GMU Software Engineering Seminar Series
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Date: Tues, 12/7/2010
Time: 12:00 – 1:00pm
Location: 4201, Engineering
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Title: Visual Language for Software Product Lines in Team Computing
Speaker: Vasilios Tzeremes
Abstract
Team Computing (TeC) is a generic end user programming framework that enables users to design and deploy software systems for their environments. TeC follows a visual programming approach. Users drag and drop software components and connect them together to achieve a goal without the need of extensive programming. Software can be deployed at runtime, across spaces, securely without the need of any interruptions. Software Product Lines (SPL) concepts can be used to enhance TeC by allowing end users to design generic software applications that can share with other users. End users will be able to design teams with different features that can be customized at runtime by others. This will increase team quality and simplify the use of complex teams. Currently there are a number of languages used to design and generate SPL members. In this seminar we will present why we believe a visual language may be a better fit for representing SPL in team computing. Moreover and we will discuss some of the challenges that come with it like how do we present SPL primitives in visual languages and how do we support end users in configuring SPL members.
Bio
Vasilios Tzeremes is a Ph.D. candidate in Information Technology at George Mason University. He is working as a software engineer in Northern Virginia for the past 9 years. He has developed several software applications for private and government organizations. Vasilios is originally from Greece where he completed his undergraduate studies. In 2004 he completed his master degree in Information Systems at American University. He is currently working on his dissertation proposal. His dissertation topic focuses on supporting end users creating software product lines.
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Title: An Inference Network Model for Data Abstraction in a First Response Context
Speaker: Salman Salloum
Abstract
First response workers collaborate in gathering knowledge of the situation on the ground during rescue and evacuation missions. While voice broadcasting is used today for small teams, it does not scale for larger teams or wider deployment areas. However, first responders must pay attention to their mission, and presenting them with all the detailed information gathered from a wide area is unhelpful and distracting. This work proposes a reliable model for data abstraction in a fire response team, taking into consideration that this data is both time-sensitive and space-sensitive, and it may be sensed by different sensors in the area or observed by first responders themselves. The proposed model builds on Inference Networks.
Bio
Salman Salloum has an Engineering Diploma (2001-2006) from the Department of Software Engineering and Information Systems, Faculty of Information Technology, Damascus University. He has achieved a remarkable graduate project on developing Reusable Learning Objects, and Developing a Learning Object Repository Supplied by Retrieval and Browsing Capabilities. He is preparing a Master Degree at the same department and focusing his research on the Reusability of Learning Objects. He was leading the eLearning team at ePedia-SY Company (2008-2010). Now, he is an exchange visitor at the Department of Computer Science, George Mason University for Fall 2010 semester.