<p> Keeping track of requirements from eliciting data to making decision needs an effective path from data to decision [43]. Visualization science helps to create this path by extracting insights from flood of data. Model helps to shape the transformation of data to visualization. Defect Detection and Prevention model was created to assess quality assurance activities. We selected DDP and started enhancing user interactivity with requirements visualization over basic DDP with implementing a visual requirements analytics framework. By applying GQM table to our framework, we added six visualization features to the existing visual requirements visualization approaches. We applied this framework to technical and non-technical stakeholder scenarios to gain the operational insights of requirements-driven risk mitigation in practice. The combination of the first and second scenarios' result presented the multiple stakeholders scenario result which was a small number of strategies from kept tradespase with common mitigations that must deploy to the system.</p>

<p> In recent years there has been a proliferation of research on a number of wireless multi-hop networks that include mobile ad-hoc networks, wireless mesh networks, and wireless sensor networks (WSNs). Routing protocols in such networks are often required to meet design objectives that include a combination of factors such as throughput, delay, energy consumption, network lifetime etc. In addition, many modern wireless networks are equipped with multi-channel radios, where channel selection plays an important role in achieving the same design objectives. Consequently, addressing the routing problem together with <i>cross-layer</i> adaptations such as channel selection is an important issue in such networks. In this work, we study the joint routing and channel selection problem that spans two domains of wireless networks. The first is a cost-effective and scalable wireless-optical access networks which is a combination of high-capacity optical access and unethered wireless access. The joint routing and channel selection problem in this case is addressed under an anycasting paradigm. In addition, we address two other problems in the context of wireless-optical access networks. The first is on optimal gateway placement and network planning for serving a given set of users. And the second is the development of an analytical model to evaluate the performance of the IEEE 802.11 DCF in radio-over- fiber wireless LANs. The second domain involves resource constrained WSNs where we focus on route and channel selection for network lifetime maximization. Here, the problem is further exacerbated by distributed power control, that introduces additional design considerations. Both problems involve cross-layer adaptations that must be solved together with routing. Finally, we present an analytical model for lifetime calculation in multi-channel, asynchronous WSNs under optimal power control.</p>

<p> Provisioning of rich routing building blocks to mobile ad-hoc networking applications has been of high interest. Several multi-hop wireless network applications need flexibility in describing paths their traffic will follow. To accommodate this need, previous work has proposed several viable routing schemes such as Dynamic Source Routing (DSR) and Trajectory-Based Routing (TBR). However, tradeoffs involved in the interaction of these routing schemes and the application-specific requirements or constraints have not been explored. Particularly, techniques to help the application to do the right routing choices based on a desired metric are much needed. Depending on the application's goals, routing choices should be steered for different metrics rather than the traditional notion of shortest-path in terms of distance. For instance, obstacle or hostility avoidance would require "accurate" paths, end-to-end traffic engineering/balancing would require "minimum utilization" paths, low delay routing for multimedia traffic would require "short distance" paths, and, finally, low loss routing for reliable end-to-end transfers would require "minimum congestion" paths. Our focus in this dissertation is the "accuracy" of paths. </p><p> First, we consider techniques that minimize routing protocol state costs under application-based constraints. We study the constraint of "accuracy" of the application's desired route, as this constraint provides a range of choices to the applications. As a crucial part of this optimization framework, we investigate the tradeoff between the packet header size and the network state. We, then, apply our framework to the case of TBR with application-based accuracy constraints in obeying a given trajectory and show that approximating trajectories under such accuracy constraints is NP-hard. We develop heuristics solving this problem and illustrate their performance. </p><p> Second, we take our TBR framework to a more general solution by adding automated trajectory generator and end-to-end traffic engineering support. We focus on the context of multi-hop wireless protocols for which application-specific needs are emphasized along with a highly dynamic underlying network environment. We propose a framework supporting a standardized way of interfacing between the network routing and the wireless applications. We use this framework to develop a roadmap-based trajectory planning scheme to engineer the end-to-end traffic over multi-hop wireless networks. We illustrate how our roadmap-based approach can automate the process of planning/selecting the trajectories so that better balancing of the traffic is achieved. We compare our roadmap-based trajectory planning approach to its shortest-path routing counterpart, Greedy Parameter Stateless Routing (GPSR), and show that beneficial tradeoffs can be attained.</p>

Thesis (M.S.)--Lehigh University, 2009. / Adviser: John R. Spletzer.

<p> In the era of the internet, we are connected to an overwhelming abundance of information. As more facets of our lives become digitized, there is a growing need for automatic tools to help us find the content we care about. To tackle the problem of information overload, a standard machine learning approach is to perform dimensionality reduction, transforming complicated high-dimensional data into a manageable, low-dimensional form. Probabilistic latent variable models provide a powerful and elegant framework for performing this transformation in a principled way. This thesis makes several advances for modeling two of the most ubiquitous types of online information: networks and text data. </p><p> Our first contribution is to develop a model for social networks as they vary over time. The model recovers latent feature representations of each individual, and tracks these representations as they change dynamically. We also show how to use text information to interpret these latent features. </p><p> Continuing the theme of modeling networks and text data, we next build a model of citation networks. The model finds influential scientific articles and the influence relationships between the articles, potentially opening the door for automated exploratory tools for scientists. The increasing prevalence of web-scale data sets provides both an opportunity and a challenge. With more data we can fit more accurate models, as long as our learning algorithms are up to the task. To meet this challenge, we present an algorithm for learning latent Dirichlet allocation topic models quickly, accurately and at scale. The algorithm leverages stochastic techniques, as well as the collapsed representation of the model. We use it to build a topic model on 4.6 million articles from the open encyclopedia Wikipedia in a matter of hours, and on a corpus of 1740 machine learning articles from the NIPS conference in seconds. </p><p> Finally, evaluating the predictive performance of topic models is an important yet computationally difficult task. We develop one algorithm for comparing topic models, and another for measuring the progress of learning algorithms for these models. The latter method achieves better estimates than previous algorithms, in many cases with an order of magnitude less computational effort.</p>