This paper introduces an approach to analyzing multivariate time series (MVTS) data
through progressive temporal abstraction of the data into patterns characterizing the …

In geo-related fields such as urban informatics, atmospheric science, and geography, large-
scale spatial time (ST) series (ie, geo-referred time series) are collected for monitoring and …

Planning an ideal tour schedule is a tedious process, where the attractions to visit and the
order of visits need to be carefully determined. In this paper, we propose a novel interactive …

Numerous patterns found in urban phenomena, such as air pollution and human mobility,
can be characterized as many directed geospatial networks (geo-networks) that represent …

Querying time series based on their relations is a crucial part of multiple time series analysis.
By retrieving and understanding time series relations, analysts can easily detect anomalies …

Spatial time series visualization offers scientific research pathways and analytical decision-
making tools across various spatiotemporal domains. Despite many advanced …

Finding the root causes of anomalies in cloud computing systems quickly is crucial to ensure
availability and efficiency since accurate root causes can guide engineers to take …

This paper studies the causes and distribution of reconstruction errors in subtractive dither
structure and provides solution to minimize these errors. We present the generation of …

A power plant is a complex real-world system associated with rich multidimensional data
relevant to its construction and activity. Thus, choosing an appropriate way to visualize …

Aiming at the problem that the machine tool operation data has many dimensions, the
parameters relationship is complex, and its abnormal patterns and hidden correlation …