This dataset consists of apps needed permissions during installation and run-time. We collect apps from three different sources google play, third-party apps and malware dataset. This file contains more than 5,00,000 Android apps. features extracted at the time of installation and execution. One file contains the name of the features and others contain .apk file corresponding to it extracted permissions and API calls. Benign apps are collected from Google's play store, hiapk, app china, Android, mumayi , gfan slideme, and pandaapp. These .apk files collected from the last three years continuously and contain 81 distinct malware families.

We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.

😊If You downloaded this dataset or it is useful to You, please upvote it!

Description:

This dataset contains records of events and errors related to the operation of mobile applications on various mobile devices. Each entry includes information about the timestamp, device characteristics, session identifiers, and textual descriptions of events or errors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F72a315b39866c02162b229d5a209f4b4%2F5.png?generation=1695227457850330&alt=media" alt=""> Data Fields: - Status: A numerical indicator of the event status (e.g., 0 for success, 1 for error). - Event: A textual description of the action or event, including error text if an error occurred. - Device Identification: Information about the mobile device, including model and Android version. - App Version: The version of the mobile application experiencing the event. - App Language: The language in which the application is running. - Android Version: The version of the Android operating system on the device. - Session Identifiers: Unique session or device identifiers associated with the event. - Additional Data: Additional event details, such as the country and other characteristics. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fbca8f9b9fb8288e258a59fad5e53ac15%2F4.png?generation=1695227273200372&alt=media" alt="">

🚀 MobileViews: A Large-Scale Mobile GUI Dataset

MobileViews is a large-scale dataset designed to support research on mobile agents and mobile user interface (UI) analysis. The first release, MobileViews-600K, includes over 600,000 mobile UI screenshot-view hierarchy (VH) pairs collected from over 20,000 apps on the Google Play Store. This dataset is based on the DroidBot, which we have optimized for large-scale data collection, capturing more comprehensive interaction details while… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/MobileViews.

Explore our dataset: 117K doctors' mobile app usage data in TSV format for AI healthcare insights. Ideal for analytics and AI development.

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Data used on the Paper: Martin-Domingo, L., & Martin, J. C. (2015). Airport Surface Access and Mobile Apps. Journal of Airline and Airport Management, 5(1), 1–17. http://doi.org/10.3926/jairm.38

As of January 2025, around 89 percent of the data linked to users collected by iOS apps was used by app publishers to integrate their product's functionalities. In comparison, 72 percent of app data not directly linked to users had the same function. Collecting analytics data was the second most common reason for apps to collect iOS users' data, while only 17 percent of identifiable user data and 10 percent of non-identifiable users' data went to improve or integrate third-party advertising services.

Dataset Card for Dataset Name

  Dataset Summary

MobileRec is a large-scale app recommendation dataset. There are 19.3 million user\item interactions. This is a 5-core dataset. User\item interactions are sorted in ascending chronological order. There are 0.7 million users who have had at least five distinct interactions. There are 10173 apps in total.

  Supported Tasks and Leaderboards

Sequential Recommendation

  Languages

English

  How to use the… See the full description on the dataset page: https://huggingface.co/datasets/recmeapp/mobilerec.

As of March 2021, Meta apps Facebook and Instagram were the mobile social apps found to collect the largest amount of data from global iOS users, with 32 data points collected across 14 data segments, respectively. Professionals-oriented social platform LinkedIn followed with 26 data points, while social video app TikTok collected 24 data points from iOS users worldwide.

Object Detection Mobile App

## Overview

Object Detection Mobile App is a dataset for object detection tasks - it contains Objects annotations for 2,255 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ABSTRACT The increasing use of mobile applications have been escalating with the increasing use of smartphones. In the present study, we examine (a) the adoption behavior of mobile apps using the extended TAM framework, and (b) whether adoption leads to subsequent use behavior and switching intentions. Based on data collected from two surveys in India we test the conceptual model of extended TAM and the effects of behavior on switching intentions using factor analysis and structural equation modeling. The major findings indicate a significant effect of most predictor variables on the perceived usefulness and perceived ease of use of apps. Further, we found a significant effect of behavioral intention on use behavior and subsequent switching intentions to apps from computers/laptops.

This dataset is about books. It has 98 rows and is filtered where the book subjects is Mobile apps. It features 9 columns including author, publication date, language, and book publisher.

During the study period

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario B. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.

In 2024, the United States was the leading app market, with the Apple App Store and the Google App Store generating approximately 31 billion U.S. dollars of in-app revenues. China was the second-largest app market, as in-app revenues in the region generated approximately 17.34 billion U.S. dollars. Japan ranked third, as the region generated around 11.25 billion U.S. dollars in app revenues for the examined period.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

Context

This dataset contains reviews for one of the most popular mobile app - tiktok. All the publicly posted reviews are scraped from the google play store.

Inspiration

• The dataset can be used to identify key insights related to the app, key problems/issues people have raised.
• Perform sentiment analysis of the reviews and find what people are talking about.
• Perform topic modeling to identify key topics mentioned in the review over time
• Generate visualizations of different worlds / n-grams / topics extracted from the reviews.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.