research simulations, and secure communications. These cutting-edge technologies are expected to revolutionize the random number generation landscape, paving the way for more reliable and tamper-proof systems across various industries.

When examining the regional outlook, North America is poised to dominate the card random number generator market, owing to its strong presence of leading technology companies and robust online gaming industry. The region's advanced technological infrastructure and high adoption rate of digital solutions further contribute to its market leadership. Asia Pacific is anticipated to showcase significant growth during the forecast period, driven by the expanding online gaming market, rising internet penetration, and increasing investments in cybersecurity. Europe is also expected to experience steady growth, supported by stringent regulatory requirements for data protection and secure digital transactions.

Type Analysis

The card random number generator market can be segmented by type into hardware random number generators (RNGs) and software RNGs. Hardware RNGs generate random numbers based on physical processes, such as electronic noise, which are inherently unpredictable. This type of RNG is favored for applications requiring high levels of security and integrity, such as cryptographic applications and secure communications. The increasing recognition of hardware RNGs' superior security features is driving their adoption in sectors like finance, where data protection is paramount.

Software RNGs, on the other hand, use algorithms to produce random numbers. While generally easier to implement and more cost-effective than hardware RNGs, software RNGs can be less secure due to their deterministic nature—they can potentially be predicted if the algorithm or seed value is compromised. Despite this, software RNGs are widely used in applications where high security is not as critical, such as gaming and lotteries. Their flexibility and ease of integration make them a popular choice for online gaming platforms and simulation applications.

The competition between hardware and software RNGs in the market is intense, as each type has its distinct advantages and applications. Innovations in both categories are continuously emerging, with hardware RNGs incorporating quantum technology to enhance randomness and security, while software RNGs are improving their algorithms to reduce

Artifacts for the paper titled Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?.

This artifact repository contains 9 compressed folders, as follows:

Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system).

Details about the generation of our datasets

1. Synthetic datasets

We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:

1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. From this DAG, time series data for each node is generated using a vector auto-regression (VAR) model. A fault is injected into a node by altering the noise term in the VAR model for two timestamps.

2. RCD data generator [25] uses the pyAgrum package [3] to generate a random DAG based on a given number of nodes, subsequently generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its conditional probability distribution.

3. CausIL data generator [15] generates causal graphs and time series data that simulate the behavior of microservice systems. It first constructs a DAG of services and metrics based on domain knowledge, then generates metric data for each node of the DAG using regressors trained on real metrics data. Unlike the CIRCA and RCD data generators, the CausIL data generator does not have the capability to inject faults.

To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods.

2. Data collected from benchmark microservice systems

We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.

Code

The code to reproduce the experimental results in the paper is available at https://github.com/phamquiluan/RCAEval.

References

As in our paper.

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Generator in Data Center Market Outlook

The global generator in data center market size was valued approximately at USD 8.5 billion in 2023 and is projected to reach an estimated USD 14.3 billion by 2032, growing at a CAGR of 6.0% during the forecast period. This steady growth trajectory is fueled by the increasing demand for uninterrupted power supply in data centers amidst the exponentially rising data usage and storage requirements globally. The advent of new technologies like IoT, AI, and big data analytics, along with the surging number of internet users across the globe, are some of the pivotal factors propelling the market forward. Moreover, the integration of renewable energy resources with traditional generator systems is creating new growth avenues for the market.

The burgeoning demand for data centers across various sectors such as IT, telecommunications, healthcare, and BFSI is a significant growth driver for the generator market. As data centers become central to business operations, ensuring uninterrupted power supply becomes crucial, thereby necessitating the deployment of robust generator systems. The increasing digital transformation initiatives have led to a boom in data generation, making data centers essential for storing and processing this massive amount of data. Consequently, the need for reliable power backup solutions is on the rise, directly impacting the demand for generators in data centers.

Another major growth factor is the heightened emphasis on energy efficiency and sustainability within data center operations. Companies are increasingly adopting strategies to minimize their carbon footprint, driving the demand for eco-friendly and energy-efficient generator systems. The integration of bi-fuel and gas generators is gaining traction as these solutions offer a greener alternative to traditional diesel generators. Moreover, the advancements in generator technologies, including the development of smart and automated systems, are enhancing operational efficiencies and presenting lucrative opportunities for market growth.

The increasing frequency of power outages and the vulnerability of power grids in certain regions further accentuate the necessity for reliable backup power solutions. In areas prone to natural disasters or with unstable power supply, generators have become indispensable for data center operations. Furthermore, regulatory standards and guidelines pertaining to data center operations and the growing concerns over data security are bolstering the market expansion, as companies strive to ensure 24/7 operational continuity. This necessity for consistent power further underscores the importance of efficient and reliable generator systems.

Regionally, North America holds a significant share of the generator market in data centers owing to the presence of major data center operators and technology firms. The ongoing digital transformation and technological advancements in countries like the United States and Canada are driving market growth. Meanwhile, the Asia Pacific region is anticipated to exhibit remarkable growth, driven by rapid technological adoption and industrialization in countries such as China, India, and Japan. The increasing number of internet users and the growth of cloud computing in these regions are contributing to the rise in data center establishments, thereby boosting the generator market.

Type Analysis

The generator market in data centers is primarily segmented by type into diesel generators, gas generators, and bi-fuel generators. Diesel generators have historically dominated the market due to their reliability and efficiency in providing backup power. They are preferred for their cost-effectiveness and robust performance in emergency situations. However, environmental concerns and government regulations regarding emissions have led to a gradual shift towards cleaner alternatives. Therefore, while diesel generators will continue to hold a substantial market share, their growth may be moderated as more sustainable solutions are adopted.

Gas generators are gaining traction as a cleaner alternative to diesel generators. With advancements in natural gas technology, these generators offer reduced emissions and operational costs, making them an attractive option for data centers aiming to meet sustainability goals. The fluctuation in oil prices and stricter emission regulations are further propelling the demand for gas generators. As data centers strive to adopt greener practices, the adoption of gas generators is likely to witness a significant uptick during the forecast period.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

The Domain Name Generator Software market is experiencing robust growth, driven by the increasing demand for online presence among Small and Medium-sized Enterprises (SMEs) and large enterprises alike. The ease of use and cost-effectiveness of these tools are major contributing factors. The market is segmented by operating system (Android and iOS), reflecting the mobile-first approach prevalent in today's digital landscape. While precise market sizing data is unavailable, considering the growth in e-commerce and online businesses, a conservative estimate places the 2025 market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 15% is projected for the period 2025-2033, indicating a significant expansion opportunity. Key market drivers include the rising need for unique and memorable domain names, the simplification of the domain registration process, and the integration of these generators with other online business tools. Trends such as AI-powered suggestion algorithms and increased automation are enhancing user experience and efficiency. However, challenges remain, including competition from free domain name suggestion tools and concerns around the security and privacy of user data. The North American market currently holds the largest market share, followed by Europe and Asia-Pacific, driven by high internet penetration and a strong entrepreneurial ecosystem. This trend is expected to continue, with significant growth potential in developing economies. The market is highly competitive, with numerous established players and emerging startups vying for market share. Future growth will depend on the continued innovation in the technology, strategic partnerships, and the adoption of advanced features that meet evolving business needs. The projected growth of the Domain Name Generator Software market is underpinned by the continuous expansion of the internet and the ongoing digital transformation across various industries. The increasing adoption of cloud-based solutions and the integration of domain name generators with website building platforms further fuel market expansion. The market is likely to witness significant consolidation in the coming years, with larger players acquiring smaller companies to enhance their offerings and broaden their reach. Moreover, the growing focus on cybersecurity and data privacy will necessitate robust security measures within domain name generator software, impacting future market developments. The demand for specialized domain name generation tools tailored for specific industry niches is also expected to increase, creating opportunities for niche players. Overall, the outlook for the Domain Name Generator Software market is positive, with continued growth projected over the forecast period, driven by increasing demand, technological advancements, and expanding global internet usage.

Data Center Generator Market Outlook

According to our latest research, the Global Data Center Generator market size was valued at $4.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 6.9% during the forecast period of 2025–2033. The primary factor fueling this growth is the exponential surge in global data consumption, driven by the proliferation of cloud computing, IoT devices, and digital transformation initiatives across industries. As enterprises and hyperscale data centers strive to ensure uninterrupted operations and meet stringent uptime requirements, the demand for reliable backup power solutions, particularly generators, is witnessing unprecedented growth. The increasing frequency of power outages and the rising cost of downtime further underscore the critical role of data center generators in maintaining business continuity.

Regional Outlook

North America continues to dominate the Data Center Generator market, accounting for the largest market share, estimated at over 35% of global revenue in 2024. This dominance can be attributed to the region’s mature data center ecosystem, advanced technological infrastructure, and the presence of major cloud service providers and hyperscale operators. Regulatory emphasis on data security and uptime, coupled with frequent weather-related power disruptions, has intensified the need for robust backup power solutions. Furthermore, substantial investments in digital infrastructure and the rapid adoption of edge computing are driving generator installations across both urban and remote locations. The United States, in particular, remains a hub for innovation and deployment of next-generation generator technologies, reinforcing North America’s leadership position in the global landscape.

The Asia Pacific region is emerging as the fastest-growing market, projected to register an impressive CAGR of 9.2% from 2025 to 2033. This rapid expansion is fueled by surging digitalization, booming e-commerce, and the aggressive rollout of 5G networks across countries like China, India, Japan, and Singapore. Governments and private enterprises are making significant investments in constructing new data centers to cater to the region’s massive internet user base and increasing data traffic. Favorable policy frameworks, tax incentives, and the establishment of data center parks are further accelerating market growth. The influx of global cloud providers establishing regional hubs and the growing trend of data localization are also propelling demand for high-capacity and energy-efficient generators in the Asia Pacific market.

In emerging economies across Latin America, the Middle East, and Africa, the Data Center Generator market is experiencing steady growth, albeit from a smaller base. Localized challenges such as unreliable grid infrastructure, frequent power outages, and regulatory uncertainties pose significant hurdles to market adoption. However, the rising penetration of mobile internet, increasing demand for digital services, and government-led digital transformation initiatives are creating new opportunities for generator suppliers. These regions are witnessing a gradual shift toward modular data centers and hybrid power solutions, as operators seek to balance cost, reliability, and sustainability. As international investors and technology providers increase their presence, localized manufacturing and tailored service offerings are expected to drive future market expansion in these high-potential markets.

Report Scope

Credit Card Generator Market Outlook

The global credit card generator market is projected to experience robust growth with a market size of approximately USD 580 million in 2023, and it is anticipated to reach USD 1.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.5%. The rising need for secure and efficient credit card testing tools, driven by the expansion of e-commerce and digital transactions, forms a significant growth catalyst for this market. As online retail and digital financial services burgeon, the demand for reliable credit card generators continues to escalate, underscoring the importance of this market segment.

One of the pivotal growth drivers for the credit card generator market is the increasing complexity and sophistication of online payment systems. As e-commerce platforms and digital payment solutions proliferate worldwide, there is a growing need for comprehensive testing tools to ensure the reliability and security of these systems. Credit card generators play a crucial role in this context by providing developers and testers with the means to simulate various credit card scenarios, thereby enhancing the robustness of payment processing systems. Additionally, the rise in cyber threats and fraud necessitates stringent testing, further propelling market growth.

Another significant factor contributing to the market's expansion is the growing emphasis on fraud prevention and security. Financial institutions and businesses are increasingly investing in sophisticated tools to combat fraud and secure financial transactions. Credit card generators offer a practical solution for testing the efficacy of anti-fraud measures and ensuring that security protocols are adequately robust. By enabling the simulation of fraudulent activities and various transaction scenarios, these tools help organizations better prepare for and mitigate potential security breaches.

Furthermore, the marketing and promotional applications of credit card generators are also driving market growth. Companies leveraging digital marketing strategies use these tools to create dummy credit card numbers for various promotional activities, such as offering free trials or discounts, without exposing real customer data. This capability not only aids in marketing efforts but also ensures compliance with data privacy regulations, thereby enhancing consumer trust and brand reputation. The versatility of credit card generators in supporting both operational and marketing functions underscores their growing importance in the digital age.

Regionally, North America holds a significant share of the credit card generator market, driven by the high penetration of digital payment systems and advanced cybersecurity measures in the region. The presence of numerous financial institutions and technology companies further bolsters the market in North America. Meanwhile, Asia Pacific is expected to witness the fastest growth, fueled by the rapid digitalization of economies, increasing internet penetration, and burgeoning e-commerce activities. Europe also presents substantial opportunities due to stringent data protection regulations and the widespread adoption of digital transaction systems.

Type Analysis

The credit card generator market can be segmented by type into software and online services. Software-based credit card generators are widely used by developers and testers within organizations to simulate credit card transactions and validate payment processing systems. These tools are typically integrated into the development and testing environments, providing a controlled and secure platform for generating valid credit card numbers. The demand for software-based generators is driven by their ability to offer customizable options and advanced features, such as bulk generation and API integration, which enhance the efficiency of testing processes.

Online services, on the other hand, cater to a broader audience, including individual users, small businesses, and marketers. These services are accessible via web platforms and provide an easy-to-use interface for generating credit card numbers for various purposes, such as testing, fraud prevention, and marketing promotions. The growing popularity of online credit card generators can be attributed to their convenience, accessibility, and the increasing need for temporary and disposable credit card numbers in the digital economy. These services are particularly useful for busin

The global market for generators in data centers is experiencing robust growth, driven by the increasing demand for reliable power backup in the face of rising energy consumption and the expanding footprint of data centers worldwide. The market's expansion is fueled by the continuous growth of cloud computing, edge computing, and the Internet of Things (IoT), all of which require substantial power and necessitate robust backup solutions to prevent data loss and operational downtime. This necessitates investments in reliable and efficient generator systems, particularly in regions with unreliable grid infrastructure. The market is segmented by application (analog and digital control technologies), power capacity (below 500 KW, 501-1000 KW, 1001-3000 KW, and above 3000 KW), and geography. While the precise market size for 2025 isn't explicitly provided, based on industry trends and observed growth in related sectors, a reasonable estimate would place it in the range of $5-7 billion USD. A Compound Annual Growth Rate (CAGR) of approximately 8-10% is anticipated for the forecast period (2025-2033), driven primarily by increasing data center density in emerging markets and ongoing technological advancements in generator technology, leading to increased efficiency and reduced operational costs. Key players in the market include Caterpillar, Generac, MTU Onsite Energy, SDMO, Atlas Copco, and others. These companies are actively involved in developing innovative generator solutions tailored to the specific needs of data centers, focusing on features such as reduced noise levels, enhanced fuel efficiency, and improved power quality. Competitive dynamics are shaped by factors such as technological advancements, pricing strategies, and the ability to provide reliable after-sales service and support. Restraints on market growth include the high initial investment cost associated with generator installations and ongoing maintenance expenses. However, the increasing awareness of potential data loss and associated financial implications are likely to offset these concerns, driving sustained demand for reliable generator backup systems within the data center landscape.

The Terms of Use Generator market is projected to grow from USD 3.42 billion in 2025 to USD 13.14 billion by 2033, with a CAGR of 18.5% during the forecast period. The increasing need for online platforms and applications, along with growing concerns about data privacy and security, is fueling the market growth. Moreover, the escalating adoption of mobile applications and e-commerce platforms has created a further need for clear and comprehensive Terms of Use agreements. North America is expected to dominate the Terms of Use Generator market. The region's developed digital infrastructure, widespread adoption of mobile devices, and stringent data privacy regulations are contributing to its dominance. The Asia Pacific region is projected to witness significant growth in the coming years, driven by the region's expanding internet penetration, rising smartphone usage, and increasing awareness about online data protection.

6114 Global import shipment records of Generator with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The dataset contains 350 features engineered from the phasor measurements (PMU-type) signals from the IEEE New England 39-bus power system test case network, which are generated from the 9360 systematic MATLAB®/Simulink electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of machine learning techniques for transient stability assessment (TSA) of electrical power systems.

Different load and generation levels of the New England 39-bus benchmark power system were systematically covered, as well as all three major types of short-circuit events (three-phase, two-phase and single-phase faults) in all parts of the network. The consumed power of the network was set to 80%, 90%, 100%, 110% and 120% of the basic system load levels. The short-circuits were located on the busbar or on the transmission line (TL). When they were located on a TL, it was assumed that they can occur at 20%, 40%, 60%, and 80% of the line length. Features were obtained directly from the time-domain signals at the pickup time (pre-fault value) and at the trip time (post-fault value) of the associated distance protection relays.

This is a stochastic dataset of 3120 cases, created from the population of 9360 systematic simulations, which features a statistical distribution of different fault types, as follows: single-phase (70%), double-phase (20%) and three-phase faults (10%). It also features a class imbalance, with less than 20% of cases belonging to the unstable class. Dataset is a compressed CSV file.

List of feature names in the dataset:

• WmGx - rotor speed for each generator Gx, from G1 to G10,
• DThetaGx - rotor angle deviation for each generator Gx, from G1 to G10,
• ThetaGx - rotor mechanical angle for each generator Gx, from G1 to G10,
• VtGx - stator voltage for each generator Gx, from G1 to G10,
• IdGx - stator d-component current for each generator Gx, from G1 to G10,
• IqGx - stator q-component current for each generator Gx, from G1 to G10,
• LAfvGx - pre-fault power load angle for each generator Gx, from G1 to G10,
• LAlvGx - post-fault power load angle for each generator Gx, from G1 to G10,
• PfvGx - pre-falut value of the generator active power for each generator Gx, from G1 to G10,
• PlvGx - post-falut value of the generator active power for each generator Gx, from G1 to G10,
• QfvGx - pre-falut value of the generator reactive power for each generator Gx, from G1 to G10,
• QlvGx - post-falut value of the generator reactive power for each generator Gx, from G1 to G10,
• VAfvBx - pre-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,
• VBfvBx - pre-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,
• VCfvBx - pre-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,
• VAlvBx - post-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,
• VBlvBx - post-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,
• VClvBx - post-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,
• Stability - binary indicator (0/1) that determines if the power system was stable or unstable (0 - stable, 1 - unstable); this is the label variable.

License: Creative Commons CC-BY.

Disclaimer: This dataset is provided "as is", without any warranties of any kind.

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

Rack Random Number Generator Market Outlook

The global Rack Random Number Generator market size was estimated at USD 1.2 billion in 2023 and is projected to reach USD 2.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 8.5% during the forecast period. This growth is driven by the increasing demand for secure and reliable random number generation across various applications such as gaming, cryptography, and statistical sampling. The rise in cyber-attacks and the need for enhanced security protocols in digital transactions are significant growth factors propelling the market forward.

One of the primary growth drivers for the Rack Random Number Generator market is the increasing complexity of cyber-attacks and the resultant need for robust security measures. Random number generators (RNGs) play a crucial role in encryption and cryptographic applications, ensuring the security and integrity of data transmissions. The growing adoption of digital banking and online financial services has heightened the demand for RNGs to safeguard sensitive information against unauthorized access and fraud, thus fueling market growth.

Another significant factor contributing to the market's expansion is the rising popularity of online gaming and gambling platforms. RNGs are integral to ensuring fair play and unpredictability in gaming outcomes, which helps maintain player trust and platform credibility. As the gaming industry continues to evolve with advancements in technology and increased internet penetration, the demand for reliable and high-performance RNGs is expected to surge, further driving market growth.

Furthermore, the increasing use of RNGs in statistical sampling and simulations across various industries, including healthcare, finance, and research, is propelling the market. RNGs are essential tools for generating random samples and simulating complex models, which are critical for data analysis, risk assessment, and decision-making processes. The growing reliance on data-driven insights and the need for accurate, randomized data in scientific studies are key drivers boosting the adoption of RNGs in these applications.

Regionally, North America is expected to dominate the Rack Random Number Generator market during the forecast period. The region's strong presence of major technology companies, high adoption rate of advanced security solutions, and significant investments in research and development activities are contributing to market growth. Additionally, Asia Pacific is anticipated to witness substantial growth due to the increasing digitalization, expanding IT infrastructure, and rising awareness about data security among businesses and consumers.

Type Analysis

The Rack Random Number Generator market is segmented by type into Hardware RNG and Software RNG. Hardware RNGs are physical devices that generate random numbers using inherent physical processes, such as electronic noise. These RNGs are considered highly secure and reliable, making them ideal for critical applications in cryptography and secure communications. The demand for hardware RNGs is expected to grow steadily as organizations seek to enhance their security frameworks and protect sensitive data from potential cyber threats.

Software RNGs, on the other hand, generate random numbers through algorithmic processes. While they are less expensive and easier to implement compared to hardware RNGs, they are generally considered less secure due to their deterministic nature. However, advancements in software RNG algorithms have significantly improved their security and unpredictability, making them suitable for various applications, including gaming and statistical sampling. The flexibility and cost-effectiveness of software RNGs are key factors driving their adoption in the market.

In terms of market share, hardware RNGs are expected to hold a larger share due to their superior security features and widespread use in critical applications. However, the software RNG segment is anticipated to grow at a faster pace during the forecast period, driven by continuous advancements in software technologies and increasing demand for cost-effective RNG solutions. Both segments are essential to meet the diverse needs of different industries and applications, offering a range of options for businesses to choose from based on their specific requirements.

The integration of hardware and software RNGs into hybrid systems is also gaining traction in the market. These systems combine the strengths of both types, offering

The market for Card Random Number Generators (CRNGs) is experiencing robust growth, driven by increasing demand for secure and reliable random number generation in various applications. The market size in 2025 is estimated at $500 million, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by the rising adoption of CRNGs in diverse sectors such as data security, cryptography, gaming, and financial transactions. Advancements in quantum-resistant cryptography further bolster the market, as CRNGs are crucial for implementing these advanced security protocols. The increasing prevalence of online transactions and the growing concerns over data breaches are key factors propelling this demand. Major players like ID Quantique, Synopsys, and Intel are actively contributing to market growth through continuous innovation and product diversification. Market segmentation includes hardware-based and software-based CRNGs, with the hardware segment currently dominating due to its higher security and performance capabilities. Geographical expansion is also a significant driver, with North America and Europe currently leading the market, followed by Asia-Pacific experiencing significant growth potential. However, the market faces restraints including the high cost of advanced CRNG solutions and the complexity of integration into existing systems. Despite these challenges, the long-term outlook for the CRNG market remains positive. The continuous evolution of digital technologies and the ever-increasing emphasis on data security will create a consistent demand for reliable and high-quality random number generators. The increasing adoption of cloud-based services and the Internet of Things (IoT) are further expected to fuel market expansion in the coming years. The competitive landscape is characterized by both established players and emerging startups, resulting in innovative product development and competitive pricing. The ongoing research and development efforts in quantum-resistant cryptography are expected to further enhance market growth by creating new applications and opportunities for CRNGs. Strategic partnerships and collaborations between CRNG vendors and system integrators will also play a critical role in expanding the market reach and adoption rate.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Data Center Generator Market Outlook

According to our latest research, the global Data Center Generator market size reached USD 7.4 billion in 2024, demonstrating robust expansion driven by the surging demand for uninterrupted data center operations worldwide. The market is projected to grow at a CAGR of 6.2% from 2025 to 2033, reaching a forecasted value of USD 12.7 billion by 2033. This growth is underpinned by the rapid proliferation of hyperscale and edge data centers, increasing digitalization, and the critical need for reliable backup power solutions in an era of escalating data traffic and stringent uptime requirements.

One of the primary growth factors propelling the Data Center Generator market is the exponential rise in global data consumption, fueled by the expansion of cloud computing, IoT, AI, and big data analytics. As businesses and consumers increasingly rely on digital platforms, the continuous operation of data centers has become non-negotiable. Power outages, even momentary, can lead to significant financial losses, data corruption, and reputational damage. Generators serve as a vital contingency, ensuring that data centers maintain seamless operations during grid failures or scheduled maintenance. The growing frequency of extreme weather events and aging power infrastructure in several regions further accentuates the need for robust backup power systems, thereby driving the adoption of advanced data center generators.

Another significant driver is the escalating complexity and scale of data center infrastructure. With the emergence of hyperscale and colocation facilities, the capacity requirements for backup power solutions have surged. Operators are investing in high-capacity and highly efficient generator systems to meet stringent uptime standards, such as those defined by the Uptime Institute's tier classifications. Furthermore, increasing regulatory scrutiny around data protection, business continuity, and disaster recovery is compelling data center operators to bolster their power resilience strategies. These factors, coupled with the rising prevalence of hybrid and edge data centers in remote locations with unreliable grid access, are collectively fueling sustained demand for both traditional and next-generation generator technologies.

Sustainability is rapidly becoming a pivotal factor in the Data Center Generator market. Environmental concerns and tightening emissions regulations are prompting data center operators to shift towards greener alternatives, such as gas and hybrid generators, which offer reduced carbon footprints compared to conventional diesel generators. The integration of renewable energy sources and advanced energy management systems is also transforming generator deployment strategies. Leading market players are innovating with cleaner fuel technologies, enhanced efficiency, and digital monitoring capabilities to align with corporate sustainability goals and regulatory mandates. This transition towards eco-friendly and intelligent backup solutions is expected to create new avenues for growth and differentiation in the coming years.

Regionally, North America continues to dominate the Data Center Generator market owing to its dense concentration of hyperscale data centers, technological advancements, and stringent uptime requirements. However, the Asia Pacific region is rapidly emerging as a high-growth market, driven by massive investments in digital infrastructure across China, India, and Southeast Asia. Europe is also witnessing steady growth, propelled by digital transformation initiatives and increasing adoption of cloud services. Meanwhile, Latin America and the Middle East & Africa are experiencing increased activity, supported by expanding internet penetration and rising demand for local data storage and processing capabilities. These regional dynamics are shaping the competitive landscape and innovation trends within the global market.

Product Type Analysis

The Product Type segment of the Data Cente