Matching Items (2)
Filtering by
- Creators: Mao, Jingxian
- Creators: Wu, Teresa
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
In today's global market, companies are facing unprecedented levels of uncertainties in supply, demand and in the economic environment. A critical issue for companies to survive increasing competition is to monitor the changing business environment and manage disturbances and changes in real time. In this dissertation, an integrated framework is proposed using simulation and online calibration methods to enable the adaptive management of large-scale complex supply chain systems. The design, implementation and verification of the integrated approach are studied in this dissertation. The research contributions are two-fold. First, this work enriches symbiotic simulation methodology by proposing a framework of simulation and advanced data fusion methods to improve simulation accuracy. Data fusion techniques optimally calibrate the simulation state/parameters by considering errors in both the simulation models and in measurements of the real-world system. Data fusion methods - Kalman Filtering, Extended Kalman Filtering, and Ensemble Kalman Filtering - are examined and discussed under varied conditions of system chaotic levels, data quality and data availability. Second, the proposed framework is developed, validated and demonstrated in `proof-of-concept' case studies on representative supply chain problems. In the case study of a simplified supply chain system, Kalman Filtering is applied to fuse simulation data and emulation data to effectively improve the accuracy of the detection of abnormalities. In the case study of the `beer game' supply chain model, the system's chaotic level is identified as a key factor to influence simulation performance and the choice of data fusion method. Ensemble Kalman Filtering is found more robust than Extended Kalman Filtering in a highly chaotic system. With appropriate tuning, the improvement of simulation accuracy is up to 80% in a chaotic system, and 60% in a stable system. In the last study, the integrated framework is applied to adaptive inventory control of a multi-echelon supply chain with non-stationary demand. It is worth pointing out that the framework proposed in this dissertation is not only useful in supply chain management, but also suitable to model other complex dynamic systems, such as healthcare delivery systems and energy consumption networks.
ContributorsWang, Shanshan (Author) / Wu, Teresa (Thesis advisor) / Fowler, John (Thesis advisor) / Pfund, Michele (Committee member) / Li, Jing (Committee member) / Pavlicek, William (Committee member) / Arizona State University (Publisher)
Created2010