Competitive Intelligence Task Analysis And Retrieval: An End-User Approach

The Internet, as one of the major resources for competitive intelligence (CI), not only provides a large amount of public data but also exposes a variety of business relations that may not otherwise be well-known. However, finding such information can be tedious and time-consuming for end-users without proper tools or expertise. In this paper, we examine the nature of CI tasks, classify and decompose them based on a task complexity theory, and propose norms for a context-based approach to retrieve CI data. We developed a meta-search engine called Competitive Intelligence Task Analysis and Retrieval (CITAR) to demonstrate the feasibility of the proposed approach. The present study provides a framework to further explore the relationships among CI tasks, interactive search, and context-based search systems design

Exploratory Competitive Intelligence through Task Complexity Analysis

As one of the major resources for competitive intelligence (CI), the Internet not only provides a large amount of public data but also exposes a variety of business relations that may not otherwise be well-known. However, finding such information can be tedious and time-consuming for end-users without proper tools or expertise. In this paper, we examine the nature of CI tasks, classify and decompose them based on task complexity theories, and propose norms for a context-based approach to retrieve CI data. Our study provides a framework to further explore the relationships among CI tasks, interactive search, and context-based search systems design

Using Big Data for Predicting Freshmen Retention

Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students’ university smart card transactions. Furthermore, we applied a sequence learning method to infer students’ campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall

Towards Improving Conceptual Modeling: An Examination of Common Errors and Their Underlying Reasons

Databases are a critical part of Information Technology. Following a rigorous methodology in the database lifecycle ensures the development of an effective and efficient database. Conceptual data modeling is a critical stage in the database lifecycle. However, modeling is hard and error prone. An error could be caused by multiple reasons. Finding the reasons behind errors helps explain why the error was made and thus facilitates corrective action to prevent recurrence of that type of error in the future. We examine what errors are made during conceptual data modeling and why. In particular, this research looks at expertise-related reasons behind errors. We use a theoretical approach, grounded in work from educational psychology, followed up by a survey study to validate the model. Our research approach includes the following steps: (1) measure expertise level, (2) classify kinds of errors made, (3) evaluate significance of errors, (4) predict types of errors that will be made based on expertise level, and (5) evaluate significance of each expertise level. Hypotheses testing revealed what aspects of expertise influence different types of errors. Once we better understand why expertise related errors are made, future research can design tailored training to eliminate the errors

Have query optimizers hit the wall?

The query optimization phase within a database management system (DBMS) ostensibly finds the fastest query execution plan from a potentially large set of enumerated plans, all of which correctly compute the specified query. Occasionally the cost-based optimizer selects a slower plan, for a variety of reasons. We introduce the notion of empirical suboptimality of a query plan chosen by the DBMS, indicated by the existence of a query plan that performs more efficiently than the chosen plan, for the same query. From an engineering perspective, it is of critical importance to understand the prevalence of suboptimality and its causal factors. We examined the plans for thousands of queries run on four DBMSes, resulting in over a million query executions. We previously observed that the construct of empirical suboptimality prevalence positively correlated with the number of operators in the DBMS. An implication is that as operators are added to a DBMS, the prevalence of slower queries will grow. Through a novel experiment that examines the plans on the query/cardinality combinations, we present evidence for a previously unknown upper bound on the number of operators a DBMS may be able to support before performance suffers. We show that this upper bound may have already been reached.National Science Foundation12 month embargo; published: 20 September 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]