Why Data Needs more Attention in Architecture Design - Experiences from Prototyping a Large-Scale Mobile App Ecosystem

2015 ◽  
Author(s):  
Matthias Naab ◽  
Susanne Braun ◽  
Torsten Lenhart ◽  
Steffen Hess ◽  
Andreas Eitel ◽  
...  
Keyword(s):  
Large Scale ◽  
Mobile App ◽  
Architecture Design

scholarly journals Incorporating Contextual Information into Personalized Mobile Applications Recommendation

2021 ◽  
Author(s):  
Ke Zhu ◽  
Yingyuan Xiao ◽  
Wenguang Zheng ◽  
Xu Jiao ◽  
Chenchen Sun ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Large Scale ◽  
Contextual Information ◽  
Mobile App ◽  
Mobile Internet ◽  
Current Time ◽  
Spatiotemporal Information ◽  
Data Overload ◽  
Effect Experiment ◽  
Contextual Characteristic

Abstract With the rise of the mobile internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. App has the functional exclusiveness feature, which means the target users will not reuse apps with the same function in a certain spatiotemporal information. Most of the existing recommended methods for apps ignore the functional exclusiveness feature which makes it difficult to further improve the recommendation performance of the app recommendation. To solve this problem, we aim to improve the app recommendation performance, and propose a Personalized Context-aware Mobile App Recommendation Approach, called PCMARA. PCMARA comprehensively considers the user and app contextual information, which can mine the users app usage preference effectively. Specifically, (1) PCMARA explores the contextual characteristic of app, and constructs the app contextual factors for app which represent the function of app. (2) For the app functional exclusiveness problem, PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate this problem. (3) PCMARA considers the contextual information of users and apps to generates a recommendation list for target users based on the target users' current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results show that the recommendation effect of PCMARA is satisfactory.

scholarly journals Implementing an Energy Calculator in a Mobile Based Application for Solar Potential Measurement

2018 ◽  
Vol 7 (3.6) ◽  
pp. 403
Author(s):  
S Mohana Krishnan ◽  
Saurav Rawat ◽  
M Surender ◽  
R Balakrishna ◽  
R Anandan
Keyword(s):  
Renewable Energy ◽  
Solar Radiation ◽  
Large Scale ◽  
Resource Assessment ◽  
Mobile App ◽  
Solar Photovoltaic ◽  
Energy Monitoring ◽  
Potential Measurement ◽  
Quick Estimate ◽  
Renewable Energy Generation

Solar photovoltaic (PV) technology has matured to become a technically viable large scale source of sustainable energy. Understanding the rooftop PV potential is critical for utility planning, accommodating grid capacity, deploying financing schemes and formulating future adaptive energy policies. The NIWE (National Institute of Wind Energy) under MNRE (Ministry of New and Renewable Energy) is an esteemed institute dedicated to Indian wind and solar renewable energy generation and monitoring. The SRRA (Solar Radiation and Resource Assessment) is a division under NIWE that is responsible for solar energy monitoring throughout India. They have created the Solar Radiation Map of India using high quality, ground measured solar data. This asks the question, whether it is possible to get a quick estimate of a solar installation. Thus, the paper explains the problems in the field of solar potential measurement and the deployment of a calculator in a mobile front platform. The mobile app would quickly and effortlessly give a rough estimate on what a solar installation could save in power consumption costs.  

scholarly journals Quantifying Drosophila adults with the use of a smartphone

Biology Open ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. bio054452 ◽  
Author(s):  
Evgenia K. Karpova ◽  
Evgenii G. Komyshev ◽  
Mikhail A. Genaev ◽  
Natalya V. Adonyeva ◽  
Dmitry A. Afonnikov ◽  
...  
Keyword(s):  
Mobile Application ◽  
Quantitative Estimate ◽  
Large Scale ◽  
High Accuracy ◽  
Mobile App ◽  
Average Error ◽  
Lighting Conditions ◽  
Manual Counting ◽  
Long Time ◽  
Imaging Conditions

ABSTRACTA method for automation of imago quantifying and fecundity assessment in Drosophila with the use of mobile devices running Android operating system is proposed. The traditional manual method of counting the progeny takes a long time and limits the opportunity of making large-scale experiments. Thus, the development of computerized methods that would allow us to automatically make a quantitative estimate of Drosophilamelanogaster fecundity is an urgent requirement. We offer a modification of the mobile application SeedCounter that analyzes images of objects placed on a standard sheet of paper for an automatic calculation of D. melanogaster offspring or quantification of adult flies in any other kind of experiment. The relative average error in estimates of the number of flies by mobile app is about 2% in comparison with the manual counting and the processing time is six times shorter. Study of the effects of imaging conditions on accuracy of flies counting showed that lighting conditions do not significantly affect this parameter, and higher accuracy can be achieved using high-resolution smartphone cameras (8 Mpx and more). These results indicate the high accuracy and efficiency of the method suggested.This article has an associated First Person interview with the first author of the paper.

Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks

2020 ◽  
Vol 23 (4) ◽  
pp. 2363-2389
Author(s):  
Xiaofei Wang ◽  
Chenyang Wang ◽  
Xu Chen ◽  
Xiaoming Fu ◽  
Jinyoung Han ◽  
...  
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Mobile App ◽  
Mobile Social Networks ◽  
Measurement And Analysis ◽  
Device To Device

scholarly journals Real-world longitudinal data collected from the SleepHealth mobile app study

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sean Deering ◽  
Abhishek Pratap ◽  
Christine Suver ◽  
A. Joseph Borelli ◽  
Adam Amdur ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Digital Health ◽  
The United States ◽  
Mobile App ◽  
Environmental Data ◽  
Sleep Habits ◽  
Real World Data ◽  
Disease Manifestation ◽  
Novel Approach

AbstractConducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide.

Offloading as a Service Middleware for Mobile Cloud Apps

2020 ◽  
Vol 10 (2) ◽  
pp. 36-55 ◽  
Author(s):  
Hamid A Jadad ◽  
Abderezak Touzene ◽  
Khaled Day
Keyword(s):  
Large Scale ◽  
Lower Cost ◽  
Performance Metrics ◽  
State Of The Art ◽  
Mobile App ◽  
Minimum Number ◽  
Computing Platforms ◽  
Load Balancing Algorithm ◽  
The Cost ◽  
Multiple Load

Recently, much research has focused on the improvement of mobile app performance and their power optimization, by offloading computation from mobile devices to public cloud computing platforms. However, the scalability of these offloading services on a large scale is still a challenge. This article describes a solution to this scalability problem by proposing a middleware that provides offloading as a service (OAS) to large-scale implementation of mobile users and apps. The proposed middleware OAS uses adaptive VM allocation and deallocation algorithms based on a CPU rate prediction model. Furthermore, it dynamically schedules the requests using a load-balancing algorithm to ensure meeting QoS requirements at a lower cost. The authors have tested the proposed algorithm by conducting multiple simulations and compared our results with state-of-the-art algorithms based on various performance metrics under multiple load conditions. The results show that OAS achieves better response time with a minimum number of VMs and reduces 50% of the cost compared to existing approaches.

iTrade

2015 ◽  
Vol 11 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Yong Hu ◽  
Xiangzhou Zhang ◽  
Bin Feng ◽  
Kang Xie ◽  
Mei Liu
Keyword(s):  
Stock Market ◽  
Large Scale ◽  
Concept Drift ◽  
Composite Index ◽  
Performance Comparison ◽  
Mobile App ◽  
Data Driven ◽  
Support Vector ◽  
Individual Investors ◽  
Chinese Stock Market

Among all investors in the Chinese stock market, more than 95% are non-professional individual investors. These individual investors are in great need of mobile apps that can provide professional and handy trading analysis and decision support everywhere. However, financial data is challenging to analyze because of its large-scale, non-linear and noisy characteristics in a varying stock environment. This paper develops a Mobile Data-Driven Stock Trading System (iTrade), which is a mobile app system based on Client-Server architecture and various data mining techniques. The iTrade is characterized by 1) a data-driven intelligent learning model, which can provide further insight compared to empirical technical analysis, 2) a concept drift adaptation process, which facilitates the model adaptation to market structure changes, and 3) a rigorous benchmark analysis, including the Buy-and-Hold strategy and the strategies of three world-famous master investors (e.g., Warren E. Buffett). Technologies used in iTrade include the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, Support Vector Machine (SVM) and risk-adjusted portfolio optimization. An application case of iTrade is presented, which is based on a seven-year (2005-2011) back-testing. Evaluation results indicated that iTrade could gain much higher cumulative return compared to the benchmark (Shanghai Composite Index). To the best of our knowledge, this is the first study and mobile app system that emphasizes and investigates the concept drift phenomenon in stock market, as well as the performance comparison between data-driven intelligent model and strategies of master investors.

Performance Testing of Mobile Applications on Smartphones

2014 ◽  
Vol 5 (4) ◽  
pp. 36-47
Author(s):  
Abdurhman Albasir ◽  
Valuppillai Mahinthan ◽  
Kshirasagar Naik ◽  
Abdulhakim Abogharaf ◽  
Nishith Goel ◽  
...  
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Performance Testing ◽  
Energy Performance ◽  
Mobile App ◽  
Battery Life ◽  
Internet Service ◽  
Proper Design ◽  
Efficient Video ◽  
Test Architecture

Smartphones became the preferred means of communication among users due to the availability of thousands of applications (apps). Although the hardware and software capabilities of smartphones are on the rise, the apps are primarily constrained by the wireless bandwidth and battery life. In this paper, the authors present a test architecture to: (i) evaluate the energy performance of two different designs of the same mobile app service; and (ii) evaluate the bandwidth and energy impacts of advertisements (ads) on smartphones. The authors' measurements on two video players show that, the proper design results a more energy efficient video players. Next, they compare the bandwidth and energy performance news and magazine websites with ads and without ads. In some cases, ads bandwidth cost reaches 50%, whereas ads energy cost reaches 17.8%. The authors also identified the challenges in reliably performing such tests on a large scale. App developers, users, manufacturers, and Internet Service Providers will benefit from this research.

Large-Scale Research via App Stores

2015 ◽  
pp. 269-292
Author(s):  
Matthias Kranz ◽  
Andreas Möller ◽  
Florian Michahelles
Keyword(s):  
Data Collection ◽  
Human Computer Interaction ◽  
Real World ◽  
Mobile Applications ◽  
Large Scale ◽  
Mobile App ◽  
Lessons Learned ◽  
Conducting Research ◽  
Collection Methods

Large-scale research has gained momentum in the context of Mobile Human-Computer Interaction (Mobile HCI), as many aspects of mobile app usage can only be evaluated in the real world. In this chapter, we present findings on the challenges of research in the large via app stores, in conjunction with selected data collection methods (logging, self-reporting) we identified and have proven as useful in our research. As a case study, we investigated the adoption of NFC technology, based on a gamification approach. We therefore describe the development of the game NFC Heroes involving two release cycles. We conclude with lessons learned and provide recommendations for conducting research in the large for mobile applications.

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