Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.

The role of individualism and collectivism as predictors of attributions for unethical work behavior: An empirical examination across two culturally diverse groups

This dissertation addresses how the cultural dimensions of individualism and collectivism affect the attributions people make for unethical behavior at work. The moderating effect of ethnicity is also examined by considering two culturally diverse groups: Hispanics and Anglos. The sample for this study is a group of business graduate students from two universities in the Southeast. A 20-minute survey was distributed to master's degree students at their classroom and later on returned to the researcher. Individualism and collectivism were operationalized as by a set of attitude items, while unethical work behavior was introduced in the form of hypothetical descriptions or scenarios. Data analysis employed multiple group confirmatory factor analysis for both independent and dependent variables, and subsequently multiple group LISREL models, in order to test predictions. Results confirmed the expected link between cultural variables and attribution responses, although the role of independent variables shifted, due to the moderating effect of ethnicity, and to the nuances of each particular situation. ^

Team diversity and leadership: Tests of model of process and direct effects

A model was tested to examine relationships among leadership behaviors, team diversity, and team process measures with team performance and satisfaction at both the team and leader-member levels of analysis. Relationships between leadership behavior and team demographic and cognitive diversity were hypothesized to have both direct effects on organizational outcomes as well as indirect effects through team processes. Leader member differences were investigated to determine the effects of leader-member diversity leader-member exchange quality, individual effectiveness and satisfaction.^ Leadership had little direct effect on team performance, but several strong positive indirect effects through team processes. Demographic Diversity had no impact on team processes, directly impacted only one performance measure, and moderated the leadership to team process relationship.^ Cognitive Diversity had a number of direct and indirect effects on team performance, the net effects uniformly positive, and did not moderate the leadership to team process relationship.^ In sum, the team model suggests a complex combination of leadership behaviors positively impacting team processes, demographic diversity having little impact on team process or performance, cognitive diversity having a positive net impact impact, and team processes having mixed effects on team outcomes.^ At the leader-member level, leadership behaviors were a strong predictor of Leader-Member Exchange (LMX) quality. Leader-member demographic and cognitive dissimilarity were each predictors of LMX quality, but failed to moderate the leader behavior to LMX quality relationship. LMX quality was strongly and positively related to self reported effectiveness and satisfaction.^ The study makes several contributions to the literature. First, it explicitly links leadership and team diversity. Second, demographic and cognitive diversity are conceptualized as distinct and multi-faceted constructs. Third, a methodology for creating an index of categorical demographic and interval cognitive measures is provided so that diversity can be measured in a holistic conjoint fashion. Fourth, the study simultaneously investigates the impact of diversity at the team and leader-member levels of analyses. Fifth, insights into the moderating impact of different forms of team diversity on the leadership to team process relationship are provided. Sixth, this study incorporates a wide range of objective and independent measures to provide a 360$\sp\circ$ assessment of team performance. ^

Self-other rating discrepancy and personality antecedents

This study explored individual difference factors to help explain the discrepancy that has been found to exist between self and other ratings in prior research. Particularly, personality characteristics of the self-rater were researched in the current study as a potential antecedent for self-other rating agreement. Self, peer, and supervisor ratings were provided for global performance as well as five competencies specific to the organization being examined. Four rating tendency categories, over-raters, under-raters, in-agreement (good), and in-agreement (poor), established in research by Atwater and Yammarino were used as the basis of the current research. The sample for rating comparisons within the current study consisted of 283 self and supervisor dyads and 275 for self and peer dyads from a large financial organization. Measures included a custom multi-rater performance instrument and the personality survey instrument, ASSESS, which measures 20 specific personality characteristics. MANCOVAs were then performed on this data to examine if specific personality characteristics significantly distinguished the four rating tendency groups. Examination of all personality dimensions and overall performance uncovered significant findings among rating groups for self-supervisor rating comparisons but not for self-peer rating comparisons. Examination of specific personality dimensions for self-supervisory ratings group comparisons and overall performance showed Detail Interest to be an important characteristic among the hypothesized variables. For self-supervisor rating comparisons and specific competencies, support was found for the hypothesized personality dimension of Fact-based Thinking which distinguished the four rating groups for the competency, Builds Relationships. For both self-supervisor and self-peer rating comparisons, the competencies, Builds Relationships and Leads in a Learning Environment, were found to have significant relationship with several personality characteristics, however, these relationships were not consistent with the hypotheses in the current study. Several unhypothesized personality dimensions were also found to distinguish rating groups for both self-supervisor and self-peer comparisons on overall performance and various competencies. Results of the current study hold implications for the training and development session that occur after a 360-degree evaluation process. Particularly, it is suggested that feedback sessions may be designed according to particular rating tendencies to maximize the interpretation, acceptance and use of evaluation information. ^

A retrospective-longitudinal examination of the relationship between apportionment of seat time in community-college algebra courses and student academic performance

During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.

A Retrospective-Longitudinal Examination of the Relationship between Apportionment of Seat Time in Community-College Algebra Courses and Student Academic Performance

During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.

Emotional Intelligence and Leadership in Organization: A Meta-analytic Test of Process Mechanisms

The present study – employing psychometric meta-analysis of 92 independent studies with sample sizes ranging from 26 to 322 leaders – examined the relationship between EI and leadership effectiveness. Overall, the results supported a linkage between leader EI and effectiveness that was moderate in nature (ρ = .25). In addition, the positive manifold of the effect sizes presented in this study, ranging from .10 to .44, indicate that emotional intelligence has meaningful relations with myriad leadership outcomes including effectiveness, transformational leadership, LMX, follower job satisfaction, and others. Furthermore, this paper examined potential process mechanisms that may account for the EI-leadership effectiveness relationship and showed that both transformational leadership and LMX partially mediate this relationship. However, while the predictive validities of EI were moderate in nature, path analysis and hierarchical regression suggests that EI contributes less than or equal to 1% of explained variance in leadership effectiveness once personality and intelligence are accounted for.