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  1. Proceedings of the 2nd Workshop on Experiences in the Design and Implementation of Smart Objects (SmartObjects '16)
  2. RECKON: an analytics framework for app developers
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A smart spa: having fun with physical activities
Determining the relative position of vehicles considering bidirectional traffic scenarios in VANETS
Cloud storage for mobile users using pre-positioned storage facilities
Enhancing compressive sensing encryption in constrained devices using chaotic sequences
RECKON: an analytics framework for app developers
DataTweet for user-centric and geo-centric IoT communications
Low-cost, low-power and long-range image sensor for visual surveillance
Abstractions and solutions to support smart-objects in the future internet
LuxLeak: capturing computing activity using smart device ambient light sensors
Data-oriented abstraction of virtual sensors for energy-aware embedded software systems

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RECKON: an analytics framework for app developers

Content Provider ACM Digital Library
Author Jain, Puneet Parate, Abhinav Kim, Kyu-Han
Abstract This paper argues that there is a need to expand the capabilities of mobile app analytics beyond providing low-level insights about how efficiently a user can execute an action on a mobile app, to deeper insights about how efficiently a user can complete a task using the app (e.g., flight reservation). This paper presents RECKON, a framework for mobile app analytics, that provides insights about end-to-end user experience in completing tasks, without explicit effort from the app developers. RECKON identifies and extracts task-level information from an unlabeled data stream of user actions. Based on the extracted information, RECKON outputs various helpful metrics to the developers. We have implemented and evaluated RECKON with a popular travel assistant app with 122, 243 users for 30 days. Our evaluation results validate RECKON's performance in obtaining critical mobile app insights as well as demonstrate its wide applicability for developers.
Starting Page 23
Ending Page 28
Page Count 6
File Format PDF EPUB
ISBN 9781450342544
DOI 10.1145/2980147.2980154
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-03
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Task User interface App analytics Developer
Content Type Text
Resource Type Article
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