4,689 research outputs found
Optimizing user experience in choosing android applications
Why is my cell phone battery already low? How did I use almost all the data of my monthly Internet plan? Is my recently released new application more efficient than similar competing applications? These are not easy questions to answer. Different applications implementing similar or identical functionalities may have different energy consumptions.
In the paper associated to this technical report we present a recommendation system aimed at helping users and developers alike. We help users to choose optimal sets of applications belonging to different categories (eg. browsers, e-mails, cameras) while minimizing energy consumption, transmitted data, and maximizing application rating. We also help developers by showing the relative placement of their application's efficiency with respect to selected others. When the optimal set of applications is computed, it is leveraged to position a given application with respect to the optimal, median and worst application in its category (eg. browsers).
Out of eight categories we selected 144 applications, manually defined typical execution scenarios, collected the relevant data, and computed the Pareto optimal front solving a multi-objective optimization problem. We report evidence that, on the one hand, ratings do not correlate with energy efficiency and data frugality. On the other hand, we show that it is possible to help developers understanding how far is a new Android application power consumption and network usage with respect to optimal applications in the same category.
From the user perspective, we show that choosing optimal sets of applications, power consumption and network usage can be reduced by 16.61% and 40.17%, respectively, in comparison to choosing the set of applications that maximizes only the rating.
This document is the technical report associated to the paper "Optimizing User Experience in Choosing Android Applications". Here we extent the original paper answering some questions related to the optimization process and giving all the figures and statistical tests generated in our experiments. Therefore, this document can be considered as an appendix of the original paper
Hikester - the event management application
Today social networks and services are one of the most important part of our
everyday life. Most of the daily activities, such as communicating with
friends, reading news or dating is usually done using social networks. However,
there are activities for which social networks do not yet provide adequate
support. This paper focuses on event management and introduces "Hikester". The
main objective of this service is to provide users with the possibility to
create any event they desire and to invite other users. "Hikester" supports the
creation and management of events like attendance of football matches, quest
rooms, shared train rides or visit of museums in foreign countries. Here we
discuss the project architecture as well as the detailed implementation of the
system components: the recommender system, the spam recognition service and the
parameters optimizer
An App Performance Optimization Advisor for Mobile Device App Marketplaces
On mobile phones, users and developers use apps official marketplaces serving
as repositories of apps. The Google Play Store and Apple Store are the official
marketplaces of Android and Apple products which offer more than a million
apps. Although both repositories offer description of apps, information
concerning performance is not available. Due to the constrained hardware of
mobile devices, users and developers have to meticulously manage the resources
available and they should be given access to performance information about
apps. Even if this information was available, the selection of apps would still
depend on user preferences and it would require a huge cognitive effort to make
optimal decisions. Considering this fact we propose APOA, a recommendation
system which can be implemented in any marketplace for helping users and
developers to compare apps in terms of performance.
APOA uses as input metric values of apps and a set of metrics to optimize. It
solves an optimization problem and it generates optimal sets of apps for
different user's context. We show how APOA works over an Android case study.
Out of 140 apps, we define typical usage scenarios and we collect measurements
of power, CPU, memory, and network usages to demonstrate the benefit of using
APOA.Comment: 18 pages, 8 figure
Comprehension of Ads-supported and Paid Android Applications: Are They Different?
The Android market is a place where developers offer paid and-or free apps to
users. Free apps are interesting to users because they can try them immediately
without incurring a monetary cost. However, free apps often have limited
features and-or contain ads when compared to their paid counterparts. Thus,
users may eventually need to pay to get additional features and-or remove ads.
While paid apps have clear market values, their ads-supported versions are not
entirely free because ads have an impact on performance.
In this paper, first, we perform an exploratory study about ads-supported and
paid apps to understand their differences in terms of implementation and
development process. We analyze 40 Android apps and we observe that (i)
ads-supported apps are preferred by users although paid apps have a better
rating, (ii) developers do not usually offer a paid app without a corresponding
free version, (iii) ads-supported apps usually have more releases and are
released more often than their corresponding paid versions, (iv) there is no a
clear strategy about the way developers set prices of paid apps, (v) paid apps
do not usually include more functionalities than their corresponding
ads-supported versions, (vi) developers do not always remove ad networks in
paid versions of their ads-supported apps, and (vii) paid apps require less
permissions than ads-supported apps. Second, we carry out an experimental study
to compare the performance of ads-supported and paid apps and we propose four
equations to estimate the cost of ads-supported apps. We obtain that (i)
ads-supported apps use more resources than their corresponding paid versions
with statistically significant differences and (ii) paid apps could be
considered a most cost-effective choice for users because their cost can be
amortized in a short period of time, depending on their usage.Comment: Accepted for publication in the proceedings of the IEEE International
Conference on Program Comprehension 201
Game Learning to Optimize Learning in Disaster Area
In terms of earth science, Indonesia is a very interesting area. But Indonesia is also the mostaffectedcountry.OneofthebiggestnaturaldisastersthatstruckIndonesiaregion is the 2004 Aceh Tsunami. This condition causes the destruction of various facilities including infrastructure in the world of education so that often hampers the learning process. Though education is a very valuable investment in human life. Therefore, various developments continue to be made to create various learning alternatives in supporting the education process. The use of technology will help the implementation of the learning process although facilities and infrastructure are minimal. One of the technologies used is the use of smartphones for learning. By using Android-based learning game is expected to be an alternative model of education that is effective, interesting, interactive and fun. Utilization of this android based learning game in addition to optimizing learning can also reduce trauma (trauma healing) in children after the disaster.
Keywords: Android-based Learning Game, Trauma, Optimizing Learning, Disaste
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