Temporal knowledge discovery from time-based data, is crucial to investigate problems in the data science era. Meaningful progress has been made computationally in the discovery process of frequent temporal patterns, which may store potentially meaningful knowledge. However, for temporal knowledge discovery and acquisition, effective visualization is essential and still stores much room for contribution. While visualization of frequent temporal patterns was relatively under researched, it stores meaningful opportunities in facilitating usable ways to assist domain experts in exploring and acquiring temporal knowledge. Typically, a frequent temporal patterns mining process results with an enumeration tree of frequent discovered patterns, which is quite challenging to browse in order to acquire meaningful knowledge. In this talk we introduce approaches for the visualization of frequent temporal patterns, and demonstrate them specifically on Time Intervals Related Patterns (TIRPs). TIRPs can be discovered from raw symbolic time intervals, or from multivariate heterogeneous temporal data that were abstracted into symbolic time intervals representation using temporal abstraction. We will introduce KLW, and specifically KLW3, a novel approach for the visualization of an enumeration tree of frequent temporal patterns for exploration and knowledge acquisition from a tree of frequent patterns. KLW enables to browse through the enumeration tree, as well as search for TIRPs given a criteria, and visualize them using a bubble chart that provides a wide view in a glimpse of the discovered TIRPs collection. Moreover, it enables to demonstrate the TIRPs that were discovered in a single population, or in two population, and show their corresponding metrics. KLW3 enables users to investigate specific TIRPs and move forward and backwards between the TIRPs according to their symbolic time intervals sequences within the tree, to better understand the relations, as will be demonstrated. Finally, a comprehensive and rigorous user study on two real-life datasets, demonstrating the usability advantages of the novel approaches will be presented.