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This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.
Dataset Specifications:
Last crawled:
(This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)Richness of Detail (11 Comprehensive Fields):
Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:
Review Content:
review
: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.title
: The title given to the review by the user, often summarizing their main point.isEdited
: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.Reviewer & Rating Information:
username
: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).rating
: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.App & Origin Context:
app_name
: The name of the application being reviewed.app_id
: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.country
: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.Metadata & Timestamps:
_id
: A unique identifier for the specific review record in the dataset.crawled_at
: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).date
: The original date the review was posted by the user on the App Store.Expanded Use Cases & Analytical Applications:
This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:
Product Development & Improvement:
review
text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.review
text to inform future product roadmap decisions and develop features users actively desire.review
field.rating
and sentiment
after new app updates to assess the effectiveness of bug fixes or new features.Market Research & Competitive Intelligence:
Marketing & App Store Optimization (ASO):
review
and title
fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.rating
trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.Academic & Data Science Research:
review
and title
fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.rating
distribution, isEdited
status, and date
to understand user engagement and feedback cycles.country
-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.
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The app analytics market, valued at $7.29 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.09% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of mobile applications across diverse industries, coupled with the rising need for businesses to understand user behavior and optimize app performance, fuels the demand for sophisticated analytics solutions. Furthermore, advancements in data analytics technologies, including artificial intelligence (AI) and machine learning (ML), are enabling more insightful and actionable data analysis, further propelling market expansion. The diverse application of app analytics across marketing/advertising, revenue generation, and in-app performance monitoring across various sectors like BFSI, e-commerce, media, travel and tourism, and IT and telecom significantly contributes to this growth. The market is segmented by deployment (mobile apps and website/desktop apps) and end-user industry, with mobile app analytics currently dominating due to the widespread adoption of smartphones. The competitive landscape is characterized by a mix of established technology giants like Google and Amazon alongside specialized app analytics providers like AppsFlyer and Mixpanel. These companies are continuously innovating, integrating new technologies, and expanding their product offerings to cater to the evolving needs of businesses. While the North American market currently holds a significant share, the Asia-Pacific region is expected to witness substantial growth in the coming years driven by increasing smartphone penetration and digitalization initiatives. However, factors like data privacy concerns and the rising complexity of integrating various analytics tools could pose challenges to market growth. Nonetheless, the overall outlook for the app analytics market remains positive, indicating substantial opportunities for players across the value chain. Recent developments include: June 2024 - Comscore and Kochava unveiled an innovative performance media measurement solution, providing marketers with enhanced insights. This cutting-edge cross-screen solution empowers marketers to understand better how linear TV ad campaigns impact both online and offline actions. By integrating Comscore’s Exact Commercial Ratings (ECR) data with Kochava’s sophisticated marketing mix modeling, the solution facilitates the measurement of crucial metrics, including mobile app activities (such as installs and in-app purchases) and website interactions., June 2024 - AppsFlyer announced its integration of the Data Collaboration Platform with Start.io, an omnichannel advertising platform that focuses on real-time mobile audiences for publishers. Through this collaboration, businesses leveraging the AppsFlyer Data Collaboration Platform can merge their Start.io data with campaign metrics and audience insights, creating a more comprehensive dataset for precise audience targeting.. Key drivers for this market are: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Potential restraints include: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Notable trends are: Media and Entertainment Industry Expected to Capture Significant Share.
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License information was derived automatically
Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.
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This replication package contains the datasets and evaluation results for the research titled "Unveiling Competition Dynamics in Mobile App Markets through User Reviews", by Quim Motger, Xavier Franch, Vincenzo Gervasi and Jordi Marco. Latest version of the full code is available at: https://github.com/quim-motger/app-market-analysis
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NoSQL Database Market was valued at $9.38 Billion in 2023, and is projected to reach $USD 86.48 Billion by 2032, at a CAGR of 28% from 2023 to 2032.
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We will create a customized phones dataset tailored to your specific requirements. Data points may include brand names, model specifications, pricing information, release dates, market availability, feature sets, and other relevant metrics.
Utilize our phones datasets for a variety of applications to boost strategic planning and market analysis. Analyzing these datasets can help organizations grasp consumer preferences and technological trends within the mobile phone industry, allowing for more precise product development and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.
Popular use cases include: enhancing competitive benchmarking, identifying pricing trends, and optimizing product portfolios.
According to our latest research, the global marketing analytics market size in 2024 stands at USD 5.8 billion, demonstrating robust momentum driven by the increasing adoption of data-driven decision-making across industries. The market is projected to register a CAGR of 13.2% from 2025 to 2033, reaching an estimated market size of USD 17.1 billion by 2033. This accelerated growth is primarily attributed to the proliferation of digital channels, the surge in big data, and the imperative for organizations to achieve higher ROI from their marketing investments. The marketing analytics market is evolving rapidly, with advanced analytics tools enabling businesses to gain actionable insights, optimize campaigns, and enhance customer engagement across diverse sectors.
One of the most significant growth factors for the marketing analytics market is the exponential increase in data generation from multiple digital touchpoints. The rise of omnichannel marketing strategies has resulted in vast and complex datasets, encompassing customer interactions from social media, websites, mobile applications, and email campaigns. Businesses are increasingly leveraging marketing analytics solutions to aggregate, process, and analyze this data in real time, gaining deeper insights into customer behavior, preferences, and purchase patterns. The ability to transform raw data into actionable intelligence is empowering marketers to personalize campaigns, improve targeting accuracy, and maximize conversion rates, thereby fueling the demand for sophisticated analytics platforms.
Another critical driver is the growing emphasis on measuring marketing effectiveness and optimizing marketing spend. As organizations face mounting pressure to justify marketing budgets and demonstrate tangible ROI, marketing analytics tools have become indispensable. These solutions enable marketers to track key performance indicators (KPIs), attribute revenue to specific channels, and identify underperforming campaigns. The integration of artificial intelligence and machine learning into marketing analytics platforms is further enhancing predictive capabilities, allowing businesses to forecast trends, automate campaign adjustments, and refine customer segmentation. This technological evolution is driving widespread adoption across both large enterprises and small and medium businesses.
The surge in regulatory requirements and data privacy concerns is also shaping the marketing analytics market. With the implementation of stringent data protection regulations such as GDPR and CCPA, organizations are compelled to adopt analytics solutions that ensure compliance while maintaining data integrity and security. Modern marketing analytics platforms are incorporating advanced data governance features, encryption, and anonymization techniques, enabling businesses to harness the power of analytics without compromising customer trust. This focus on compliance, coupled with the increasing need for transparency in marketing practices, is accelerating the adoption of analytics tools across regulated industries such as BFSI and healthcare.
Regionally, North America dominates the marketing analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to the presence of major analytics vendors, high digital adoption, and substantial marketing expenditure by enterprises. However, the Asia Pacific region is poised for the fastest growth over the forecast period, driven by rapid digital transformation, expanding e-commerce ecosystems, and increasing investments in marketing technology. Latin America and the Middle East & Africa are also witnessing steady growth as organizations in these regions recognize the strategic value of data-driven marketing.
The marketing analytics market is segmented by component into software and services, each playing a vital role in the overall ecosystem. The software segment dominates th
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Mobile Application Development Platform Market is estimated to be USD 87153.5 Million by 2030 with a CAGR of 26.0% during the forecast period
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Global Cloud Mobile Backend as a Service (BaaS) Market size was $3.0 Billion in 2022 and is slated to hit $7.3 Billion by the end of 2030 with a CAGR of nearly 24.1%.
The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.
What is Big data?
Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.
Big data analytics
Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.
US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.
Dataset Highlights:
Taxonomy Data:
Data Updates:
Use Cases:
Data Quality and Reliability:
Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.
Ideal for:
Why Choose This Dataset?
By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.
Summary:
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The global graph database market size reached USD 2.0 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 8.6 Billion by 2033, exhibiting a growth rate (CAGR) of 17.57% during 2025-2033. The increasing adoption of graph databases in cybersecurity for threat detection and network analysis, growing demand for real-time analytics and AI-driven insights, and expanding application in industries, such as healthcare and finance, for data integration and personalized services, are some of the key factors catalyzing the market growth.
Report Attribute
| Key Statistics |
---|---|
Base Year
| 2024 |
Forecast Years
| 2025-2033 |
Historical Years
|
2019-2024
|
Market Size in 2024 | USD 2.0 Billion |
Market Forecast in 2033 | USD 8.6 Billion |
Market Growth Rate 2025-2033 | 17.57% |
IMARC Group provides an analysis of the key trends in each segment of the global graph database market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, type of database, analysis type, deployment model, application, and industry vertical.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions. Hence, the dataset can be used for interaction prediction and building a recommendation system. Furthermore, the data forms a dynamic network of interactions, and we can also perform network representation learning on the nodes in the network, which are users and applications.
Data Creation
The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student at Sharif University of Technology at the time. The data team focused on a two-week period and randomly sampled 1/3 of the users with interactions during that period. They then selected install and update interactions for three months before and after the two-week period, resulting in interactions spanning about 6 months and two weeks.
We further subsampled and cleaned the data to focus on application download interactions. We identified the top 8000 most installed applications and selected interactions related to them. We retained users with more than 32 interactions, resulting in 280,391 users. From this group, we randomly selected 10,000 users, and the data was filtered to include only interactions for these users. The detailed procedure can be found in here.
Data Structure
The dataset has two main files.
myket.csv
: This file contains the interaction information and follows the same format as the datasets used in the "JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks" (ACM SIGKDD 2019) project. However, this data does not contain state labels and interaction features, resulting in associated columns being all zero.app_info_sample.csv
: This file comprises features associated with applications present in the sample. For each individual application, information such as the approximate number of installs, average rating, count of ratings, and category are included. These features provide insights into the applications present in the dataset.Dataset Details
For a detailed summary of the data's statistics, including information on users, applications, and interactions, please refer to the Python notebook available at summary-stats.ipynb. The notebook provides an overview of the dataset's characteristics and can be helpful for understanding the data's structure before using it for research or analysis.
Top 20 Most Installed Applications
Package Name | Count of Interactions |
---|---|
com.instagram.android | 15292 |
ir.resaneh1.iptv | 12143 |
com.tencent.ig | 7919 |
com.ForgeGames.SpecialForcesGroup2 | 7797 |
ir.nomogame.ClutchGame | 6193 |
com.dts.freefireth | 6041 |
com.whatsapp | 5876 |
com.supercell.clashofclans | 5817 |
com.mojang.minecraftpe | 5649 |
com.lenovo.anyshare.gps | 5076 |
ir.medu.shad | 4673 |
com.firsttouchgames.dls3 | 4641 |
com.activision.callofduty.shooter | 4357 |
com.tencent.iglite | 4126 |
com.aparat | 3598 |
com.kiloo.subwaysurf | 3135 |
com.supercell.clashroyale | 2793 |
co.palang.QuizOfKings | 2589 |
com.nazdika.app | 2436 |
com.digikala | 2413 |
Comparison with SNAP Datasets
The Myket dataset introduced in this repository exhibits distinct characteristics compared to the real-world datasets used by the project. The table below provides a comparative overview of the key dataset characteristics:
Dataset | #Users | #Items | #Interactions | Average Interactions per User | Average Unique Items per User |
---|---|---|---|---|---|
Myket | 10,000 | 7,988 | 694,121 | 69.4 | 54.6 |
LastFM | 980 | 1,000 | 1,293,103 | 1,319.5 | 158.2 |
10,000 | 984 | 672,447 | 67.2 | 7.9 | |
Wikipedia | 8,227 | 1,000 | 157,474 | 19.1 | 2.2 |
MOOC | 7,047 | 97 | 411,749 | 58.4 | 25.3 |
The Myket dataset stands out by having an ample number of both users and items, highlighting its relevance for real-world, large-scale applications. Unlike LastFM, Reddit, and Wikipedia datasets, where users exhibit repetitive item interactions, the Myket dataset contains a comparatively lower amount of repetitive interactions. This unique characteristic reflects the diverse nature of user behaviors in the Android application market environment.
Citation
If you use this dataset in your research, please cite the following preprint:
@misc{loghmani2023effect,
title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks},
author={Erfan Loghmani and MohammadAmin Fazli},
year={2023},
eprint={2308.06862},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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The global low code development market is approximated at a value of US$ 22.5 billion in 2024 and is calculated to increase at a CAGR of 26.8% to reach US$ 241.9 billion by the end of 2034.
Report Attribute | Detail |
---|---|
Low Code Development Market Size (2024E) | US$ 22.5 Billion |
Forecasted Market Value (2034F) | US$ 241.9 Billion |
Global Market Growth Rate (2024 to 2034) | 26.8% CAGR |
South Korea Market Value (2034F) | US$ 13.1 Billion |
On-premise Demand Growth Rate (2024 to 2034) | 24.9% CAGR |
Key Companies Profiled | Mendix Technology BV; Zoho Corporation Pvt. Ltd.; Kintonne; Appian Corporation; Microsoft Corporation; Salesforce.com, Inc.; NewGen; AuraQuantic; Oracle Corporation; Pegasystems Inc.; ServiceNow Inc.; Creatio; Quick Base; Betty Blocks; TrackVia; OutSystems Inc. |
Country-wise Analysis
Attribute | United States |
---|---|
Market Value (2024E) | US$ 2.5 Billion |
Growth Rate (2024 to 2034) | 26.7% CAGR |
Projected Value (2034F) | US$ 26.7 Billion |
Attribute | China |
---|---|
Market Value (2024E) | US$ 2.5 Billion |
Growth Rate (2024 to 2034) | 26.7% CAGR |
Projected Value (2034F) | US$ 27 Billion |
Category-wise Analysis
Attribute | BFSI |
---|---|
Segment Value (2024E) | US$ 4.5 Billion |
Growth Rate (2024 to 2034) | 27.8% CAGR |
Projected Value (2034F) | US$ 52.2 Billion |
Attribute | Cloud-based Low Code Development Platforms |
---|---|
Segment Value (2024E) | US$ 14.6 Billion |
Growth Rate (2024 to 2034) | 27.7% CAGR |
Projected Value (2034F) | US$ 169.3 Billion |
Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions. Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth). Demography: sex, age; federal state; highest level of school education; highest level of vocational education. Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.
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The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.
One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.
Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.
The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.
In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.
From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.
Graph Database Market Size 2025-2029
The graph database market size is forecast to increase by USD 11.24 billion at a CAGR of 29% between 2024 and 2029.
The market is experiencing significant growth, driven by the increasing popularity of open knowledge networks and the rising demand for low-latency query processing. These trends reflect the growing importance of real-time data analytics and the need for more complex data relationships to be managed effectively. However, the market also faces challenges, including the lack of standardization and programming flexibility. These obstacles require innovative solutions from market participants to ensure interoperability and ease of use for businesses looking to adopt graph databases.
Companies seeking to capitalize on market opportunities must focus on addressing these challenges while also offering advanced features and strong performance to differentiate themselves. Effective navigation of these dynamics will be crucial for success in the evolving graph database landscape. Compliance requirements and data privacy regulations drive the need for security access control and data anonymization methods. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures.
What will be the Size of the Graph Database Market during the forecast period?
Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, security and data management are increasingly prioritized. Authorization mechanisms and encryption techniques ensure data access control and confidentiality. Query optimization strategies and indexing enhance query performance, while data anonymization methods protect sensitive information. Fault tolerance mechanisms and data governance frameworks maintain data availability and compliance with regulations. Data quality assessment and consistency checks address data integrity issues, and authentication protocols secure concurrent graph updates. This model is particularly well-suited for applications in social networks, recommendation engines, and business processes that require real-time analytics and visualization.
Graph database tuning and monitoring optimize hardware resource usage and detect performance bottlenecks. Data recovery procedures and replication methods ensure data availability during disasters and maintain data consistency. Data version control and concurrent graph updates address versioning and conflict resolution challenges. Data anomaly detection and consistency checks maintain data accuracy and reliability. Distributed transactions and data recovery procedures ensure data consistency across nodes in a distributed graph database system.
How is this Graph Database Industry segmented?
The graph database 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.
End-user
Large enterprises
SMEs
Type
RDF
LPG
Solution
Native graph database
Knowledge graph engines
Graph processing engines
Graph extension
Geography
North America
US
Canada
Europe
France
Germany
Italy
Spain
UK
APAC
China
India
Japan
Rest of World (ROW)
By End-user Insights
The Large enterprises segment is estimated to witness significant growth during the forecast period. In today's business landscape, large enterprises are turning to graph databases to manage intricate data relationships and improve decision-making processes. Graph databases offer unique advantages over traditional relational databases, enabling superior agility in modeling and querying interconnected data. These systems are particularly valuable for applications such as fraud detection, supply chain optimization, customer 360 views, and network analysis. Graph databases provide the scalability and performance required to handle large, dynamic datasets and uncover hidden patterns and insights in real time. Their support for advanced analytics and AI-driven applications further bolsters their role in enterprise digital transformation strategies. Additionally, their flexibility and integration capabilities make them well-suited for deployment in hybrid and multi-cloud environments.
Graph databases offer various features that cater to diverse business needs. Data lineage tracking ensures accountability and transparency, while graph analytics engines provide advanced insights. Graph database benchmarking helps organizations evaluate performance, and relationship property indexing streamlines data access. Node relationship management facilitates complex data modeling, an
TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?
Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.
Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:
Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed
Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:
Digital Marketing and Advertising:
Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking
E-commerce and Retail:
Customer journey mapping Product recommendation enhancements Cart abandonment analysis
Media and Entertainment:
Content consumption trends Audience engagement metrics Cross-platform user behavior analysis
Financial Services:
Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis
Technology and Software:
User experience optimization Feature adoption tracking Competitive intelligence
Market Research and Consulting:
Consumer behavior studies Industry trend analysis Digital transformation strategies
Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:
Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.
By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:
Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.
Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...
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The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.
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BASE YEAR | 2024 |
HISTORICAL DATA | 2019 - 2024 |
REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
MARKET SIZE 2023 | 4.51(USD Billion) |
MARKET SIZE 2024 | 5.46(USD Billion) |
MARKET SIZE 2032 | 25.2(USD Billion) |
SEGMENTS COVERED | Application ,Technology ,Dataset ,Type of Action ,Deployment Model ,Regional |
COUNTRIES COVERED | North America, Europe, APAC, South America, MEA |
KEY MARKET DYNAMICS | 1 Growing adoption of AI 2 Increased demand for computer vision solutions 3 Rising concerns over data privacy and security 4 Advancements in deep learning algorithms 5 Surge in investment for development of advanced HAR systems |
MARKET FORECAST UNITS | USD Billion |
KEY COMPANIES PROFILED | NXP Semiconductors ,Intel ,Rohm ,Infineon ,Texas Instruments ,ON Semiconductor ,Wolfspeed ,Qualcomm ,Analog Devices ,Microchip Technology ,Renesas ,Mitsubishi ,Toshiba ,NVIDIA ,STMicroelectronics |
MARKET FORECAST PERIOD | 2024 - 2032 |
KEY MARKET OPPORTUNITIES | 1 Healthcare Remote patient monitoring medical diagnosis assistance 2 Surveillance Enhanced security crime prevention 3 Sports Motion analysis performance optimization 4 Entertainment Immersive gaming virtual reality applications 5 Robotics Humanrobot interaction navigation |
COMPOUND ANNUAL GROWTH RATE (CAGR) | 21.06% (2024 - 2032) |
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This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.
Dataset Specifications:
Last crawled:
(This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)Richness of Detail (11 Comprehensive Fields):
Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:
Review Content:
review
: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.title
: The title given to the review by the user, often summarizing their main point.isEdited
: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.Reviewer & Rating Information:
username
: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).rating
: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.App & Origin Context:
app_name
: The name of the application being reviewed.app_id
: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.country
: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.Metadata & Timestamps:
_id
: A unique identifier for the specific review record in the dataset.crawled_at
: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).date
: The original date the review was posted by the user on the App Store.Expanded Use Cases & Analytical Applications:
This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:
Product Development & Improvement:
review
text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.review
text to inform future product roadmap decisions and develop features users actively desire.review
field.rating
and sentiment
after new app updates to assess the effectiveness of bug fixes or new features.Market Research & Competitive Intelligence:
Marketing & App Store Optimization (ASO):
review
and title
fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.rating
trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.Academic & Data Science Research:
review
and title
fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.rating
distribution, isEdited
status, and date
to understand user engagement and feedback cycles.country
-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.