Mustafa Ally

A framework for understanding the factors influencing pair programming success

Pair programming is one of the more controversial aspects of several Agile system development methods, in particular eXtreme Programming (XP). Various studies have assessed factors that either drive the success or suggest advantages (and disadvantages) of pair programming. In this exploratory study the literature on pair programming is examined and factors distilled. These factors are then compared and contrasted with those discovered in our recent Delphi study of pair programming. Gallis et al. (2003) have proposed an initial framework aimed at providing a comprehensive identification of the major factors impacting team programming situations including pair programming. However, this study demonstrates that the framework should be extended to include an additional category of factors that relate to organizational matters. These factors will be further refined, and used to develop and empirically evaluate a conceptual model of pair programming (success).

Multimedia data mining using deep learning

Due to the large amounts of Multimedia data on the Internet, Multimedia mining has become a very active area of research. Multimedia mining is a form of data mining. Data mining uses algorithms to segment data to identify useful patterns and to make predictions. Despite the successes in many areas, data mining remains a challenging task. In the past, multimedia mining was one of the fields where the results were often not satisfactory. Multimedia Data Mining extracts relevant data from multimedia files such as audio, video and still images to perform similarity searches, identify associations, entity resolution and for classification. As the mining techniques have matured, new techniques were developed. A lot of progress has been made in areas such as visual data mining and natural language processing using deep learning techniques. Deep learning is a branch of machine learning and has been used among other on Smartphones for face recognition and voice commands. Deep learners are a type of artificial neural networks with multiple data processing layers that learn representations by increasing the level of abstraction from one layer to the next. These methods have improved the state-of-the-art in multimedia mining, in speech recognition, visual object recognition, natural language processing and other areas such as genome mining and predicting the efficacy of drug molecules. This paper describes some of the deep learning techniques that have been used in recent research for multimedia data mining.

Data mining in IoT: data analysis for a new paradigm on the internet

This paper provides an overview on Data Mining (DM) technologies for the Internet of Things (IoT). IoT has become an active area of research, since IoT promises among other to improve quality of live and safety in Smart Cities, to make resource supply and waste management more efficient, and optimize traffic. DM is highly domain specific and depends on what is being mined for. For instance, if IoT is used to optimize traffic in a Smart City to reduce traffic jams and to find parking spaces quicker, different types of data needs to be collected and analysed from an eHealth solution, where IoT is used in a Smart Home to monitor the well being of patients or elderly people. IoT connects things that can collect numeric data from smart sensors, streaming data from cameras or route information on maps. Depending on the type of data, different techniques need to be adopted to analyse them. Also, many IoT applications analyse data from different devices and correlate them to make predictions about possible machine failures in production sites or looming emergency situations in Smart Buildings in a home security application. DM techniques need to handle the heterogeneity of IoT data, the large volumes of data and the speed at which they are produced. This paper explores the state of the art DM techniques for IoT.

Genome Mining Using Machine Learning Techniques

A major milestone in modern biology was the complete sequencing of the human genome. But it produced a whole set of new challenges in exploring the functions and interactions of different parts of the genome. One application is predicting disorders based on mining the genotype and understanding how the interactions between genetic loci lead to certain human diseases.

Reality Mining in eHealth

There is increasing interest in Big Data analytics in health care. Behavioral health analytics is a care management technology that aims to improve the quality of care and reduce health care costs based capture and analysis of data on patient’s behavioral patterns. Big Data analytics of behavioral health data offers the potential of more precise and personalized treatment as well as monitor population-wide events such as epidemics.

Context Aware Computing for Ambient Assisted Living

With the prevalence of wireless technologies, cloud computing and the rapid growth of deployed smart sensors in the past few years, we live in an increasingly interconnected world. These technologies have fostered the dissemination of the Internet of Things IoT. They form the foundation for smart homes and smart cities. Context aware devices and ambient computing techniques have expanded the application of the IoT into new areas such as assisted living, eHealth, and elderly care. However, there are challenges to analyze the large volumes of sensor and context data generated by these devices. Also, there are serious security and privacy concerns especially in the area of health care that need to be addressed. This paper gives an overview of the state-of-the-art technologies for ambient assisted living AAL and proposes an architecture based on SOA.

Web Publishing: An Extreme, Agile Experience

The proponents of agile methodologies suggest that many of the inhibitors to system development methodology adoption have largely been addressed in the underlying principles of agile methods. This paper reports the experience of a small team developing Web publishing software tools for use in building Web sites for online delivery of tertiary education study materials. These early adopters successfully used eXtreme Programming (XP) practices for this tool development exercise. Almost all XP practices were adopted, although some were adhered to more rigorously than others and some proved to be more successful than others. Continued use of XP and communication of its benefits to others has been a consequential focus for the developers.