We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …

Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the data and …

Data-driven problem solving in many real-world applications involves analysis of time-
dependent multivariate data, for which dimensionality reduction (DR) methods are often …

Road traffic accidents are a 'global tragedy'that generates unpredictable chunks of data
having heterogeneity. To avoid this heterogeneous tragedy, we need to fraternize and …

Understanding the behavior of software in execution is a key step in identifying and fixing
performance issues. This is especially important in high performance computing contexts …

We present P6, a declarative language for building high performance visual analytics
systems through its support for specifying and integrating machine learning and interactive …

Distributed query processing systems such as Apache Hive and Spark are widely-used in
many organizations for large-scale data analytics. Analyzing and understanding the query …

Understanding and tuning the performance of extreme-scale parallel computing systems
demands a streaming approach due to the computational cost of applying offline algorithms …

Projections aim to convey the relationships and similarity of high‐dimensional data in a low‐
dimensional representation. Most such techniques are designed for static data. When used …

A common network analysis task is comparison of two networks to identify unique
characteristics in one network with respect to the other. For example, when comparing …