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This paper describes an innovative approach for teaching the challenges in the management of data warehouse development. The approach contains lecture material providing conceptual background about the management of data warehouse development, a simulation game supporting experiential learning, and a post-play debriefing to support synthesis of conceptual material and experiential learning. The simulation game, Emerge2Maturity, addresses learning challenges faced by students as they experience development over time, determine capabilities to balance costs and benefits for consistency with an organization's strategy, observe organizational learning effects on costs and benefits, and gain awareness of the impact of external events. To support decision-making by players and address these learning challenges, Emerge2Maturity uses two novel models: the Capability Assessment Model for choices about data sources subject to budget and resource constraints and the Configuration Model for transition among decision-making phases involving constraint levels, learning effects, and external events. Simulation in each phase and phase summaries provide opportunities for players to reflect about their progress in developing a data warehouse. Initial evaluation of Emerge2Maturity in a data warehouse course demonstrated the potential to improve instruction about maturity concepts pertinent to data warehouse development in organizations.

This paper describes an innovative approach for teaching the challenges in the management of data warehouse development. The approach contains lecture material providing conceptual background about the management of data warehouse development, a simulation game supporting experiential learning, and a post-play debriefing to support synthesis of conceptual material and experiential learning. The simulation game, Emerge2Maturity, addresses learning challenges faced by students as they experience development over time, determine capabilities to balance costs and benefits for consistency with an organization's strategy, observe organizational learning effects on costs and benefits, and gain awareness of the impact of external events. To support decision-making by players and address these learning challenges, Emerge2Maturity uses two novel models: the Capability Assessment Model for choices about data sources subject to budget and resource constraints and the Configuration Model for transition among decision-making phases involving constraint levels, learning effects, and external events. Simulation in each phase and phase summaries provide opportunities for players to reflect about their progress in developing a data warehouse. Initial evaluation of Emerge2Maturity in a data warehouse course demonstrated the potential to improve instruction about maturity concepts pertinent to data warehouse development in organizations.

The choice of a probabilistic model to describe sequence evolution can and should be justified. Underfitting the data through the use of overly simplistic models may miss out on interesting phenomena and lead to incorrect inferences. Overfitting the data with models that are too complex may ascribe biological meaning to statistical artifacts and result in falsely significant findings. We describe a likelihood-based approach for evolutionary model selection. The procedure employs a genetic algorithm (GA) to quickly explore a combinatorially large set of all possible time-reversible Markov models with a fixed number of substitution rates. When applied to stem RNA data subject to well-understood evolutionary forces, the models found by the GA 1) capture the expected overall rate patterns a priori; 2) fit the data better than the best available models based on a priori assumptions, suggesting subtle substitution patterns not previously recognized; 3) cannot be rejected in favor of the general reversible model, implying that the evolution of stem RNA sequences can be explained well with only a few substitution rate parameters; and 4) perform well on simulated data, both in terms of goodness of fit and the ability to estimate evolutionary rates. We also investigate the utility of several distance measures for comparing and contrasting inferred evolutionary models. Using widely available small computer clusters, our approach allows, for the first time, to evaluate the performance of existing RNA evolutionary models by comparing them with a large pool of candidate models and to validate common modeling assumptions. In addition, the new method provides the foundation for rigorous selection and comparison of substitution models for other types of sequence data.

With significant under funding of public defined benefit pension plans, public debate often focuses on funding problems, neglecting benefit-side factors that contribute to under funding. In this study we examine the benefit side by calculating the value of deferred compensation, using a unique dataset of salary histories for recent university retirees covered by the Colorado Public Employees Retirement Association plan. We find sizable levels of deferred compensation that is associated with retirement age and period, job class, service years, and to some extent gender, with administrators receiving the highest levels. We also find wage–earning profiles to underestimate salary growth for higher-paid employees.

Notably absent in previous research on inductive expert systems is the study of meanrisk trade-offs. Such trade-offs may be significant when there are asymmetries such as unequal classification costs, and uncertainties in classification and information acquisition costs. The objective of this research is to developmodels to evaluate mean-risk trade-offs in value-based inductive approaches. We develop a combined mean-risk measure and incorporate it into the Risk-Based induction algorithm. The mean-risk measure has desirable theoretical properties (consistency and separability) and is supported by empirical results on decision making under risk. Simulation results using the Risk-Based algorithm demonstrate: (i) an order of magnitude performance difference between mean-based and risk-based algorithms and (ii) an increase in the performance difference between these algorithms as either risk aversion, uncertainty, or asymmetry increases given modest thresholds of the other two factors.

This study uses a unique data set of retiree characteristics and salary histories for administrators, teachers, and non-professional employees of the Denver Public School Retirement System (DPSRS) to analyze surplus deferred compensation for DPSRS and four state K-12 defined benefit pension plans. We find sizable levels of surplus deferred compensation for each plan, with significant differences across plans, job classes, and age groups. Across plans, differences in cost of living allowances impact the expected present value of retirement benefits more than benefit table differences when controlling for each respective factor. Somewhat surprisingly, the plans in our study with the largest present value of future benefits had lower employee contribution rates. Pension wealth for reduced benefits showed larger wealth accrual at younger ages than full, unreduced benefits, and younger cohorts starting work at an earlier age received significantly higher surplus deferred compensation.

Retrieval of a set of cases similar to a new case is a problem common to a number of machine learning approaches such as nearest neighbor algorithms, conceptual clustering, and case based reasoning. A limitation of most case retrieval algorithms is their lack of attention to information acquisition costs. When information acquisition costs are considered, cost reduction is hampered by the practice of separating concept formation and retrieval strategy formation. To demonstrate the above claim, we examine two approaches. The first approach separates concept formation and retrieval strategy formation. To form a retrieval strategy in this approach, we develop the CR lc (case retrieval loss criterion) algorithm that selects attributes in ascending order of expected loss. The second approach jointly optimizes concept formation and retrieval strategy formation using a cost based variant of the ID 3 algorithm ( ID 3 c ). ID 3 c builds a decision tree wherein attributes are selected using entropy reduction per unit information acquisition cost. Experiments with four data sets are described in which algorithm, attribute cost coefficient of variation, and matching threshold are factors. The experimental results demonstrate that (i) jointly optimizing concept formation and retrieval strategy formation has substantial benefits, and (ii) using cost considerations can significantly reduce information acquisition costs, even if concept formation and retrieval strategy formation are separated.

We study the sequential information acquisition problem for rule-based expert systems as follows: find the information acquisition strategy that minimizes the expected cost to operate the system while maintaining the same output decisions. This problem arises for rule-based expert systems when the cost or time to collect inputs is significant and the inputs are not known until the system operates. We develop several \"optimistic\" heuristics to generate information acquisition strategies and study their properties. The heuristics provide choices concerning precision (i.e., how optimistic) versus computational effort. The heuristics are embedded into an informed search algorithm (based on AO*) that produces an optimal strategy and a greedy search algorithm. The search strategies are designed for situations in which rules can overlap and the cost of collecting an input may depend on the set of inputs previously collected. We study the properties of these approaches and simulate their performance on a variety of synthetic expert systems. Our results indicate that the heuristics are very precise, leading to near optimal results for greedy search and moderate search effort for optimal search.

Oral antibiotics, especially tetracyclines, are commonly used to treat moderate to moderately severe acne vulgaris. There are hints suggesting that a combination treatment with oral tetracyclines and topical retinoids can cause a greater and prompter improvement of acne than monotherapy with tetracyclines. We evaluated the clinical activity of a 12-week combined therapy with oral lymecycline (300mg/day for 2 weeks and then 150mg/day) and topical adapalene (gel or cream) in 419 patients with inflammatory acne. A significant reduction in the number of acne lesions was noted at 4 and 12 weeks (P<0.0001). Thereafter, 400 patients underwent a maintenance treatment with adapalene alone for 12 weeks. At week 24 a relevant improvement of acne lesions still persisted (P<0.0001) in most patients. Only 16 patients relapsed and required additional use of oral lymecycline which proved again successful. No substantial differences were noted in the magnitude of clinical response between patients treated with adapalene gel and those treated with cream formulation. Treatment was well tolerated. Local adverse reactions occurred in 11.7 % of patients and resulted in premature discontinuation of treatment in 1.4 %. Systemic (gastrointestinal) untoward effects developed in 1.2 % of patients and caused treatment interruption in 0.7 % of cases. No serious adverse events occurred.

Inductive expert systems typically operate with imperfect or noisy input attributes. We study design differences in inductive expert systems arising from implicit versus explicit handling of input noise. Most previous approaches use an implicit approach wherein inductive expert systems are constructed using input data of quality comparable to problems the system will be called upon to solve. We develop an explicit algorithm (ID3 ecp ) that uses a clean (without input errors) training set and an explicit measure of the input noise level and compare it to a traditional implicit algorithm, ID3 p (the ID3 algorithm with the pessimistic pruning procedure). The novel feature of the explicit algorithm is that it injects noise in a controlled rather than random manner in order to reduce the performance variance due to noise. We show analytically that the implicit algorithm has the same expected partitioning behavior as the explicit algorithm. In contrast, however, the partitioning behavior of the explicit algorithm is shown to be more stable (i.e., lower variance) than the implicit algorithm. To extend the analysis to the predictive performance of the algorithms, a set of simulation experiments is described in which the average performance and coefficient of variation of performance of both algorithms are studied on real and artificial data sets. The experimental results confirm the analytical results and demonstrate substantial differences in stability of performance between the algorithms especially as the noise level increases.