Matching Items (3)
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- All Subjects: Applications
- Creators: Ahmad, Altaf
Description
The Wish List is a website that allows users to input URLs of products that they like into a wish list, much like Amazon's Wish List. The website also connects users to their Facebook friends who also use the application, so that users can view their friends' wish lists and "claim" products that they've purchased. This makes the Wish List like a registry as well. This report documents the functionality and the structure of the website, but the website itself is not yet released to the general public.
ContributorsChesley, Bryana Renee (Author) / Ahmad, Altaf (Thesis director) / Prince, Linda (Committee member) / Barrett, The Honors College (Contributor) / WPC Graduate Programs (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor)
Created2014-05
Description
Veelog is an Android application created to monitor and track data regarding maintenance for an individual's personal vehicles. For instance, most car owners change their oil every 10,000 miles or so. The application will help track this data, allowing users to identify their own range of how often a service needs to be completed and provide helpful information when the need comes around. The goal of the application is to provide a platform for individuals to record, use, and save information relevant to themselves as the owner. By ensuring that there is space for the data to be recorded and properly tracked, car owners can take initiative in providing preventative maintenance for their vehicles. The idea for the application originally came from observing many individuals who keep a notebook in each of their vehicles for recording and keeping track of maintenance schedules manually. Veelog is a solution that keeps all maintenance manuals in one place, with the additional benefit of calculating upcoming services automatically. Veelog users can also make customizations to their profiles including custom services that are specific to their own needs. The target users for Veelog are individuals who want to be proactive in servicing their vehicles. The application requires frequent checking and regular updates to stay current and provide accurate information for upcoming services. Being proactive about vehicle maintenance provides long term benefits such as preventing serious car trouble, which ultimately results in saving money and staying safe and makes the application worth the extra attention. Ideally, individuals who have not previously been proactive about vehicle maintenance will also be encouraged by the convenience that Veelog provides.
ContributorsKnorr, Jeremy Joseph (Author) / Ahmad, Altaf (Thesis director) / Olsen, Christopher (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges.
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
ContributorsLi, Jundong (Author) / Liu, Huan (Thesis advisor) / Faloutsos, Christos (Committee member) / He, Jingrui (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2019