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The purpose of this paper is to help industrial managers monitor and analyze critical performance indicators in real time during the execution of business processes by proposing a visualization technique using an extended formal concept analysis (FCA). The proposed approach monitors the current progress of ongoing processes and periodically predicts their probable routes and performances.

FCA is utilized to analyze relations among patterns of events in historical process logs, and this method of data analysis visualizes the relations in a concept lattice. To apply FCA to real‐time business process monitoring, the authors extended the conventional concept lattice into a reachability lattice, which enables managers to recognize reachable patterns of events in specific instances of business processes.

By using a reachability lattice, expected values of a target key performance indicator are predicted and traced along with probable outcomes. Analysis is conducted periodically as the monitoring time elapses over the course of business processes.

The proposed approach focuses on the visualization of probable event occurrences on the basis of historical data. Such visualization can be utilized by industrial managers to evaluate the status of any given instance during business processes and to easily predict possible subsequent states for purposes of effective and efficient decision making. The proposed method was developed in a prototype system for proof of concept and has been illustrated using a simplified real‐world example of a business process in a telecommunications company.

The main contribution of this paper lies in the development of a real‐time monitoring approach of ongoing processes. The authors have provided a new data structure, namely a reachability lattice, which visualizes real‐time progress of ongoing business processes. As a result, current and probable next states can be predicted graphically using periodically conducted analysis during the processes.

Revenue maximization through improving click‐throughs is of great importance for price comparison shopping services (PCSSs) whose revenues directly depend on the number of click‐throughs of items in their itemsets. The purpose of this paper is to present an approach aiming to maximize the revenue of a PCSS by proposing effective itemset construction methods that can maximize the click‐throughs.

The authors suggest three itemset construction methods, namely naïve method (NM), exhaustive method (EM), and local update method (LM). Specifically, NM searches for the best itemset for an item in terms of textual similarity between an item and an itemset, while EM produces the best itemset for each item for maximizing click‐throughs by considering all the possible memberships of the item. Finally, through combining NM and EM, the authors propose an LM that attempts to improve click‐throughs by locally updating the memberships of items according to their ranks in each itemset.

Through evaluation of the proposed methods based on a real‐world dataset, it has been found that improvement of click‐throughs is small when itemsets are constructed by using the textual similarity alone. However, significant improvement in the number of click‐throughs was achieved when considering items' membership updates dynamically.

Unlike the previous studies that mainly focus on the textual similarity, the authors attempt to maximize the revenue through constructing itemsets that can result in more click‐throughs. By using the proposed methods, it is expected that PCSSs will be able to automatically construct itemsets that can maximize their revenues without the need for manual task.