Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework

2016 | Management Information Systems Quarterly | Citations: 0

Authors: Pil Han, Sang; Park, Sungho; Oh, Wonseok

Abstract: The number of mobile apps launched in the market has exponentially grown to more ... Expand

Abstract: The number of mobile apps launched in the market has exponentially grown to more than 2 million, but little is known about how users choose and consume apps of numerous categories. This study develops a utility theory-based structural model for mobile app analytics. We use the theoretical concepts of utility and satiation along with the factor analytic approach, as bases in simultaneously modeling the complex relationships among choice, consumption, and utility maximization for consumers of various mobile apps. Using a unique panel dataset detailing individual user-level mobile app time consumption, we quantify the baseline utility and satiation levels of diverse mobile apps and delineate how app preferences and consumption patterns vary across demographic groups affected by persistent use and time trends. The findings suggest that users’ baseline utility substantially diverges across app categories and that their demographic characteristics and habit formation explain the appreciable heterogeneity in baseline utility and satiation. These parameters also exhibit positive and negative correlations in mobile websites and app categories. Our modeling approaches and computational methods can unlock new perspectives and opportunities for handling large-scale, micro-level data, while serving as important resources for big data analytics and mobile app analytics. Collapse

Topics: mobile application social network advertising management big data website

Methods: longitudinal research usage data multidimensional scaling descriptive statistic data modeling

Theories: utility theory

Using Domain Knowledge to Facilitate Cyber Security Analysis

2012 | Americas Conference on Information Systems | Citations: 0

Authors: He, Peng; Zhou, Lina; Karabatis, George

Abstract: Network attack classification is essential to intrusion detection in that it cou ... Expand

Abstract: Network attack classification is essential to intrusion detection in that it could improve the performance of intrusion detection system. Several machine-learning methods have been applied to correlating attacks. However, the attack classification models developed in these methods share one common limitation in that they strongly rely on the training data, which can hardly be generalized to other datasets. To address the limitation, we propose to utilize domain knowledge in form of taxonomy and ontology to improve attack correlation in cyber security application. In addition, the attack correlations generated by machine-learning techniques are expected to refine the original attack taxonomy. We evaluate the proposed methods via a series of experiments. The findings of this study suggest that domain knowledge and machine-learning technique should be mutually beneficial and complementary to each other for attack classification. Collapse

Topics: semantic network IT security intrusion detection system knowledge representation user behavior

Methods: machine learning ontological modelling experiment cluster analysis descriptive statistic

A framework for designing policies for networked systems with uncertainty

2010 | Decision Support Systems | Citations: 2

Authors: Pathak, Surya; McDonald, Mark; Mahadevan, Sankaran

Abstract: This paper presents a framework to design policies for networked systems. The fr ... Expand

Abstract: This paper presents a framework to design policies for networked systems. The framework integrates model building, stability analysis of dynamic systems, surrogate model generation and optimization under uncertainty. We illustrate the framework using a transportation network benchmark problem. We consider bounded rational users and model the network using software agents. We use Largest Lyapunov exponents to characterize stability and use Gaussian process model as an inexpensive surrogate, facilitating computational efficiency in policy optimization under uncertainty. We demonstrate scalability by solving a traffic grid policy design problem and show how the framework lends itself towards carrying out stability versus performance tradeoffs. Collapse

Topics: IT investment user behavior software agent decision making

Methods: simulation agent based simulation experiment computational algorithm time series analysis