Introduction to the Special Issue on Pattern-Driven Mining, Analytics, and Prediction for Decision Making, Part II

Data Mining is an analytic process to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. More specifically, pattern-driven mining, analytics, and prediction have received a lot of attention in the last two decades since information discovered in data can be used to support decision and strategy making. The results can also be utilized in decision support or information management system (IMS). Different types of patterns and knowledge can be mined (extracted) from various applications and domains. Many previous studies focused on designing and implementing new methodologies to handle different constraints and requirements. This special issue focuses on discovering the knowledge, rules, and information for decision support and management information systems. Innovative methodologies, principles, methods, techniques, framework, theory, and applications are thus considered to deal with the challenges for decision support and management information systems. In this special issue, there were 47 submissions. For Part II, we are publishing seven articles. All accepted manuscripts have made a significant scientific contribution and presented a rigorous evaluation of the Information Systems outcomes in real-world practices and analysis. The summary of the accepted papers is stated below.

In “A Quantitative Comparative Study of Data-oriented Trust Management Schemes in Internet of Things” [1], the authors survey, analyze, and compare several approaches that have been taken in building trust management systems for the Internet of Things (IoT). The features of such systems are then analyzed and studied and extensive comparisons by simulation are then implemented to demonstrate the similarities and discrepancies of current IoT trust management schemes and extract the essence of a resilient trust management framework.

In “OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining” [2], the authors address a self-adaptive One-off Weak-gap Strong Pattern (OWSP) mining and design an OWSP-Miner and the reverse-order filling strategy to support calculation and generate candidate patterns. Time-series data is employed in the experiments and the results show that OWSP-Miner is not only more efficient but also easier to mine valuable patterns. Results show that OWSP mining is more meaningful in real life. In addition, the developed algorithm is available and can be accessed from the GitHub repository.

In “Feature Extraction of High-dimensional Data Based on J-HOSVD for Cyber-physicalsocial Systems” [3], the authors propose two general dimension reduction and feature extraction methods for high-dimensional data based on joint tensor decomposition, namely core feature extraction methods and factor feature extraction methods, which can effectively mine out the common components and hidden patterns of high-dimensional data by joint analysis while maintaining the original data structure. Results show the effectiveness of the developed model from both theoretical and practical aspects.

In “A Decision Framework to Recommend Cruising Locations for Taxi Drivers under the Constraint of Booking Information” [4], the authors propose a path decision framework that considers real-time spatial-temporal predictions and traffic network information. The designed model aims to optimize a taxi driver's profit when considering a reservation. In addition, the designed search scheme in H* can decrease the computing time and allow the search process to be more efficient. The authors claim that this is the first work focused on guiding a route, which can increase the income of taxi drivers under the constraint of booking information.

In “Configurable Batch-processing Discovery from Event Logs” [5], the authors present a novel approach for the identification of batching behavior from process execution data recorded in event logs. The approach can discover different types of batch processing behaviors and allows users to configure batch processing characteristics they are interested in. The approach is implemented and evaluated through experiments with synthetic event logs and case studies with real-life event logs. Results demonstrate that the approach can identify various batch processing behaviors in the context of business processes.

In “Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection” [6], the authors present a novel anomaly detection approach called Anomaly Detection Using Feature Selection (ADUFS) using feature selection (FS) based on cooperative co-evolution on cybersecurity datasets for anomaly detection. The designed model investigated both supervised and unsupervised anomaly detection techniques with and without feature selection on five cybersecurity datasets. The experimental results indicate that instead of using the original dataset, a dataset with a reduced number of features yields better performance than the existing techniques for anomaly detection. 

In “An Evolutive Frequent Pattern Tree-based Incremental Knowledge Discovery Algorithm” [7], the authors propose a novel knowledge discovery algorithm based on double evolving frequent pattern trees that can trace the dynamically evolving data by an incremental sliding window. One tree is used to record frequent patterns from the historical data, and the other one records incremental frequent items. The structures of the double frequent pattern trees and their relationships are updated periodically according to the emerging data and a sliding window. New frequent patterns are discovered from the incremental data, and new knowledge can be obtained from pattern changes. Results show that the developed algorithm can discover new knowledge from evolving data with good performance and high accuracy.

Overall, with a highly respected and well-known journal like ACM Transactions on Information Management, the quality of submissions was exceptional. Combining the concepts of information management and decision making in pattern-driven mining, analytics, and prediction, it has only just begun and this Special Issue as a whole can pave the way for future research avenues in this highly important interdisciplinary area of study.