A Client MicroServices Automatic Collaboration Framework Based on Fine-Grained APP

Chen, Shizhan; Feng, Zhiyong; Huang, Keman

doi:10.1109/scc.2018.00011

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…• Detailed described studies as the data term level of the third layer in Figure 4 (e.g., "click", "scroll", "text changed", "notification time", "accelerometer", "gyro", "battery level"): [23,24,34,33,25,31,20,21,17,30,28,27,102,87,103,104,18,22,36,121,68,66,73,79,82,83,80,49,57] • Vaguely described studies as the data term level of the first or second layer in Figure 4 (e.g., "all UI events", "sensors", "sensor data", "event type", "user actions", "gesture action", "context events", "context logs"): [35,16,32,140,29,127,72,76] Furthermore, depending on the method of describing the data terms, there can be a difference in the degree of ease in terms of reproducibility for other researchers. The method of describing data terms in 110 number of papers can be divided into two types of studies: First type of studies are described by words or words with categorization, second type of studies are described by words in sentences.…”

Section: Data Term Issues Regarding Reproducibilitymentioning

confidence: 99%

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A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

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Recently, there has been an increase in industrial and academic research on data-driven analytics with smartphones based on the collection of app usage patterns and surrounding context data. The Android mobile operating system utilizes Usage Statistics API (US API) and Accessibility Service API (AS API) as representative APIs to passively collect app usage data. These APIs are used for various research purposes as they can collect app usage patterns (e.g., app status, usage time, app name, user interaction state, and smartphone use state) and fine-grained data (e.g., user interface elements & hierarchy and user interaction type & target & time) of each application. In addition, other sensing APIs help to collect the user's surroundings context (location, network, ambient environment) and device state data, along with AS/US API. In this review, we provide insights on the types of mobile usage and sensor data that can be collected for each research purpose by considering Android built-in APIs and sensors (AS/US API, and other sensing APIs). Moreover, we classify the research purposes of the surveyed papers into four categories and 17 sub-categories, and create a hierarchical structure for data classification, comprising three layers. We present the important trends in the usage of Android's built-in APIs and sensors, including AS/US API, the types of data collected using the presented APIs, and discuss the utilization of mobile usage and sensor data in future research.

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