Jerry Gao

Building a Big Data Analytics Service Framework for Mobile Advertising and Marketing

The unprecedented growth in mobile device adoption and the rapid advancement of mobile technologies & wireless networks have created new opportunities in mobile marketing and adverting. The opportunities for Mobile Marketers and Advertisers include real-time customer engagement, improve customer experience, build brand loyalty, increase revenues, and drive customer satisfaction. The challenges, however, for the Marketers and Advertisers include how to analyze troves of data that mobile devices emit and how to derive customer engagement insights from the mobile data. This research paper addresses the challenge by developing Big Data Mobile Marketing analytics and advertising recommendation framework. The proposed framework supports both offline and online advertising operations in which the selected analytics techniques are used to provide advertising recommendations based on collected Big Data on mobile users profiles, access behaviors, and mobility patterns. The paper presents prototyping solution design as well as its application and certain experimental results.

Big Data Sensing and Service: A Tutorial

As the advance of the Internet of Things (IoT), more M2M sensors and devices are connected to the Internet. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study. This paper is written as a tutorial paper by providing the informative concepts and taxonomy on big data sensing and services. The paper not only discusses the motivation, research scope, and features of big data sensing and services, but also exams the required services in big data sensing based on the state-of-the-art research work. Moreover, the paper discusses big data sensing challenges, issues, and needs.

On building a cloud-based mobile testing infrastructure service system

The paper described a developed mobile infrastructure service system (MTaaS).The paper provides informative perspectives and issues on cloud-based mobile TaaS.The paper gives a comparison between MTaaS and current major mobile TaaS players. With the rapid advance of mobile computing, cloud computing and wireless network, there is a significant increasing number of mobile subscriptions. This brings new business requirements and demands in mobile testing service, and causes new issues and challenges. In this paper, informative discussions about cloud-based mobile testing-as-a-service (mobile TaaS) are offered, including the essential concepts, focuses, test process, and the expected testing infrastructures. To address the need of infrastructure level service for mobile TaaS, this paper presents a developed system known as MTaaS to provide an infrastructure-as-a-service (IaaS) for mobile testing, in order to indicate the feasibility and effectiveness of cloud-based mobile testing service. In addition, the paper presents a comparison among cloud-based mobile TaaS approaches and several best practices in industry are discussed. Finally, the primary issues, challenges, and needs existed in current mobile TaaS are analyzed.

Cloud-Based Infrastructure for Mobile Testing as a Service

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions. This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile testing service. In this paper, informative and insightful discussions about cloud-based mobile testing-as-a-service (Mobile TaaS) are offered, including the essential concepts, focuses, and the expected testing infrastructures. Currently, there lack of well-defined infrastructures and approaches which allow both mobile application vendors and users to access for Mobile TaaS services. A suitable testing infrastructure is required which allows end users to submit on-demand service requests for mobile TaaS resources in order to form an infrastructure for mobile testing. Therefore, this paper introduces an approach to setup an cloud-based infrastructure as a service for Mobile TaaS for addressing the major challenges faced by test engineers in testing mobile applications. Finally, the paper reports case studies to indicate the feasibility and effectiveness of the proposed approach.

On Building Test Automation System for Mobile Applications Using GUI Ripping.

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions. This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile testing and automation. Current existing mobile application testing tools mostly concentrate on GUI, load and performance testing which seldom consider large-scale concurrent automation, coverage analysis, fault tolerance and usage of well-defined models. This paper introduces an implemented system that provides an automation solution across platforms on diverse devices using GUI ripping test scripting technique. Through incorporating open source technologies such as Appium and Selenium Grid, this paper addresses the scalable test automation control with the capability of fault tolerant. Additionally, maximum test coverage can also be obtained by executing parallel test scripts within the model. Finally, the paper reports case studies to indicate the feasibility and effectiveness of the proposed approach.

Cloud-Based Mobile Testing as a Service

With the rapid advance of mobile computing, cloud computing and wireless network, there is a significant increase of mobile subscriptions. This brings new business requirements and demands in mobile testing service, and causes new issues and challenges. In this paper, informative discussions about cloud-based mobile testing-as-a-service (mobile TaaS) are offered, including the essential concepts, focuses, test process, and the expected testing infrastructures. Moreover, the paper presents a comparison among cloud-based mobile TaaS approaches and several best practices in industry are discussed. Futhermore, the primary issues, challenges, and needs are analyzed.

A Quality Evaluation Approach to Search Engines of Shopping Platforms

Search engine is one of the mostly-used big data applications. However, search system varies in different platforms and fields, and there are few approaches to evaluate its quality. Search engine for online shopping systems combines text search and classification-based retrieval. It is more difficult to validate and evaluate quality since there are no definite quality standards or testing methods. Some quality factors and relational indicators are provided in this paper through studying on search system of online shopping platform, Experiments and comparisons on different shopping platforms are also performed to show the quality problems of search engines in shopping platforms. The study results indicate the feasibility and effectiveness of the proposed quality evaluation approach.

Bike-Sharing System: A Big-Data Perspective

Bike-sharing systems, as a green travel way, recently have been widely spreading over 1000 cities around the world. How to plan and optimize such systems receive attention in academia as well as in practice. However, scientific literature about planning, usage prediction, pattern analysis, and system operation in this field is still rather scarce and full of challenges. And the solutions of these articles can hardly meet the increasing the demands of users and management of bike-sharing systems in the big-data era. To this end, a comprehensive literature comparison and analysis has been given focused on four topics. Then, a bike-sharing systems process framework from a big-data analysis perspective is proposed in the paper.

Building a Model-Based GUI Test Automation System for Mobile Applications

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile applications (apps). This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile test automation. Existing mobile application testing approaches mostly concentrate on GUI-based testing, load and performance testing without considering large-scale concurrent mobile app test automation, and model-based test coverage analysis. In this paper, a mobile hierarchical GUI model is proposed to present mobile operation scenario flows and gesture features in a hierarchical manner, in order to facilitate test dependency analysis in test automation. Mobile app test coverage analysis is performed based on GUI ripping models. The paper also presents a developed system that provides a test automation solution using GUI models. Finally, the paper reports a case study to indicate the feasibility and effectiveness of the proposed approach.

A Model-Based Framework to Support Complexity Analysis Service for Regression Testing of Component-Based Software

Today, software components have been widely used in software construction to reduce the cost of project and speed up software development cycle. During software maintenance, various software change approaches can be used to realize specific change requirements of software components. Different change approaches lead to diverse regression testing complexity. Such complexity is one of the key contributors to the cost and effectiveness of software maintenance. However, there is a lack of research work addressing regression testing complexity analysis service for software components. This paper proposes a framework to measure and analyze regression testing complexity based on a set of change and impact complexity models and metrics. The framework can provide services for complexity modeling, complexity factor classification, and regression testing complexity measurements. The initial study results indicate the proposed framework is feasible and effective in measuring the complexity of regression testing for component-based software.