This statistic shows the share of agricultural retailers in the United States offering yield monitor data analysis from 2005 to 2018. According to the report, the share of retailers offering yield data monitoring analysis will increase from ** percent in 2015 to ** percent in 2018.

In the second quarter of 2022, Dell's shipments of PC monitors amounted to over ************* units, whilst those of HP reached **** million shipments. Lenovo ranked third among vendors with **** million PC monitor shipments.

IntelligentMonitor: Empowering DevOps Environments With Advanced Monitoring and Observability aims to improve monitoring and observability in complex, distributed DevOps environments by leveraging machine learning and data analytics. This repository contains a sample implementation of the IntelligentMonitor system proposed in the research paper, presented and published as part of the 11th International Conference on Information Technology (ICIT 2023).

If you use this dataset and code or any herein modified part of it in any publication, please cite these papers:

P. Thantharate, "IntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability," 2023 International Conference on Information Technology (ICIT), Amman, Jordan, 2023, pp. 800-805, doi: 10.1109/ICIT58056.2023.10226123.

For any questions and research queries - please reach out via Email.

Abstract - In the dynamic field of software development, DevOps has become a critical tool for enhancing collaboration, streamlining processes, and accelerating delivery. However, monitoring and observability within DevOps environments pose significant challenges, often leading to delayed issue detection, inefficient troubleshooting, and compromised service quality. These issues stem from DevOps environments' complex and ever-changing nature, where traditional monitoring tools often fall short, creating blind spots that can conceal performance issues or system failures. This research addresses these challenges by proposing an innovative approach to improve monitoring and observability in DevOps environments. Our solution, Intelligent-Monitor, leverages realtime data collection, intelligent analytics, and automated anomaly detection powered by advanced technologies such as machine learning and artificial intelligence. The experimental results demonstrate that IntelligentMonitor effectively manages data overload, reduces alert fatigue, and improves system visibility, thereby enhancing performance and reliability. For instance, the average CPU usage across all components showed a decrease of 9.10%, indicating improved CPU efficiency. Similarly, memory utilization and network traffic showed an average increase of 7.33% and 0.49%, respectively, suggesting more efficient use of resources. By providing deep insights into system performance and facilitating rapid issue resolution, this research contributes to the DevOps community by offering a comprehensive solution to one of its most pressing challenges. This fosters more efficient, reliable, and resilient software development and delivery processes.

Components The key components that would need to be implemented are:

• Data Collection - Collect performance metrics and log data from the distributed system components. Could use technology like Kafka or telemetry libraries.
• Data Processing - Preprocess and aggregate the collected data into an analyzable format. Could use Spark for distributed data processing.
• Anomaly Detection - Apply machine learning algorithms to detect anomalies in the performance metrics. Could use isolation forest or LSTM models.
• Alerting - Generate alerts when anomalies are detected. It could integrate with tools like PagerDuty.
• Visualization - Create dashboards to visualize system health and key metrics. Could use Grafana or Kibana.
• Data Storage - Store the collected metrics and log data. Could use Elasticsearch or InfluxDB.

Implementation Details The core of the implementation would involve the following: - Setting up the data collection pipelines. - Building and training anomaly detection ML models on historical data. - Developing a real-time data processing pipeline. - Creating an alerting framework that ties into the ML models. - Building visualizations and dashboards.

The code would need to handle scaled-out, distributed execution for production environments.

Proper code documentation, logging, and testing would be added throughout the implementation.

Usage Examples Usage examples could include:

• Running the data collection agents on each system component.
• Visualizing system metrics through Grafana dashboards.
• Investigating anomalies detected by the ML models.
• Tuning the alerting rules to minimize false positives.
• Correlating metrics with log data to troubleshoot issues.

References The implementation would follow the details provided in the original research paper: P. Thantharate, "IntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability," 2023 International Conference on Information Technology (ICIT), Amman, Jordan, 2023, pp. 800-805, doi: 10.1109/ICIT58056.2023.10226123.

Any additional external libraries or sources used would be properly cited.

Tags - DevOps, Software Development, Collaboration, Streamlini...

Statistics illustrates consumption, production, prices, and trade of Video Monitors in Peru from 2007 to 2024.

Introduction

Emission Monitoring System Statistics: An Emission Monitoring System (EMS) is a critical tool used to measure and report the concentration of pollutants, such as COâ‚‚, NOx, SOâ‚‚, and particulate matter, from industrial processes.

It consists of sensors, analyzers, sampling probes, and data acquisition systems that continuously or periodically monitor emissions, ensuring compliance with environmental regulations.

EMS data supports both regulatory reporting and operational optimization, helping industries reduce emissions, improve energy efficiency, and maintain air quality standards.

By providing real-time insights and enabling corrective actions, EMS plays a key role in environmental protection, regulatory compliance, and sustainable industrial practices.

Enforcement and Compliance's Global Steel Trade Monitor provides global import and export trends for the top countries trading in steel mill products.To supplement ITA's core trade statistics in the Steel Import Monitoring and Analysis (SIMA) system, quarterly country reports for the top 20 steel importing and top 20 steel exporting countries will be published.Each country report includes export and import trends by country and aggregate product, production and consumption data, and where available, information about trade remedy actions taken on steel products.

🇬🇧 United Kingdom

Statistics on fires, casualties, false alarms and non-fire incidents attended by fire and rescue services in England.

Data Monitoring Software Market Outlook

The global data monitoring software market size was valued at approximately USD 4.5 billion in 2023, and it is projected to reach around USD 10.2 billion by 2032, witnessing a robust CAGR of 9.5% during the forecast period. The growth of this market is primarily driven by the increasing need for effective data management solutions amidst growing data volumes and the rising demand for real-time data analytics.

One of the primary growth factors contributing to the data monitoring software market is the exponential increase in data generation across various industries. The advent of digitization and the proliferation of IoT devices have led to an unprecedented rise in data volumes. Companies are now increasingly focusing on harnessing this data to gain actionable insights, improve operational efficiencies, and make informed decisions, thereby accelerating the adoption of data monitoring software solutions.

Furthermore, the growing emphasis on regulatory compliance and data privacy is significantly propelling the demand for data monitoring software. Regulatory frameworks such as GDPR, HIPAA, and CCPA necessitate stringent data monitoring and protection measures to avoid hefty penalties. Organizations are therefore investing heavily in sophisticated data monitoring solutions to ensure compliance and safeguard sensitive information. This regulatory landscape is expected to continue boosting the market growth over the forecast period.

Another key factor driving market expansion is the rising adoption of cloud-based solutions. Cloud computing offers scalable and cost-effective data storage and processing capabilities, making it an attractive option for enterprises of all sizes. The flexibility and ease of integration provided by cloud-based data monitoring solutions allow organizations to efficiently manage and monitor their data from remote locations. As a result, the demand for cloud-based data monitoring software is anticipated to witness substantial growth in the coming years.

In the realm of data management, a Database Performance Monitoring System plays a crucial role in ensuring that databases operate efficiently and without interruption. These systems provide real-time insights into database performance metrics, helping organizations identify bottlenecks and optimize query performance. As data volumes continue to grow, the ability to monitor and manage database performance becomes increasingly important. Organizations rely on these systems to maintain high availability, enhance data processing speeds, and ensure seamless user experiences. The integration of advanced analytics within these systems further aids in predictive maintenance, reducing downtime and improving overall operational efficiency.

Regionally, North America is expected to dominate the data monitoring software market due to the presence of major market players and the high adoption rate of advanced technologies. However, significant growth is also anticipated in the Asia Pacific region, driven by the increasing digitization efforts and the rising number of small and medium enterprises (SMEs) adopting data monitoring solutions. Europe is also expected to register substantial growth, propelled by stringent data protection regulations and the growing focus on data security.

Component Analysis

The data monitoring software market can be segmented by component into software and services. The software segment constitutes a significant portion of the market and is expected to continue its dominance over the forecast period. This segment includes various types of data monitoring software, such as network monitoring, application performance monitoring, and IT infrastructure monitoring tools. The increasing complexity of IT infrastructures and the growing need for real-time monitoring solutions are major factors driving the growth of the software segment.

Within the software segment, application performance monitoring (APM) tools are particularly gaining traction. These tools help organizations ensure the optimal performance of their applications by providing real-time insights into application health, user experience, and transaction performance. As businesses increasingly rely on digital applications for their operations, the demand for APM solutions is expected to witness robust growth.

On the other hand, the services segment comprises profes

Statistics illustrates consumption, production, prices, and trade of Video Monitors in the Netherlands from Jan 2019 to May 2025.

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes' data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo --- a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Data on fires attended by UK Fire and Rescue Service within the UK. This series replaced by Fire Statistics Monitor with effect from this edition. Source agency: Communities and Local Government Designation: National Statistics Language: English Alternative title: Quarterly fire statistics

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). The FAO launched a Round 6 household survey in Bangladesh through the DIEM Monitoring System to monitor agricultural livelihoods and food security. The survey started on 7 September 2022, conducting computer-assisted telephone interviews (CATI) until 8 October 2022. The sixth-round survey in Bangladesh utilized random sampling techniques to reach a sample size of 2,546 households, representative at the division level. The survey targeted all eight divisions of the country: Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

For the household survey conducted in Bangladesh for the sixth round, a total of 2,546 households were interviewed. The sampling design involved representative sampling at the division level, targeting all eight divisions of the country: Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. Additionally, specific hotspots identified in the Bangladesh Delta Plan 2100 were targeted, including Barind and the Drought-Prone Areas, Chars, Chittagong Hill Tracts, Coastal Zone, Cross-Cutting Area, and Haor and the Flash Flood Areas. The sampling procedure, a stratified random sampling approach was employed to ensure representation across divisions and hotspots. Data collection involved computer-assisted telephone interviews conducted between 7 September and 8 October 2022.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

A link to the questionnaire has been provided in the documentation tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries.

Abstract Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. Bios Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.

In 2022, gaming monitor shipments worldwide reached **** million units, with forecasts for 2022 suggesting that this is likely to rise to **** million units. This equates to a rise of around five percent and is a recovery from the ** percent decline the market experienced in the previous year.

A fully queryable REST API with JSON, XML, and CSV output as well as inline, runable examples. This is a monitor using data from Swift/BAT, MAXI and Fermi/GBM instruments.

The report covers North America Remote Patient Monitoring Companies and the market is segmented by Type of Device (Heart Monitors, Breath Monitors, Hematology Monitors, Multi-Parameter Monitors, Other Types of Devices), Application, End-User, and Geography.

The Global Entrepreneurship Monitor [GEM] research program was developed to provide comparisons among countries related to participation of adults in the firm creation process. The initial data was assembled as a pretest of five countries in 1998 and by 2012 over 100 countries had been involved in the program. The initial design for the GEM initiative was based on the first US Panel Study of Entrepreneurial Dynamics, and by 2012 data from 1,827,513 individuals had been gathered in 563 national samples and 6 specialized regional samples. This dataset is a harmonized file capturing results from all of the surveys. The procedure has been to harmonize the basic items across all surveys in all years, followed by implementing a standardized transform to identify those active as nascent entrepreneurs in the start-up process, as owner-managers of new firms, or as owner-managers of established firms. Those identified as nascent entrepreneurs or new business owners are the basis for the Total Entrepreneurial Activity [TEA] or Total Early-Stage index. This harmonized, consolidated assessment not only facilitates comparisons across countries, but provides a basis for temporal comparisons for individual countries. Respondents were queried on the following main topics: general entrepreneurship, start-up activities, ownership and management of the firm, and business angels (angel investors). Respondents were initially screened by way of a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, whether they thought it was a good time to start a new business, as well as their perceptions of the income potential and the prestige associated with starting a new business. Demographic variables include respondent age, sex, and employment status.

To determine inundation patterns and calculate site-specific tidal datums, we deployed water level data loggers (Model 3001, Solinst Canada Ltd., Georgetown, Ontario, Canada and Model U-20-001-01-Ti, Onset Computer Corp., Bourne, MA, USA) at all sites over the study period. Each site had one or two loggers (n = 16). We placed loggers at the mouth and upper reaches of second-order tidal channels to capture high tides and determine seasonal inundation patterns. Water loggers collected water level readings every six minutes starting on the date of deployment and continuing to the present. We used data from the lowest elevation logger at each site to develop local hydrographs and inundation rates. We surveyed loggers with RTK GPS at the time of deployment and at each data download that occurred quarterly, to correct for any vertical movement. We corrected all raw water level data with local time series of barometric pressure. For Solinst loggers, we deployed independent barometric loggers (Model 3001, Solinst Canada Ltd., Georgetown, Ontario, Canada); for Hobo water level loggers, we used barometric pressure from local airports (distance less than 10 miles). To determine tidal channel salinities, we deployed one conductivity logger at each site next to the lower elevation water level logger (Odyssey conductivity/temperature logger, Dataflow Systems Pty Limited, Christchurch, New Zealand). We converted specific conductance values obtained with the Odyssey loggers to practical salinity units using the equation from UNESCO (1983). We used water level data to estimate local tidal datums for all sites using procedures outlined in the NOAA Tidal Datums Handbook (NOAA 2003). We only calculated local MHW and MHHW because the loggers were positioned in the intertidal, which is relatively high in the tidal frame, and therefore did not capture MLW or MLLW and could not be used to compute these lower datums. We estimated mean tide level (MTL) for each site by using NOAA’s VDATUM 3.4 software (vdatum.noaa.gov), except at Bandon where we used MTL directly from historic NOAA data. Many results in this report are reported relative to local MHHW calculated from local water data. Water level loggers deployed within marsh channels recorded variation in water levels and salinity throughout the study duration. Loggers often did not capture lower portions of the tidal curve because of their location in tidal marsh channels which frequently drain at lower tides. From peak water levels, we calculated site-specific tidal datums (MHW and MHHW), and information on the highest observed water level (HOWL) during the time series. Our site specific tidal datum calculations generally closely matched tidal datums computed at nearby NOAA stations (tidesandcurrents.noaa.gov). Differences likely reflect site-specific tidal and bathymetric conditions in local estuarine hydrology. We collected salinity data at all sites, however, due to equipment recalls and failure we do not have salinity data for the duration of the study. We report weekly maximum salinities since many of our salinity loggers were not submerged during the entire tidal cycle at all sites, except for Grays Harbor due to recalled loggers and loggers being washed away during storm events. We observed a high level of variation in salinity between and within sites. Siletz experienced the greatest variation in salinity during the study period, ranging from 0.8 to 32 ppt. Willapa was the freshest system, ranging from 12-15 ppt and had very little temporal variation. The largest variation in salinity at most sites occurred from September through December. All sites had salinity below 35 ppt throughout most of the year; however the highest salinities were measured in August. See appendices for detailed site specific results.