Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.

Evaluation of semi-passive treatment technologies for septic lagoon capacity expansion

In Canada, increases in rural development has led to a growing need to effectively manage the resulting municipal and city sewage without the addition of significant cost- and energy- expending infrastructure. Storring Septic Service Limited is a family-owned, licensed wastewater treatment facility located in eastern Ontario. It makes use of a passive waste stabilization pond system to treat and dispose of waste and wastewater in an environmentally responsible manner. Storring Septic, like many other similar small-scale wastewater treatment facilities across Canada, has the potential to act as a sustainable eco-engineered facility that municipalities and service providers could utilize to manage and dispose of their wastewater. However, it is of concern that the substantial inclusion of third party material could be detrimental to the stability and robustness of the pond system. In order to augment the capacity of the current facility, and ensure it remains a self-sustaining system with the capacity to safely accept septage from other sewage haulers, it was hypothesized that pond effluent treatment could be further enhanced through the incorporation of one of three different technology solutions, which would allow the reduction of wastewater quality parameters below existing regulatory effluent discharge limits put in place by Ontario’s Ministry of the Environment and Climate Change (MOECC). Two of these solutions make use of biofilm technologies in order to enhance the removal of wastewater parameters of interest, and the third utilizes the natural water filtration capabilities of zebra mussels. Pilot-scale testing investigated the effects of each of these technologies on treatment performance under both cold and warm weather operation. This research aimed to understand the important mechanisms behind biological filtration methods in order to choose and optimize the best treatment strategy for full-scale testing and implementation. In doing so, a recommendation matrix was elaborated provided with the potential to be used as a universal operational strategy for wastewater treatment facilities located in environments of similar climate and ecology.