Browsing by Subject "Data mining"

This thesis presents theoretical studies of some stochastic processes and their appli- cations in the Bayesian nonparametric methods. The stochastic processes discussed in the thesis are mainly the ones with independent increments - the Levy processes. We develop new representations for the Levy measures of two representative exam- ples of the Levy processes, the beta and gamma processes. These representations are manifested in terms of an infinite sum of well-behaved (proper) beta and gamma dis- tributions, with the truncation and posterior analyses provided. The decompositions provide new insights into the beta and gamma processes (and their generalizations), and we demonstrate how the proposed representation unifies some properties of the two, as these are of increasing importance in machine learning.

Next a new Levy process is proposed for an uncountable collection of covariate- dependent feature-learning measures; the process is called the kernel beta process. Available covariates are handled efficiently via the kernel construction, with covari- ates assumed observed with each data sample ("customer"), and latent covariates learned for each feature ("dish"). The dependencies among the data are represented with the covariate-parameterized kernel function. The beta process is recovered as a limiting case of the kernel beta process. An efficient Gibbs sampler is developed for computations, and state-of-the-art results are presented for image processing and music analysis tasks.

Last is a non-Levy process example of the multiplicative gamma process applied in the low-rank representation of tensors. The multiplicative gamma process is applied along the super-diagonal of tensors in the rank decomposition, with its shrinkage property nonparametrically learns the rank from the multiway data. This model is constructed as conjugate for the continuous multiway data case. For the non- conjugate binary multiway data, the Polya-Gamma auxiliary variable is sampled to elicit closed-form Gibbs sampling updates. This rank decomposition of tensors driven by the multiplicative gamma process yields state-of-art performance on various synthetic and benchmark real-world datasets, with desirable model scalability.

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.