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- All Subjects: Monitoring
- Creators: Bekki, Jennifer
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014
Description
In rural and urban areas of Nigeria, dependence on groundwater is increasing since the population is growing and high quality, treated municipal water is scarce. Municipal drinking water is often compromised because of old and leaking distribution pipes. About 58% of the water consumed in Lagos State, Nigeria, comes from residential wells. However, a majority of residential wells are shallow wells that are constructed relatively close to septic tanks or pit latrines and are therefore subject to contamination. In certain parts of Africa, there is high potential of severe epidemic if water quality is not improved. With increasing reliance on groundwater, a need exists to monitor the quality of groundwater. This thesis develops a plan for a monitoring program for residential wells in Lagos State, Nigeria. The program focuses on ways by which owners can maintain reasonably good water quality, and on the role of government in implementing water quality requirements. In addition, this thesis describes a survey conducted in various areas of Lagos State to assess community awareness of the importance of groundwater quality and its impact on individuals and the community at large. The survey shows that 30% to 40% of the households have located their wells and septic tanks in the same general area. Various templates have been created to help the staff of a future monitoring program team to effectively gather information during site characterization. A "Questions and Answers" leaflet has been developed to educate citizens about the need for monitoring residential wells.
ContributorsTalabi, Omogbemiga Adepitan (Author) / Edwards, David (Thesis advisor) / Hild, Nicholas (Committee member) / Olson, Larry (Committee member) / Arizona State University (Publisher)
Created2010