Data collected to facilitate research and development activities pertaining to Annual NLCD algorithm development used as a benchmark for algorithm improvement iterations. The primary goal of these preliminary data was to offer preliminary insight into algorithm performance and guidance for algorithm improvement through error analysis.

ARSENAL

Ground Data Processing Acceleration Market Outlook

According to our latest research, the global ground data processing acceleration market size reached USD 4.68 billion in 2024, driven by the increasing demand for real-time analytics and high-speed data processing across various industries. The market is expected to grow at a robust CAGR of 13.2% from 2025 to 2033, reaching a projected value of USD 14.03 billion by 2033. This significant growth is primarily fueled by the proliferation of satellite data, advancements in artificial intelligence, and the need for efficient ground-based data management solutions.

The primary growth factor for the ground data processing acceleration market is the exponential increase in data generated from satellites, remote sensors, and Earth observation platforms. As the number of satellites and remote sensing devices in orbit continues to surge, the volume of data being transmitted to ground stations has reached unprecedented levels. This has created an urgent need for advanced data processing acceleration technologies capable of handling massive datasets in real-time or near-real-time. The integration of high-performance computing, field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) into ground stations is enabling organizations to process, analyze, and extract valuable insights from raw data much faster than traditional approaches. Furthermore, the growing adoption of artificial intelligence and machine learning algorithms in data processing workflows is further enhancing the efficiency and accuracy of data interpretation, making ground data processing acceleration an indispensable component for industries reliant on timely and precise information.

Another key driver of market growth is the increasing reliance on ground data processing acceleration solutions in critical applications such as defense and intelligence, weather forecasting, and disaster management. In defense and intelligence, rapid data processing is essential for situational awareness, threat detection, and mission planning, where delays can have significant consequences. Similarly, in weather forecasting and disaster management, the ability to process large volumes of satellite and sensor data in real-time can be the difference between timely alerts and catastrophic impacts. The adoption of cloud-based and on-premises acceleration solutions is enabling governments, research institutes, and commercial organizations to scale their data processing capabilities according to mission requirements, ensuring they can respond effectively to dynamic and unpredictable scenarios.

Technological advancements are also playing a pivotal role in shaping the ground data processing acceleration market. Innovations such as edge computing, 5G connectivity, and hybrid cloud infrastructures are enabling faster, more secure, and cost-effective data processing. Edge computing, in particular, allows for preliminary data analysis closer to the data source, reducing latency and bandwidth requirements for transmitting raw data to centralized ground stations. The convergence of these technologies is creating new opportunities for service providers, hardware manufacturers, and software developers to deliver integrated solutions that address the evolving needs of end-users across aerospace & defense, government, commercial, and research sectors.

From a regional perspective, North America continues to dominate the ground data processing acceleration market, accounting for the largest share due to its advanced satellite infrastructure, significant investments in defense and space exploration, and a strong presence of leading technology providers. Europe follows closely, driven by collaborative space programs and robust research initiatives. The Asia Pacific region is witnessing the fastest growth, fueled by increasing satellite launches, expanding commercial space activities, and government initiatives to enhance national security and disaster preparedness. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing investments in space technologies and international collaborations.

Component Analysis

The ground data processing acceleration market is segmented by component into hardware, software, and services, each playing a crucial role in the ecosystem. Hardware forms the backbone of acceleration solutions, encompassing high-performance servers, FPGAs, GPUs, and specialized processors designed for intensive d

In-Orbit Data Processing Platform Market Outlook

As per our latest research, the global In-Orbit Data Processing Platform market size in 2024 is valued at USD 1.13 billion, reflecting the rapid adoption of advanced satellite technologies and edge computing in space. The market is projected to grow at a robust CAGR of 17.8% during the forecast period, reaching an estimated USD 5.07 billion by 2033. This remarkable growth is primarily driven by the increasing demand for real-time data analytics, enhanced satellite capabilities, and the proliferation of commercial space activities worldwide.

The primary growth factor fueling the In-Orbit Data Processing Platform market is the exponential increase in satellite launches and the subsequent surge in data generation from space assets. With the deployment of large satellite constellations for Earth observation, communication, and navigation, there is a critical need to process vast amounts of data directly in orbit to enable faster decision-making and reduce latency. Traditional approaches that rely on transmitting raw data back to ground stations are increasingly becoming impractical due to bandwidth limitations and the need for near-instantaneous responses in applications such as disaster management, defense, and climate monitoring. As a result, in-orbit data processing platforms are being rapidly adopted to perform preliminary data filtering, compression, and analytics, thereby optimizing the use of limited downlink resources and enhancing mission efficiency.

Another significant growth driver is the evolution of hardware and software technologies tailored for the harsh space environment. Advances in radiation-hardened processors, miniaturized high-performance computing modules, and AI-enabled analytics engines have enabled the deployment of sophisticated processing platforms onboard satellites. These platforms are capable of executing complex algorithms for image recognition, pattern detection, and anomaly identification, directly in orbit. The integration of machine learning and artificial intelligence has opened new avenues for autonomous satellite operations, predictive maintenance, and adaptive mission planning, further accelerating the adoption of in-orbit data processing solutions across commercial, government, and scientific missions.

The expanding role of private space companies and the growing interest of national space agencies in deep space exploration are also contributing to the marketÂ’s momentum. Commercial ventures are increasingly leveraging in-orbit data processing to provide value-added services such as real-time geospatial intelligence, broadband connectivity, and space situational awareness. Government and defense organizations are investing in advanced platforms to enhance surveillance, reconnaissance, and secure communications. Additionally, the emergence of collaborative international projects aimed at planetary exploration and space science is fostering innovation and driving investments in this sector. Collectively, these factors are creating a vibrant ecosystem that supports the sustained growth of the in-orbit data processing platform market.

Onboard Data Processing is revolutionizing the way satellites manage and utilize data in space. By processing data directly on the satellite, these systems significantly reduce the amount of raw data that needs to be transmitted back to Earth, thus optimizing bandwidth usage and minimizing latency. This capability is particularly crucial for applications requiring real-time analytics, such as disaster response and environmental monitoring, where timely data can make a significant difference. The integration of Onboard Data Processing allows for more efficient use of satellite resources, enabling complex computations and data analysis to occur in orbit, which enhances the overall mission effectiveness and responsiveness.

From a regional perspective, North America currently leads the global market, accounting for the largest share due to its robust space infrastructure, significant R&D investments, and the presence of major industry players. Europe follows closely, driven by strong governmental support and a focus on space sustainability and security. Asia Pacific is witnessing the fastest growth, propelled by increasing satellite launches from countries like China, India, and Japan, as well as the expansion of commercial space activ

Data pre-processing plays a key role in a data analytics process (e.g., applying a classification algorithm on a predictive task). It encompasses a broad range of activities that span from correcting errors to selecting the most relevant features for the analysis phase. There is no clear evidence, or rules defined, on how pre-processing transformations impact the final results of the analysis. The problem is exacerbated when transformations are combined into pre-processing pipeline prototypes. Data scientists cannot easily foresee the impact of pipeline prototypes and hence require a method to discriminate between them and find the most relevant ones (e.g., with highest positive impact) for their study at hand. Once found, these prototypes can be instantiated and optimized e.g., using Bayesian Optimization. In this work, we study the impact of transformations when chained together into prototypes, and the impact of transformations when instantiated via various operators. We develop and scrutinize a generic method that allows to generate pre-processing pipelines, as a step towards AutoETL. We make use of rules that enable the construction of prototypes (i.e., define the order of transformations), and rules that guide the instantiation of the transformations inside the prototypes (i.e., define the operator for each transformation). The optimization of our effective pipeline prototypes provide results that compared to an exhaustive search, get 90% of the predictive accuracy in the median, but with a time cost that is 24 times smaller.

The dataset includes annual data for average processing time and counts of initial disability claims in which there was a medical determination made. The data is broken out by those cases handled by each Processing Center (PC), the total for all PCs, and total claims processed by the agency for all offices. The cases processed by PC8 are international claims. This dataset provides data for federal fiscal years 2012 on.

Dataset

This dataset was created by Vuppala Adithya Sairam

Released under CC0: Public Domain

Contents

In accordance with Annex 2 of Decree 106/2025 Coll., which implements certain provisions of the Act on the Public Hydrometeorological Service, the Czech Hydrometeorological Institute (CHMI) has made operational (unverified) primary and aggregated data available for outdoor air pollutants (pollutant concentrations from automatic monitoring stations, provided in the scope and temporal aggregations specified by Act No. 201/2012 Coll., on Air Protection).

The data include key pollutants with ambient air limit values under current legislation: arsenic (As), benzene, benzo[a]pyrene, cadmium (Cd), carbon monoxide (CO), nickel (Ni), nitrogen dioxide (NO₂), nitrogen oxides (NOₓ), ground-level ozone (O₃), lead (Pb), suspended particles PM₁₀, suspended particles PM₂.₅, and sulfur dioxide (SO₂).

The data are based on measurements from CHMI-owned stations since 1969. Primary data are provided in measurement intervals of 30 minutes, 1 hour, or 24 hours, depending on the type of measurement. Aggregated data are calculated from the aforementioned primary data and include the following: daily averages, monthly averages, annual averages, and the number of exceedances of ambient air limit values. Preliminary data include air quality information that is older than one hour. Verified data can be found in the dataset Air Quality Measurements – Verified Data, in accordance with the data verification process. Verification is carried out once a year, no later than July 1st (YYYY-1).

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is valued to increase USD 763.9 million, at a CAGR of 40.2% from 2024 to 2029. Integration of AI and ML technologies with data science platforms will drive the data science platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 48% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 38.70 million in 2023
By Component - Platform segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 763.90 million
CAGR : 40.2%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and continually evolving landscape, underpinned by advancements in core technologies and applications. Key technologies, such as machine learning and artificial intelligence, are increasingly integrated into data science platforms to enhance predictive analytics and automate data processing. Additionally, the emergence of containerization and microservices in data science platforms enables greater flexibility and scalability. However, the market also faces challenges, including data privacy and security risks, which necessitate robust compliance with regulations.
According to recent estimates, the market is expected to account for over 30% of the overall big data analytics market by 2025, underscoring its growing importance in the data-driven business landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Science Platform Market Segmented and what are the key trends of market segmentation?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Application

  Data Preparation
  Data Visualization
  Machine Learning
  Predictive Analytics
  Data Governance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving the market, big data processing is a key focus, enabling advanced model accuracy metrics through various data mining methods. Distributed computing and algorithm optimization are integral components, ensuring efficient handling of large datasets. Data governance policies are crucial for managing data security protocols and ensuring data lineage tracking. Software development kits, model versioning, and anomaly detection systems facilitate seamless development, deployment, and monitoring of predictive modeling techniques, including machine learning algorithms, regression analysis, and statistical modeling. Real-time data streaming and parallelized algorithms enable real-time insights, while predictive modeling techniques and machine learning algorithms drive business intelligence and decision-making.

Cloud computing infrastructure, data visualization tools, high-performance computing, and database management systems support scalable data solutions and efficient data warehousing. ETL processes and data integration pipelines ensure data quality assessment and feature engineering techniques. Clustering techniques and natural language processing are essential for advanced data analysis. The market is witnessing significant growth, with adoption increasing by 18.7% in the past year, and industry experts anticipate a further expansion of 21.6% in the upcoming period. Companies across various sectors are recognizing the potential of data science platforms, leading to a surge in demand for scalable, secure, and efficient solutions.

API integration services and deep learning frameworks are gaining traction, offering advanced capabilities and seamless integration with existing systems. Data security protocols and model explainability methods are becoming increasingly important, ensuring transparency and trust in data-driven decision-making. The market is expected to continue unfolding, with ongoing advancements in technology and evolving business needs shaping its future trajectory.

Request Free Sample

The On-premises segment was valued at USD 38.70 million in 2019 and showed

The Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) is a deep 120-168 MHz imaging survey that will eventually cover the entire Northern sky. Each of the 3,170 pointings will be observed for 8 hours, which, at most declinations, is sufficient to produce ~5-arcsec resolution images with a sensitivity of ~0.1 mJy/beam and accomplish the main scientific aims of the survey which are to explore the formation and evolution of massive black holes, galaxies, clusters of galaxies and large-scale structure. Due to the compact core and long baselines of LOFAR, the images provide excellent sensitivity to both highly extended and compact emission. For legacy value, the data are archived at high spectral and time resolution to facilitate sub-arcsecond imaging and spectral line studies. In this paper, The authors provide an overview of the LoTSS. They outline the survey strategy, the observational status, the current calibration techniques, a preliminary data release, and the anticipated scientific impact. The preliminary images that they have released were created using a fully-automated but direction-independent calibration strategy and are significantly more sensitive than those produced by any existing large-area low-frequency survey. In excess of 44,000 sources are detected in the images that have a resolution of 25-arcseconds, typical noise levels of less than 0.5 mJy/beam, and cover an area of 381 square degrees in the region of the HETDEX Spring Field (Right Ascension 10^h 45^m 00^s to 15^h 30^m ^00^s and Declination +45^o 00' 00" to +57^o 00' 00"). Source detection on the mosaics that are centered on each pointing was performed with PyBDSM (See http://www.astron.nl/citt/pybdsm/ for more details). In an effort to minimize contamination from artifacts, the catalog was created using a conservative 7-sigma detection threshold. Furthermore, as the artifacts are predominantly in regions surrounding bright sources, the authors utilized the PyBDSM functionality to decrease the size of the box used to calculate the local noise when close to bright sources, which has the effect of increasing the estimated noise level in these regions. Their catalogs from each mosaic are merged to create a final catalogue of the entire HETDEX Spring Field region. During this process, the authors remove multiple entries for sources by only keeping sources that are detected in the mosaic centered on the pointing to which the source is closest to the center. In the catalog, they provide the type of source, for which they used PyBDSM to distinguish isolated compact sources, large complex sources, and sources that are within an island of emission that contains multiple sources. In addition, they attempted to distinguish between sources that are resolved and unresolved in their images. The authors have provided a preliminary data release from the LOFAR Two-metre Sky Survey (LoTSS). This release contains 44,500 sources which were detected with a signal in excess of seven times the local noise in their 25" resolution images. The noise varies across the surveyed region but is typically below 0.5 mJy/beam and the authors estimate the catalog to be 90% complete for sources with flux densities in excess of 3.9 mJy/beam. This table was created by the HEASARC in February 2017 based on CDS Catalog J/A+A/598/A104 file lotss.dat. This is a service provided by NASA HEASARC .

Paper logs are the primary data collection tool used by observers of the Northeast Fisheries Observer Program and Industry Funded Scallop Program deployed on commercial fishing vessels. Data collected on paper logs are used to enter critical data fields into a web-based data entry program, OBPRELIM, and loads data directly into Oracle tables. OBPRELIM is used to enter trip, incidental take, and haul level data for in-season quota monitored fisheries and discard log data for the herring and longfin squid fisheries to track slippage events. OBPRELIM contains built in audit checks to increase data quality. OBPRELIM is also used for post-entry Fisheries Sampling Branch processing and verification.

Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.

Snapshot img

Cloud Analytics Market Size 2024-2028

The cloud analytics market size is forecast to increase by USD 74.08 billion at a CAGR of 24.4% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The adoption of hybrid and multi-cloud setups is on the rise, as these configurations enhance data connectivity and flexibility. Another trend driving market growth is the increasing use of cloud security applications to safeguard sensitive data.
However, concerns regarding confidential data security and privacy remain a challenge for market growth. Organizations must ensure robust security measures are in place to mitigate risks and maintain trust with their customers. Overall, the market is poised for continued expansion as businesses seek to leverage the benefits of cloud technologies for data processing and data analytics.

What will be the Size of the Cloud Analytics Market During the Forecast Period?

Request Free Sample

The market is experiencing significant growth due to the increasing volume of data generated by businesses and the demand for advanced analytics solutions. Cloud-based analytics enables organizations to process and analyze large datasets from various data sources, including unstructured data, in real-time. This is crucial for businesses looking to make data-driven decisions and gain valuable insights to optimize their operations and meet customer requirements. Key industries such as sales and marketing, customer service, and finance are adopting cloud analytics to improve key performance indicators and gain a competitive edge. Both Small and Medium-sized Enterprises (SMEs) and large enterprises are embracing cloud analytics, with solutions available on private, public, and multi-cloud platforms.
Big data technology, such as machine learning and artificial intelligence, are integral to cloud analytics, enabling advanced data analytics and business intelligence. Cloud analytics provides businesses with the flexibility to store and process data In the cloud, reducing the need for expensive on-premises data storage and computation. Hybrid environments are also gaining popularity, allowing businesses to leverage the benefits of both private and public clouds. Overall, the market is poised for continued growth as businesses increasingly rely on data-driven insights to inform their decision-making processes.

How is this Cloud Analytics Industry segmented and which is the largest segment?

The cloud analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments.

Solution

  Hosted data warehouse solutions
  Cloud BI tools
  Complex event processing
  Others


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The hosted data warehouse solutions segment is estimated to witness significant growth during the forecast period.

Hosted data warehouses enable organizations to centralize and analyze large datasets from multiple sources, facilitating advanced analytics solutions and real-time insights. By utilizing cloud-based infrastructure, businesses can reduce operational costs through eliminating licensing expenses, hardware investments, and maintenance fees. Additionally, cloud solutions offer network security measures, such as Software Defined Networking and Network integration, ensuring data protection. Cloud analytics caters to diverse industries, including SMEs and large enterprises, addressing requirements for sales and marketing, customer service, and key performance indicators. Advanced analytics capabilities, including predictive analytics, automated decision making, and fraud prevention, are essential for data-driven decision making and business optimization.

Furthermore, cloud platforms provide access to specialized talent, big data technology, and AI, enhancing customer experiences and digital business opportunities. Data connectivity and data processing in real-time are crucial for network agility and application performance. Hosted data warehouses offer computational power and storage capabilities, ensuring efficient data utilization and enterprise information management. Cloud service providers offer various cloud environments, including private, public, multi-cloud, and hybrid, catering to diverse business needs. Compliance and security concerns are addressed through cybersecurity frameworks and data security measures, ensuring data breaches and thefts are minimized.

Get a glance at the Cloud Analytics Industry report of share of various segments Request Free Sample

The Hosted data warehouse solutions s

This is the Harvard Dataset containing the raw results for the companion reproducible paper of [1]. To implement the optimization process, we departed from the code provided in 2. [1] J. Giovanelli, B. Bilalli, A. Abelló, "Data pre-processing pipeline generation for AutoETL", Inf. Syst. (2021) 101957. http://dx.doi.org/10.1016/j.is.2021.101957 [2] A. Quemy, "Data Pipeline Selection and Optimization." DOLAP. 2019. http://ceur-ws.org/Vol-2324/Paper19-AQuemy.pdf

Snapshot img

Big Data Market Size 2025-2029

The big data market size is valued to increase USD 193.2 billion, at a CAGR of 13.3% from 2024 to 2029. Surge in data generation will drive the big data market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 36% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 55.30 billion in 2023
By Type - Services segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 193.04 billion
Market Future Opportunities: USD 193.20 billion
CAGR from 2024 to 2029 : 13.3%

Market Summary

In the dynamic realm of business intelligence, the market continues to expand at an unprecedented pace. According to recent estimates, this market is projected to reach a value of USD 274.3 billion by 2022, underscoring its significant impact on modern industries. This growth is driven by several factors, including the increasing volume, variety, and velocity of data generation. Moreover, the adoption of advanced technologies, such as machine learning and artificial intelligence, is enabling businesses to derive valuable insights from their data. Another key trend is the integration of blockchain solutions into big data implementation, enhancing data security and trust.
However, this rapid expansion also presents challenges, such as ensuring data privacy and security, managing data complexity, and addressing the skills gap. Despite these challenges, the future of the market looks promising, with continued innovation and investment in data analytics and management solutions. As businesses increasingly rely on data to drive decision-making and gain a competitive edge, the importance of effective big data strategies will only grow.

What will be the Size of the Big Data Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Big Data Market Segmented?

The big data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


End-user

  BFSI
  Healthcare
  Retail and e-commerce
  IT and telecom
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the ever-evolving landscape of data management, the market continues to expand with innovative technologies and solutions. On-premises big data software deployment, a popular choice for many organizations, offers control over hardware and software functions. Despite the high upfront costs for hardware purchases, it eliminates recurring monthly payments, making it a cost-effective alternative for some. However, cloud-based deployment, with its ease of access and flexibility, is increasingly popular, particularly for businesses dealing with high-velocity data ingestion. Cloud deployment, while convenient, comes with its own challenges, such as potential security breaches and the need for companies to manage their servers.

On-premises solutions, on the other hand, provide enhanced security and control, but require significant capital expenditure. Advanced analytics platforms, such as those employing deep learning models, parallel processing, and machine learning algorithms, are transforming data processing and analysis. Metadata management, data lineage tracking, and data versioning control are crucial components of these solutions, ensuring data accuracy and reliability. Data integration platforms, including IoT data integration and ETL process optimization, are essential for seamless data flow between systems. Real-time analytics, data visualization tools, and business intelligence dashboards enable organizations to make data-driven decisions. Data encryption methods, distributed computing, and data lake architectures further enhance data security and scalability.

Request Free Sample

The On-premises segment was valued at USD 55.30 billion in 2019 and showed a gradual increase during the forecast period.

With the integration of AI-powered insights, natural language processing, and predictive modeling, businesses can unlock valuable insights from their data, improving operational efficiency and driving growth. A recent study reveals that the market is projected to reach USD 274.3 billion by 2022, underscoring its growing importance in today's data-driven economy. This continuous evolution of big data technologies and solutions underscores the need for robust data governa

Quantitative real-time polymerase chain reaction (qPCR) is routinely conducted for DNA quantitative analysis using the cycle-threshold (Ct) method, which assumes uniform/optimum template amplification. In practice, amplification efficiencies vary from cycle to cycle in a PCR reaction, and often decline as the amplification proceeds, which results in substantial errors in measurement. This study reveals the cumulative error for quantification of initial template amounts, due to the difference between the assumed perfect amplification efficiency and actual one in each amplification cycle. The novel CyC* method involves determination of both the earliest amplification cycle detectable above background (“outlier” C*) and the amplification efficiency over the cycle range from C* to the next two amplification cycles; subsequent analysis allows the calculation of initial template amount with minimal cumulative error. Simulation tests indicated that the CyC* method resulted in significantly less variation in the predicted initial DNA level represented as fluorescence intensity F0 when the outlier cycle C* was advanced to an earlier cycle. Performance comparison revealed that CyC* was better than the majority of 13 established qPCR data analysis methods in terms of bias, linearity, reproducibility, and resolution. Actual PCR test also suggested that relative expression levels of nine genes in tea leaves obtained using CyC* were much closer to the real value than those obtained with the conventional 2-ΔΔCt method. Our data indicated that increasing the input of initial template was effective in advancing emergence of the earliest amplification cycle among the tested variants. A computer program (CyC* method) was compiled to perform the data processing. This novel method can minimize cumulative error over the amplification process, and thus, can improve qPCR analysis.

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask:

A. Guiding Questions:
1. Who are the key stakeholders and what are their goals for the data analysis project? 2. What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: 1. Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team.

1. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare:

A. Guiding Questions: 1. Where is the data stored and organized? 2. Are there any problems with the data? 3. How does the data help answer the business question?

B. Key Tasks:

1. Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016.
   *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDay_merged.csv -dailyActivity_merged.csv

2. Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual ...

This data set contains Preliminary Investigation of Paleoenvironment, Processes, and Carbon Stocks of Drained Thaw-Lake Basins, Arctic Coastal Plain, Alaska data. These data contain measurements taken from 78 drained thaw-lake basins on the Arctic Coastal Plain between Barrow and Atqasuk, Alaska. Investigators collected and analyzed sediment cores to determine soil and vegetation composition, and used Landsat 7+ imagery and degree of basin polygonization to determine the relative age of the basins. Analysis of the cores included pollen, radiocarbon, and organic carbon subsampling. This project was part of an effort to determine the amount of carbon sequestered in drained basins, to determine changes in carbon accumulation rates over time, and to understand the influence of climate on the geomorphological evolution of lake basins on the Arctic Coastal Plain.

This is version 2.0.2.2017p of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.1.2016f to include 2017 and so spans 1931-2017. These data include an update to the station selected and contain 8103 stations. These are the preliminary data for this version, a finalised version will be released in a few months with any station updates. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20171231_v2-0-2-2017p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp