Big Data Analytics In Healthcare Market size is estimated at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

This statistic depicts the revenue generated by the big data services market in the Asia Pacific (excluding Japan) from 2012 to 2014, as well as a forecast of revenue from 2015 to 2017. In 2014, revenues associated with the big data services market in the Asia Pacific amounted to 290 million U.S. dollars. 'Big data' refers to data sets that are too large or too complex for traditional data processing applications. Additionally, the term is often used to refer to the technologies that enable predictive analytics or other methods of extracting value from data.

Hadoop Big Data Analytics Solution Market Outlook

In 2023, the global Hadoop Big Data Analytics Solution market size was valued at approximately USD 45 billion and is projected to reach around USD 145 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.5% during the forecast period. This significant growth is driven by the increasing adoption of big data technologies across various industries, advancements in data analytics, and the rising need for cost-effective and scalable data management solutions.

One of the primary growth factors for the Hadoop Big Data Analytics Solution market is the exponential increase in data generation. With the proliferation of digital devices and the internet, vast amounts of data are being produced every second. This data, often referred to as big data, contains valuable insights that can drive business decisions and innovation. Organizations across sectors are increasingly recognizing the potential of big data analytics in enhancing operational efficiency, optimizing business processes, and gaining a competitive edge. Consequently, the demand for advanced analytics solutions like Hadoop, which can handle and process large datasets efficiently, is witnessing a substantial rise.

Another significant growth driver is the ongoing digital transformation initiatives undertaken by businesses globally. As organizations strive to become more data-driven, they are investing heavily in advanced analytics solutions to harness the power of their data. Hadoop, with its ability to store and process vast volumes of structured and unstructured data, is becoming a preferred choice for businesses aiming to leverage big data for strategic decision-making. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with Hadoop platforms is further augmenting their analytical capabilities, making them indispensable tools for modern enterprises.

The cost-effectiveness and scalability of Hadoop solutions also contribute to their growing popularity. Traditional data storage and processing systems often struggle to handle the sheer volume and variety of big data. In contrast, Hadoop offers a more flexible and scalable architecture, allowing organizations to store and analyze large datasets without incurring prohibitive costs. Moreover, the open-source nature of Hadoop software reduces the total cost of ownership, making it an attractive option for organizations of all sizes, including small and medium enterprises (SMEs).

From a regional perspective, North America is expected to dominate the Hadoop Big Data Analytics Solution market during the forecast period. The region's strong technological infrastructure, coupled with the presence of major market players and early adopters of advanced analytics solutions, drives market growth. Additionally, the increasing focus on data-driven decision-making and the high adoption rates of digital technologies in sectors like BFSI, healthcare, and retail further bolster the market in North America. Conversely, the Asia Pacific region is anticipated to witness the highest growth rate, driven by rapid digitalization, government initiatives promoting big data analytics, and the expanding e-commerce industry.

MapReduce Services play a pivotal role in the Hadoop ecosystem by enabling the processing of large data sets across distributed clusters. As businesses continue to generate vast amounts of data, the need for efficient data processing frameworks becomes increasingly critical. MapReduce, with its ability to break down complex data processing tasks into smaller, manageable units, allows organizations to analyze data at scale. This service is particularly beneficial for industries dealing with high-volume data streams, such as finance, healthcare, and retail, where timely insights can drive strategic decisions. The integration of MapReduce Services with Hadoop platforms enhances their data processing capabilities, making them indispensable tools for modern enterprises seeking to leverage big data for competitive advantage.

Component Analysis

When analyzing the Hadoop Big Data Analytics Solution market by component, it becomes evident that software, hardware, and services are the three main segments. The software segment encompasses the core Hadoop components like Hadoop Distributed File System (HDFS) and MapReduce, along with various tools and platforms designed to enhance its capabilities. The growing complexity and volume of data necessitate robust s

Big Data Security Market size was valued at USD 36.57 Billion in 2024 and is projected to reach USD 121.03 Billion by 2031, growing at a CAGR of 17.8% from 2024 to 2031.Global Big Data Security Market DriversGrowth in Data Volumes: Every day, an exponential amount of data is generated from a variety of sources, such as social media, IoT devices, and enterprise applications. For enterprises, managing and safeguarding this enormous volume of data is turning into a major concern. Robust big data security solutions are in high demand due to the requirement to protect important and sensitive data.Growing Complexity of Cyberthreats: Cyberattacks are become more advanced and focused. AI and machine learning are examples of cutting-edge tactics that attackers are employing to get past security measures. Advanced big data security procedures that can recognize, stop, and react to these complex threats instantly are required due to the constantly changing threat landscape.Strict Adherence to Regulations: Strict data protection laws, like the California Consumer Privacy Act (CCPA) in the US and the General Data Protection Regulation (GDPR) in Europe, are being implemented by governments and regulatory agencies around the globe. To avoid heavy fines and legal ramifications, organizations must abide by these requirements. Adoption of comprehensive big data security solutions to guarantee data privacy and protection is being driven by compliance requirements.Cloud Service Proliferation: Cloud services are becoming more and more popular as businesses look for scalable and affordable ways to handle and store data. But moving to cloud settings also means dealing with security issues. The need for big data security solutions that can safeguard cloud-based data is fueled by the need for specific security procedures to protect data in cloud infrastructures.

The Data Mining Tools Market size was valued at USD 1.01 USD billion in 2023 and is projected to reach USD 1.99 USD billion by 2032, exhibiting a CAGR of 10.2 % during the forecast period. The growing adoption of data-driven decision-making and the increasing need for business intelligence are major factors driving market growth. Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times. Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information. Recent developments include: May 2023 – WiMi Hologram Cloud Inc. introduced a new data interaction system developed by combining neural network technology and data mining. Using real-time interaction, the system can offer reliable and safe information transmission., May 2023 – U.S. Data Mining Group, Inc., operating in bitcoin mining site, announced a hosting contract to deploy 150,000 bitcoins in partnership with major companies such as TeslaWatt, Sphere 3D, Marathon Digital, and more. The company is offering industry turn-key solutions for curtailment, accounting, and customer relations., April 2023 – Artificial intelligence and single-cell biotech analytics firm, One Biosciences, launched a single cell data mining algorithm called ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities., May 2022 – Europe-based Solarisbank, a banking-as-a-service provider, announced its partnership with Snowflake to boost its cloud data strategy. Using the advanced cloud infrastructure, the company can enhance data mining efficiency and strengthen its banking position.. Key drivers for this market are: Increasing Focus on Customer Satisfaction to Drive Market Growth. Potential restraints include: Requirement of Skilled Technical Resources Likely to Hamper Market Growth. Notable trends are: Incorporation of Data Mining and Machine Learning Solutions to Propel Market Growth.

R code and data for a landscape scan of data services at academic libraries. Original data is licensed CC By 4.0, data obtained from other sources is licensed according to the original licensing terms. R scripts are licensed under the BSD 3-clause license. Summary This work generally focuses on four questions:

Which research data services does an academic library provide? For a subset of those services, what form does the support come in? i.e. consulting, instruction, or web resources? Are there differences in support between three categories of services: data management, geospatial, and data science? How does library resourcing (i.e. salaries) affect the number of research data services?

Approach Using direct survey of web resources, we investigated the services offered at 25 Research 1 universities in the United States of America. Please refer to the included README.md files for more information.

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Commercial reference buildings provide complete descriptions for whole building energy analysis using EnergyPlus (see "About EnergyPlus" resource link) simulation software. Included here is data pertaining to the reference building type "Large Office" for each of the 16 climate zones described on the Wiki page (see "OpenEI Wiki Page for Commercial Reference Buildings" resource link), and each of three construction categories: new (2004) construction, post-1980 construction existing buildings, and pre-1980 construction existing buildings. The dataset includes four key components: building summary, zone summary, location summary and a picture. Building summary includes details about: form, fabric, and HVAC. Zone summary includes details such as: area, volume, lighting, and occupants for all types of zones in the building. Location summary includes key building information as it pertains to each climate zone, including: fabric and HVAC details, utility costs, energy end use, and peak energy demand. In total, DOE developed 16 reference building types that represent approximately 70% of commercial buildings in the U.S.; for each type, building models are available for each of the three construction categories. The commercial reference buildings (formerly known as commercial building benchmark models) were developed by the U.S. Department of Energy (DOE), in conjunction with three of its national laboratories. Additional data is available directly from DOE's Energy Efficiency & Renewable Energy (EERE) website (see "About Commercial Buildings" resource link), including EnergyPlus software input files (.idf) and results of the EnergyPlus simulations (.html). Note: There have been many changes and improvements since this dataset was released. Several revisions have been made to the models and moved to a different approach to representing typical building energy consumption. For current data on building energy consumption please see the ComStock resource below.

The High Performance Computing (HPC) and High Performance Data Analytics (HPDA) Marketsize was valued at USD 46.01 USD billion in 2023 and is projected to reach USD 84.65 USD billion by 2032, exhibiting a CAGR of 9.1 % during the forecast period. High-Performance Computing (HPC) refers to the use of advanced computing systems and technologies to solve complex and large-scale computational problems at high speeds. HPC systems utilize powerful processors, large memory capacities, and high-speed interconnects to execute vast numbers of calculations rapidly. High-Performance Data Analytics (HPDA) extends this concept to handle and analyze big data with similar performance goals. HPDA encompasses techniques like data mining, machine learning, and statistical analysis to extract insights from massive datasets. Types of HPDA include batch processing, stream processing, and real-time analytics. Features include parallel processing, scalability, and high throughput. Applications span scientific research, financial modeling, and large-scale simulations, addressing challenges that require both intensive computing and sophisticated data analysis. Recent developments include: December 2023: Lenovo, a company offering computer hardware, software, and services, extended the HPC system “LISE” at the Zuse Institute Berlin (ZIB). This expansion would provide researchers at the institute with high computing power required to execute data-intensive applications. The major focus of this expansion is to enhance the energy efficiency of “LISE”. , August 2023: atNorth, a data center services company, announced the acquisition of Gompute, the HPC cloud platform offering Cloud HPC services, as well as on-premises and hybrid cloud solutions. Under the terms of the agreement, atNorth would add Gompute’s data center to its portfolio., July 2023: HCL Technologies Limited, a consulting and information technology services firm, extended its collaboration with Microsoft Corporation to provide HPC solutions, such as advanced analytics, ML, core infrastructure, and simulations, for clients across numerous sectors., June 2023: Leostream, a cloud-based desktop provider, launched new features designed to enhance HPC workloads on AWS EC2. The company develops zero-trust architecture around HPC workloads to deliver cost-effective and secure resources to users on virtual machines., November 2022: Intel Corporation, a global technology company, launched the latest advanced processors for HPC, artificial intelligence (AI), and supercomputing. These processors include data center version GPUs and 4th Gen Xeon Scalable CPUs.. Key drivers for this market are: Technological Advancements Coupled with Robust Government Investments to Fuel Market Growth. Potential restraints include: High Cost and Skill Gap to Restrain Industry Expansion. Notable trends are: Comprehensive Benefits Provided by Hybrid Cloud HPC Solutions to Aid Industry Expansion .

During a 2023 survey conducted in a variety of countries across the globe, it was found that 50 percent of respondents considered artificial intelligence (AI) to be a technology of strategic importance and would prioritize it in the coming year. 5G came in hot on the heels of AI, with 46 percent of respondents saying they would prioritize it.

Artificial intelligence

Artificial intelligence refers to the development of computer and machine skills to mimic human mind capabilities, such as problem-solving and decision-making. Particularly, AI learns from previous experiences to understand and respond to language, decisions, and problems. In recent years, more and more industries have adopted AI, from automotive to retail to healthcare, deployed to perform a variety of different tasks, including service operations and supply chain management. However, given its fast development, AI is not only affecting industries and job markets but is also impacting our everyday life.

Big data analytics

The expression “big data” indicates extremely large data sets that are difficult to process using traditional data-processing application software. In recent years, the size of the big data analytics market has increased and is forecast to amount to over 308 billion U.S. dollars in 2023. The growth of the big data analytics market has been fueled by the exponential growth in the volume of data exchanged online via a variety of sources, ranging from healthcare to social media. Tech giants like Oracle, Microsoft, and IBM form part of the market, providing big data analytics software tools for predictive analytics, forecasting, data mining, and optimization.

It is widely agreed that reference-based super-resolution (RefSR) achieves superior results by referring to similar high quality images, compared to single image super-resolution (SISR). Intuitively, the more references, the better performance. However, previous RefSR methods have all focused on single-reference image training, while multiple reference images are often available in testing or practical applications. The root cause of such training-testing mismatch is the absence of publicly available multi-reference SR training datasets, which greatly hinders research efforts on multi-reference super-resolution. To this end, we construct a large-scale, multi-reference super-resolution dataset, named LMR. It contains 112,142 groups of 300x300 training images, which is 10x of the existing largest RefSR dataset. The image size is also much larger. More importantly, each group is equipped with 5 reference images with different similarity levels. Furthermore, we propose a new baseline metho

Analytics of Things Market Outlook

The global market size for Analytics of Things (AoT) was estimated to be around $15 billion in 2023 and is expected to reach approximately $45 billion by 2032, growing at a compound annual growth rate (CAGR) of 15%. This remarkable surge in market size can be attributed to the increasing adoption of IoT devices and the growing necessity for advanced analytics to derive actionable insights from the vast amounts of data generated by these devices. Factors such as technological advancements, the proliferation of connected devices, and the need for operational efficiency are instrumental in driving this growth.

One of the primary growth factors for the Analytics of Things market is the exponential growth in IoT devices. As both consumer and industrial IoT devices become more prevalent, the volume of data generated has skyrocketed. This data, however, is only as valuable as the insights that can be gleaned from it. Advanced analytics tools provide the means to process and interpret this data, thereby enabling businesses to make data-driven decisions that enhance efficiency, productivity, and profitability. The increasing integration of IoT with AI and machine learning technologies further amplifies the potential of data analytics, making it an indispensable tool for modern enterprises.

Furthermore, the drive towards digital transformation in various industries is significantly fueling the demand for Analytics of Things. Organizations across sectors such as manufacturing, healthcare, retail, and transportation are increasingly leveraging IoT analytics to optimize their operations, enhance customer experiences, and develop new business models. Predictive maintenance, for instance, is a key application in manufacturing that helps in preempting equipment failures, thus reducing downtime and maintenance costs. Similarly, smart healthcare solutions that incorporate IoT analytics are improving patient outcomes through better monitoring and personalized treatment plans.

Another crucial growth factor is the rising emphasis on energy efficiency and sustainability. In sectors like energy and utilities, IoT analytics is being used to monitor and manage energy consumption, optimize resource utilization, and reduce carbon footprints. As global awareness and regulatory pressures regarding climate change and sustainability intensify, the adoption of Analytics of Things solutions is expected to grow. These solutions not only help in achieving sustainability goals but also contribute to cost savings by optimizing energy use and reducing waste.

The role of Big Data in Internet of Things (IoT) is becoming increasingly significant as the volume of data generated by IoT devices continues to grow exponentially. Big Data technologies enable the processing and analysis of vast datasets, which are crucial for deriving actionable insights from IoT data. By leveraging Big Data, organizations can handle the complexity and scale of IoT data, ensuring that they can extract meaningful patterns and trends. This capability is essential for applications such as predictive maintenance, real-time monitoring, and personalized services, where timely and accurate insights can lead to improved decision-making and operational efficiency. As IoT ecosystems expand, the integration of Big Data analytics is becoming a cornerstone for businesses aiming to harness the full potential of their IoT investments.

Regionally, North America holds a significant share in the Analytics of Things market, driven by the rapid adoption of advanced technologies and a robust IoT infrastructure. Europe and Asia Pacific are also prominent markets, with Asia Pacific expected to register the highest CAGR during the forecast period. The burgeoning industrial sector, increased investments in smart city projects, and government initiatives to promote digitalization are key factors propelling the growth of the Analytics of Things market in this region.

Component Analysis

The Analytics of Things market is segmented into software and services. The software segment encompasses various analytics tools and platforms that process, analyze, and visualize IoT data. These tools are critical for transforming raw data into actionable insights. The services segment includes consulting, implementation, and maintenance services that support the deployment and utilization of analytics solutions. Both segments are witnessing substantial growth, driven by the increasing complexity of IoT ecosys

EuroCrops is a dataset collection combining all publicly available self-declared crop reporting datasets from countries of the European Union.

The raw data obtained from the countries does not come in a unified, machine-readable taxonomy. We, therefore, developed a new Hierarchical Crop and Agriculture Taxonomy (HCAT) that harmonises all declared crops across the European Union. In the shapefiles you'll find these as additional attributes:

Participating countries

Find detailed information for all countries of the European Union in our GitHub Wiki, especially the countries represented in EuroCrops:

• Austria
• Belgium
• Germany
• Denmark
• Estonia
• Spain
• France
• Croatia
• Lithuania
• Latvia
• Netherlands
• Portugal
• Romania
• Sweden
• Slovenia
• Slovakia

Please also reference the countries' dependent source in case you're using their data.

New data-gathering techniques, often referred to as “Big Data” have the potential to improve statistics and empirical research in economics. In this paper we describe our work with online data at the Billion Prices Project at MIT and discuss key lessons for both inflation measurement and some fundamental research questions in macro and international economics. In particular, we show how online prices can be used to construct daily price indexes in multiple countries and to avoid measurement biases that distort evidence of price stickiness and international relative prices. We emphasize how Big Data technologies are providing macro and international economists with opportunities to stop treating the data as “given” and to get directly involved with data collection.

The size of the Saudi Arabia Data Center Storage market was valued at USD XXX Million in 2023 and is projected to reach USD XXXX Million by 2032, with an expected CAGR of 13.60% during the forecast period.In terms of the growth of data centers, Saudi Arabia is a promising market that witnesses a tremendous upsurge as a result of the accelerated speed of digital transformation across the country and Vision 2030. Data center storage refers to specifically designed hardware and software systems that protect large volumes of data stored and managed in the data centers using disk arrays, tape libraries, object storage, and cloud storage solutions.The growth of the market is also supported by cloud computing, big data analytics, artificial intelligence, and the Internet of Things (IoT) in Saudi Arabia. These technologies are generating huge amounts of data, which need strong and scalable storage infrastructure. Moreover, government initiatives in Saudi Arabia, focusing on the development of digital innovation and e-governance, are further supporting the demand for advanced data center storage solutions in the country. Recent developments include: March 2024: Pure Storage launched advanced data storage technologies and services. The company announced new self-service capabilities across its Pure1 storage management platform and Evergreen portfolio, offering software-based solutions, all via a single platform experience, to global customers., April 2023 -Hewlett Packard Enterprise announced new file, block, disaster, and backup recovery data services designed to help customers eliminate data silos, reduce cost and complexity, and improve performance. The new file storage data services deliver scale-out, enterprise-grade performance for data-intensive workloads, and the expanded block services provide mission-critical storage with mid-range economics.. Key drivers for this market are: Growing Digitalization and Emergence of Data-centric Applications, Evolution of Hybrid Flash Arrays. Potential restraints include: Compatibility and Optimum Storage Performance Issues. Notable trends are: IT and Telecom to Hold Significant Share.

In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to “improve the efficiency of all social systems,” “create new industries,” and “increase intellectual productivity.” For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea. Methods We used the bibliographic data of Web of Science (WoS) core collection, one of the biggest bibliographic databases from 2000 to 2018. The analysis of the visualization organizational diversity used data from 2018; studies on AI, big data, and IoT have been continuously increasing, reaching 3,133, 5,155, and 4,662 related papers in 2018, respectively. The 23 Essential Science Indicators Subject Areas in the Web of Science Core Collection were used for the article specialties. This data was generated from the "Web of Science Categories” using a conversion table (Thomson Reuters Community, 2012).

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

The size of the Analytics in Healthcare Industry market was valued at USD 46.50 Million in 2023 and is projected to reach USD 197.16 Million by 2032, with an expected CAGR of 22.92% during the forecast period. The Analytics in Healthcare Industry refers to the use of data analysis, predictive modeling, and statistical methods to derive insights and support decision-making in healthcare. Healthcare analytics enables organizations to improve patient care, optimize operations, reduce costs, and enhance overall efficiency. The rise of big data, artificial intelligence (AI), machine learning (ML), and cloud computing has transformed the way healthcare providers, payers, and pharmaceutical companies manage and analyze data. The widespread implementation of EHRs has led to an enormous amount of patient data being collected. Healthcare analytics tools help in extracting valuable insights from this data to improve patient outcomes and operational efficiency.Increased emphasis on personalized healthcare: Analytics enable healthcare providers to tailor treatments based on individual patient data.Cost optimization: Analytics help healthcare organizations optimize costs by identifying areas for improvement and reducing operational inefficiencies.Improved patient outcomes: By analyzing patient data, healthcare providers can identify risk factors and develop early intervention strategies.Enhanced research and development: Analytics empower researchers to analyze vast amounts of data to identify new patterns and develop innovative therapies. Recent developments include: August 2022: Syntellis Performance Solutions acquired Stratasan Healthcare Solutions, a healthcare market intelligence and data analytics company. Through the acquisition, Syntellis expanded its solutions for healthcare organizations with data and intelligence solutions to improve operational, financial, and strategic growth planning., June 2022: Oracle Corporation acquired Cerner Corporation to combine the clinical capabilities of Cerner with Oracle's enterprise platform analytics and automation expertise., January 2022: IBM and Francisco Partners signed a definitive agreement under which Francisco Partners will acquire healthcare data and analytics assets from IBM that are currently part of the Watson Health business.. Key drivers for this market are: Technological Advancements and Favorable Governemnt Initiatives, Emergence of Big Data in the Healthcare Industry. Potential restraints include: Cost and Complexity of Software, Data Integrity and Privacy Concerns; Lack of Proper Skilled Labors. Notable trends are: The Predictive Analytics Segment is Expected to Witness High Growth Over the Forecast Period.

This study engaged 409 participants over a period spanning from July 10 to August 8, 2023, ensuring representation across various demographic factors: 221 females, 186 males, 2 non-binary, year of birth between 1951 and 2005, with varied annual incomes and from 15 Spanish regions. The MobileWell400+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic, emotional, social, behavioral, and well-being data. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.

The following is a non-exhaustive list of collected data:

Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).

Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, effects and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception.

Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) and social interaction (quantity and quality) are measured.

Data corresponding to weekly surveys prompted via the smartphone, where information on overall health, hours of physical activity per week, lonileness, and questions related to well-being are asked.

Data corresponding to an final survey prompted via the smartphone, consisting of similar questions to the ones asked in the initial survey, namely psychological and well-being items (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception questions.

For a more detailed description of the study please refer to MobileWell400+StudyDescription.pdf.

For a more detailed description of the collected data, variables and data files please refer to MobileWell400+FilesDescription.pdf.

The Japan data center server market, valued at approximately ¥22.68 billion (assuming "Million" refers to Japanese Yen) in 2025, is projected to experience steady growth with a Compound Annual Growth Rate (CAGR) of 2.52% from 2025 to 2033. This growth is fueled by increasing digitalization across various sectors, particularly IT and telecommunications, BFSI (Banking, Financial Services, and Insurance), and the government. The rising adoption of cloud computing and big data analytics, along with the need for enhanced data security and processing capabilities, are significant drivers. Demand for high-performance computing (HPC) solutions is also contributing to market expansion. Different server form factors, including blade, rack, and tower servers, cater to diverse needs within the market. While the market faces potential restraints such as high initial investment costs for data center infrastructure and concerns about energy consumption, the overall positive trend towards digital transformation is expected to outweigh these challenges. Major players like Dell Technologies, Hewlett Packard Enterprise, Cisco, Lenovo, and others, are vying for market share through technological innovation and strategic partnerships. The market's segmentation by both form factor and end-user allows for a granular understanding of specific demands within the Japanese market. While the provided data focuses on the overall market size and CAGR, a deeper dive into regional variations within Japan (e.g., Tokyo vs. other regions) would reveal further insights. Analysis of specific customer segments, such as the unique needs of the BFSI sector in Japan compared to other countries, offers opportunities for targeted marketing and product development. Furthermore, exploring emerging technologies like edge computing and their influence on server demand would provide a more comprehensive understanding of future market trajectories. Competitive analysis focusing on the strategies employed by leading vendors and their market penetration will also be crucial to understanding the competitive landscape and anticipating future market shifts. Recent developments include: February 2024 - Marubeni Corporation and Yondr Group entered a joint venture to develop data center facilities in Japan. Initially, Marubeni will construct a data center facility in the West Tokyo area, with further projects planned in the future. The project will initiate Marubeni's expansion into the emerging hyper-scale data center development market and its ambition to contribute to the decarbonization of society by supplying renewable energy to data centers., October 2023 - Air Trunk announced a significant expansion in Japan by launching a new data center in Osaka. The facility, OSK1, marks AirTrunk's third data center in Japan and its first venture outside the Tokyo region. OSK1 is set to offer more than 20 megawatts (MW) capacity, contributing to the regional diversity in a new major availability zone.. Key drivers for this market are: Increase in Construction of New Data Centers, Development of Internet Infrastructure, Increasing Adoption of Cloud and IoT Services. Potential restraints include: Increase in Construction of New Data Centers, Development of Internet Infrastructure, Increasing Adoption of Cloud and IoT Services. Notable trends are: Blade Server Form Factor Segment is Expected to Witness Significant Growth.