Sherin Moussa

Semantic Trajectories: A Survey from Modeling to Application

Trajectory data analysis has recently become an active research area. This is due to the large availability of mobile tracking sensors, such as GPS-enabled smart phones. However, those GPS trackers only provide raw trajectories (x, y, t), ignoring information about the activity, transportation mode, etc. This information can contribute in producing significant knowledge about movements, which transforms raw trajectories into semantic trajectories. Therefore, research lately has focused on semantic trajectories; their representation, construction, and applications. This paper investigates the current studies on semantic trajectories so far. We propose a new classification schema for the research efforts in semantic trajectory construction and applications. The proposed classification schema includes three main classes: semantic trajectory modeling, computation, and applications. Besides, we discuss the current research gaps found in this research area.

Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness.

Cluster-based test cases prioritization and selection technique for agile regression testing

Regression testing repeatedly executes test cases of previous builds to validate that the original features are not affected with any new changes. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress the central role of regression testing in maintaining software quality. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time. In this paper, we present an automated agile regression testing approach on both the sprints and release levels. The proposed approach addresses both weighted sprint test cases prioritization technique, which prioritizes test cases based on several parameters having real practical weight for testers, and Cluster‐based Release Test cases Selection technique that clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text mining techniques. The proposed approach achieves enhancement for both the prioritization and selection of test cases for agile regression testing. Copyright

A Comprehensive Study for Software Testing and Test Cases Generation Paradigms

Software testing is accounted to be an essential part in software development life cycle in terms of cost and manpower, where its total cost is considerable high. Consequently, many studies [48] have been conducted to minimize the associated cost and human effort to fix bugs and errors, and to improve the quality of testing process by automatically generating test cases. Test cases can be generated from different phases (requirement phase, design phase and after development phase). Though, test case generation at early stages is more effective rather than that after development, where time and effort used for finding and fixing errors and bugs is less than that after development. At later stage, fixing errors results in enormous code correction, consuming a lot of time and effort. In this paper, we study the different paradigms of testing techniques for generating test cases, where we investigate their coverage and associated capabilities. We finally propose a preliminary model for a generic automated test cases generation.

An Approach for Erosion and Power Loss Prediction of Wind Turbines Using Big Data Analytics

Due to the huge costs associated with wind energy development, this makes wind farms maintenance and production reliability are of high necessity to ensure sustainability. The continuous evolution of turbines industry has a serious impact on the operation and maintenance costs. Thus, monitoring wind turbines performance and early deterioration prediction are highly required. During the operational life of turbines, some components are persistently exposed to extreme environmental influences that result in their edge erosion. Sensors can be deployed in wind farms to detect such factors, where vast quantities of incomplete, heterogeneous and multi-sourced data are rapidly generated. Hence, wind-related data have been considered as big data that necessitate the intervention of big data analytics for accurate data analysis, which become severely hard to process using traditional approaches. In this paper, we propose the Wind Turbine Erosion Predictor (WTEP) System that uses big data analytics to handle the data volume, variety, and veracity and estimate the turbines erosion rate, in addition to the total power loss. WTEP proposes an optimized flexible multiple regression technique. Experiments show that WTEP achieves high erosion rate prediction accuracy with fast processing time. Thus, it effectively evaluates the accompanied percentage of power loss for wind turbines.

An Approach for Opinion-Demographic-Topology Based Microblog Friend Recommendation.

Through the tremendous increase of users on the microblogging social networks with their associated streams of content, the scarcity of one user’s attention arises. The process of filtering such massive content and discovering who other users could be aligned with his own interests would consume much time. Thus, various mechanisms have been investigated to recommend friends by analyzing the posted content, social graph, or user profiles. In this paper, we propose a new approach for microblog friend recommendation based on the opinion, or sentiment, towards the topics in the microblogs combined with the social graph, in addition to the demographic data available in the user profiles, including age, gender, and location. We have deployed a cloud-based recommender service using R language for big data analytics, which applies our proposed approach to gather feedback from real Twitter users. Results show 0.77 average precision value, with 21 % increase rate considering opinion mining.

A methodology for regression testing reduction and prioritization of agile releases

Regression testing is the type of software testing that seeks to uncover new software bugs in existing areas of a system after changes have been made to them. The significance of regression testing have grown in the past decade with the amplified adoption of agile development methodologies, which requires the execution of regression testing at the end of each release. In this paper, we present an automated agile regression testing approach that reduces the number of test cases to be used at regression phase depending on the similarity of issues exposed from the different test cases, taking into consideration the user story coverage. It then prioritizes the reduced test cases using user-provided weighted agile parameters. The proposed approach achieves enhancement for both the reduction and prioritization of test cases for agile regression testing.

Regression Testing Approach for Large-Scale Systems

Regression testing is an important and expensive activity that is undertaken every time a program is modified to ensure that the changes do not introduce new bugs into previously validated code. Instead of re-running all test cases, different approaches were studied to solve regression testing problems. Data mining techniques are introduced to solve regression testing problems with large-scale systems containing huge sets of test cases, as different data mining techniques were studied to group test cases with similar features. Dealing with groups of test cases instead of each test case separately helped to solve regression testing scalability issues. In this paper, we propose a new methodology for regression testing of large-scale systems using data mining techniques to prioritize and select test cases based on their coverage criteria and fault history.