Event sequence data record series of discrete events in the time order of occurrence. They
are commonly observed in a variety of applications ranging from electronic health records to …

We present a systematic review on three comparative layouts-juxtaposition, superposition,
and explicit-encoding-which are information visualization (InfoVis) layouts designed to …

Online services are increasingly dependent on user participation. Whether it's online social
networks or crowdsourcing services, understanding user behavior is important yet …

Deep Q-Network (DQN), as one type of deep reinforcement learning model, targets to train
an intelligent agent that acquires optimal actions while interacting with an environment. The …

Modern web clickstream data consists of long, high-dimensional sequences of multivariate
events, making it difficult to analyze. Following the overarching principle that the visual …

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial
internet) as modern machines and assembly lines generate large amounts of data and …

Event sequence mining is often used to summarize patterns from hundreds of sequences
but faces special challenges when handling racket sports data. In racket sports (eg, tennis …

Event sequences analysis plays an important role in many application domains such as
customer behavior analysis, electronic health record analysis and vehicle fault diagnosis …

Event sequence data such as electronic health records, a person's academic records, or car
service records, are ordered series of events which have occurred over a period of time …

Event sequence data is increasingly available in various application domains, such as
business process management, software engineering, or medical pathways. Processes in …