Copyright (c) 2010 All rights reserved. http://works.bepress.com/monica_adya Recent documents in Monica Adya en-us Tue, 02 Mar 2010 10:27:00 PST 3600 http://works.bepress.com/monica_adya/9 http://works.bepress.com/monica_adya/9 Tue, 06 Oct 2009 13:07:51 PDT D Nath http://works.bepress.com/monica_adya/8 http://works.bepress.com/monica_adya/8 Tue, 06 Oct 2009 13:05:43 PDT Monica Adya http://works.bepress.com/monica_adya/7 http://works.bepress.com/monica_adya/7 Tue, 06 Oct 2009 13:04:12 PDT Monica Adya http://works.bepress.com/monica_adya/6 http://works.bepress.com/monica_adya/6 Tue, 06 Oct 2009 13:02:14 PDT V Yadav http://works.bepress.com/monica_adya/5 http://works.bepress.com/monica_adya/5 Tue, 06 Oct 2009 12:56:59 PDT With the advances in communication technology, large volumes of information can transfer across continents within a fraction of a second. Nevertheless, computer users still suffer from unpleasant situations when they interact with systems and are required to adapt to systems rather than the other way round. User modeling aims to overcome this problem by enabling computer systems to interact with users according to the users' models, i.e., goals, knowledge, and preferences of users. Although, user modeling has shown invaluable benefits, methods of capturing user information to build precise and useful user models are still in their early states. This paper proposes a new approach for gathering user information by pooling the information from different systems. This concept, entitled Distributed User Modeling (DUM) is based on a method in which sensors built into each system contribute specific user information to the pooling. Having multiple sources of user information increases the possibility that a system can generate reliable user models. A general model of DUM is presented in this paper. The conceptual framework of the Distributed Fuzzy Object-Oriented User Modeling System (DFOOUMS) that uses DUM as its basis structure is also presented. Thawatchai Piyawat http://works.bepress.com/monica_adya/4 http://works.bepress.com/monica_adya/4 Tue, 06 Oct 2009 12:56:58 PDT Kate Kaiser http://works.bepress.com/monica_adya/3 http://works.bepress.com/monica_adya/3 Tue, 06 Oct 2009 12:56:58 PDT Rule-based forecasting (RBF) uses rules to combine forecasts from simple extrapolation methods. Weights for combining the rules use statistical and domain-based features of time series. RBF was originally developed, tested, and validated only on annual data. For the M3-Competition, three major modifications were made to RBF. First, due to the absence of much in the way of domain knowledge, we prepared the forecasts under the assumption that no domain knowledge was available. This removes what we believe is one of RBF's primary advantages. We had to re-calibrate some of the rules relating to causal forces to allow for this lack of domain knowledge. Second, automatic identification procedures were used for six time-series features that had previously been identified using judgment. This was done to reduce cost and improve reliability. Third, we simplified the rule-base by removing one method from the four that were used in the original implementation. Although this resulted in some loss in accuracy, it reduced the number of rules in the rule-base from 99 to 64. This version of RBF still benefits from the use of prior findings on extrapolation, so we expected that it would be substantially more accurate than the random walk and somewhat more accurate than equal weights combining. Because most of the previous work on RBF was done using annual data, we especially expected it to perform well with annual data. Monica Adya http://works.bepress.com/monica_adya/2 http://works.bepress.com/monica_adya/2 Tue, 06 Oct 2009 12:56:57 PDT Rule-based forecasting (RBF) is an expert system that uses features of time series to select and weight extrapolation techniques. Thus, it is dependent upon the identification of features of the time series. Judgmental coding of these features is expensive and the reliability of the ratings is modest. We developed and automated heuristics to detect six features that had previously been judgmentally identified in RBF: outliers, level shifts, change in basic trend, unstable recent trend, unusual last observation, and functional form. These heuristics rely on simple statistics such as first differences and regression estimates. In general, there was agreement between automated and judgmental codings for all features other than functional form. Heuristic coding was more sensitive than judgment and consequently, identified more series with a certain feature than judgmental coding. We compared forecast accuracy using automated codings with that using judgmental codings across 122 series. Forecasts were produced for six horizons, resulting in a total of 732 forecasts. Accuracy for 30% of the 122 annual time series was similar to that reported for RBF. For the remaining series, there were as many that did better with automated feature detection as there were that did worse. In other words, the use of automated feature detection heuristics reduced the costs of using RBF without negatively affecting forecast accuracy. Monica Adya http://works.bepress.com/monica_adya/1 http://works.bepress.com/monica_adya/1 Tue, 06 Oct 2009 12:56:56 PDT Global sourcing of software development has imposed new skill requirements on Information Technology (IT) personnel. In the U.S., this has resulted in a paradigm shift from technical to softer skills such as communications and virtual team management. Higher education institutions must, consequently, initiate innovative curriculum transformations to better prepare students for these emerging workforce needs. This paper describes one such venture between MU, U.S.A. and MDI, India, wherein IT students at MU collaborated with Management Information Systems (MIS) students at MDI on an offshore software development project. The class environment replicated an offshore client/vendor relationship in a fully virtual setting while integrating communications and virtual team management with traditional IT project management principles. Course measures indicated that students benefited from this project, gained first-hand experience in the process of software offshoring, and learned skills critical for conduct of global business. For faculty considering such initiatives, we describe the design and administration of this class over two semesters, lessons learned from our engagement, and factors critical to success of such initiatives and those detrimental to their sustenance. Monica Adya