@article {1063,
	title = {Time series representation and similarity based on local autopatterns},
	journal = {Data Mining and Knowledge Discovery},
	volume = {30},
	year = {2016},
	month = {03/2016},
	pages = {476{\textendash}509},
	abstract = {<p>Time series data mining has received much greater interest along with the increase in temporal data sets from different domains such as medicine, finance, multimedia, etc. Representations are important to reduce dimensionality and generate useful similarity measures. High-level representations such as Fourier transforms, wavelets, piecewise polynomial models, etc., were considered previously. Recently, autoregressive kernels were introduced to reflect the similarity of the time series. We introduce a novel approach to model the dependency structure in time series that generalizes the concept of autoregression to local autopatterns. Our approach generates a pattern-based representation along with a similarity measure called learned pattern similarity (LPS). A tree-based ensemble-learning strategy that is fast and insensitive to parameter settings is the basis for the approach. Then, a robust similarity measure based on the learned patterns is presented. This unsupervised approach to represent and measure the similarity between time series generally applies to a number of data mining tasks (e.g., clustering, anomaly detection, classification). Furthermore, an embedded learning of the representation avoids pre-defined features and an extraction step which is common in some feature-based approaches. The method generalizes in a straightforward manner to multivariate time series. The effectiveness of LPS is evaluated on time series classification problems from various domains. We compare LPS to eleven well-known similarity measures. Our experimental results show that LPS provides fast and competitive results on benchmark datasets from several domains. Furthermore, LPS provides a research direction and template approach that breaks from the linear dependency models to potentially foster other promising nonlinear approaches.</p>},
	issn = {1573-756X},
	author = {Mustafa Gokce Baydogan and Runger, George}
}
@article {941,
	title = {A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems},
	journal = {Technometrics},
	volume = {57},
	year = {2015},
	pages = {362-373},
	doi = {10.1080/00401706.2015.1044117},
	url = {http://dx.doi.org/10.1080/00401706.2015.1044117},
	author = {Na Zou and Yun Zhu and Ji Zhu and Mustafa Gokce Baydogan and Wei Wang and Jing Li}
}
@article {34,
	title = {Toward Development of Adaptive Service-Based Software Systems},
	journal = {IEEE Transactions on Services Computing},
	volume = {2},
	year = {2009},
	pages = {247-260},
	abstract = {<p style="text-align: justify;">The rapid adoption of service-oriented architecture (SOA) in many large-scale distributed applications requires the development of adaptive service-based software systems (ASBS) with the capability of monitoring the changing system status, analyzing, and controlling tradeoffs among various quality-of-service (QoS) aspects, and adapting service configurations to satisfy multiple QoS requirements simultaneously. In this paper, our results toward the development of adaptive service-based software systems are presented. The formulation of activity-state-QoS (ASQ) models and how to use the data from controlled experiments to establish ASQ models for capturing the cause-effect dynamics among service activities, system resource states, and QoS in service-based systems are presented. Then, QoS monitoring modules based on ASQ models and SOA-compliant simulation models are developed to support the validation of the ASBS design. The main idea for developing QoS adaptation modules based on ASQ models is discussed. An experiment based on a voice communication service is used to illustrate our results.</p>},
	keywords = {Design concepts, distributed/Internet-based software engineering tools and techniques, methodologies, modeling methodologies, quality of services, services systems},
	issn = {1939-1374},
	doi = {http://doi.ieeecomputersociety.org/10.1109/TSC.2009.17},
	author = {Stephen S. Yau and Nong Ye and Hessam S. Sarjoughian and Dazhi Huang and Auttawut Roontiva and Mustafa Gokce Baydogan and Mohammed A. Muqsith}
}
