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

Spatio-temporal set-based constraints in conceptual modeling: A theoretical framework and evaluation

The objective of this research is to define and evaluate a framework and detailed specifications for capturing spatio-temporal set-based constraints during conceptual database design. The proposed framework aims to bridge the gap between rule discovery during requirements analysis and rule implementation during the logical design phase. It also aids conceptual design by providing the analyst with a structured way to determine rules from the client. Our research approach takes advantage of multiple research methods in: (i) defining a formal framework and using it to specify spatio-temporal constraints employing set theory and first order logic, (ii) evaluating expressiveness of the proposed framework using a case study, and (iii) evaluating user analyst performance and perceived ease of use via a laboratory experiment. A case study at the Internal Review Board of a major public university demonstrates that the framework is expressive enough to capture discovered rules in this application, and that the rules can be translated to schema constraints at the logical level. The experiment provides answers regarding analyst performance using the framework, perceived ease of use, and explores the utility of the framework as a structured approach to elicit rules from clients. Future work includes additional case studies to test the framework, experiments to establish perceived usefulness from the perspective of real-world analysts, and broadening the framework to incorporate additional types of rules

Understanding Semantic Completeness in Rule Frameworks for Modeling Cardinality Constraints

Modeling organizational rules during conceptual design provides a more accurate picture of the underlying domain and helps enforce data integrity. In a database development context, there are many advantages to explicitly representing rules during conceptual design. Early modeling ensures they are visible to designers and users, thus improving requirements and validation. The rules can then be semi-automatically translated into logical design code. One limitation to widespread adoption of such modeling is variance in standards and semantics of rules. We consider cardinality constraints-a useful and integral part of conceptual database design. Many papers discussing classification frameworks for cardinality exist. Completeness of such schemes has always been in question since well-defined criteria do not exist to evaluate them. We suggest a "reverse engineering" approach, i.e., one of defining conceptual modeling constraint completeness based on mappings from the relational model. We develop a correspondence from relational algebra operator combinations to existing semantic constraint types. In doing so, we also come up with a new category of set-level cardinality constraints not previously examined in literature. We believe our work demonstrates a unique approach to establishing conceptual framework completeness and enables standardization of rule semantics which in turn allows for semantics-based (as opposed to procedural-based) representation. On the implementation side, it supports developing automated mechanisms for translating constraints to improve developer productivity.Open access journalThis 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]

Understanding Semantic Completeness in Rule Frameworks for Modeling Cardinality Constraints

Modeling organizational rules during conceptual design provides a more accurate picture of the underlying domain and helps enforce data integrity. In a database development context, there are many advantages to explicitly representing rules during conceptual design. Early modeling ensures they are visible to designers and users, thus improving requirements and validation. The rules can then be semi-automatically translated into logical design code. One limitation to widespread adoption of such modeling is variance in standards and semantics of rules. We consider cardinality constraints-a useful and integral part of conceptual database design. Many papers discussing classification frameworks for cardinality exist. Completeness of such schemes has always been in question since well-defined criteria do not exist to evaluate them. We suggest a "reverse engineering" approach, i.e., one of defining conceptual modeling constraint completeness based on mappings from the relational model. We develop a correspondence from relational algebra operator combinations to existing semantic constraint types. In doing so, we also come up with a new category of set-level cardinality constraints not previously examined in literature. We believe our work demonstrates a unique approach to establishing conceptual framework completeness and enables standardization of rule semantics which in turn allows for semantics-based (as opposed to procedural-based) representation. On the implementation side, it supports developing automated mechanisms for translating constraints to improve developer productivity.Open access journalThis 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]

What Makes an Online Suggestion A Good One for Online Health Communities

An increasing number of people search online for health information or suggestions when they are confronted with health problems. However, identifying good suggestions when inundated with a wide variety of responses is a big challenge. Most online forums don’t provide an automated suggestion-rating procedure for users to locate the quality suggestions that meet their expectations. In this study, we focus on the problem of identifying good suggestions. We propose a novel framework that accounts for the dynamic nature of social media by modeling the evolution of features over time. We use a combination of LSTM time series prediction of temporal features and Adaptive Thresholding Normalization to address this problem. Our study discusses why evolving language features need to be considered to determine the quality of suggestions. Besides, our method can identify important language features that can boost the prediction ability of the best suggestions