StreamStory: Exploring Multivariate Time Series on Multiple Scales

Abstract

This paper presents an approach for the interactive visualization, exploration and interpretation of large multivariate time series. Interesting patterns in such datasets usually appear as periodic or recurrent behavior often caused by the interaction between variables. To identify such patterns, we summarize the data as conceptual states, modeling temporal dynamics as transitions between the states. This representation can visualize large datasets with potentially billions of examples. We extend the representation to multiple spatial granularities allowing the user to find patterns on multiple scales. The result is an interactive web-based tool called StreamStory. StreamStory couples the abstraction with several tools that map the abstractions back to domain-specific concepts using techniques from statistics and machine learning. It is aimed at users who are not experts in data analytics, minimizing the number of parameters to configure out-of-the-box. We use three real-world datasets to demonstrate how StreamStory can be used to perform three main visual analytics tasks: identify the main states of a complex system and map them back to data-specific concepts, find high-level and long-term periodic behavior and traverse the scales to identify which scales exhibit interesting phenomena. We find and interpret several known, as well as previously unknown patterns in these datasets.

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

“…In future, we plan to complete our on-going platform for generic big data analytics which will be capable of processing various types of data for a wide range of applications. We also plan to incorporate multivariate visualization [59], graph mining techniques [54] and fine-grained spatial analysis [60] to uncover more potential patterns and trends within these datasets. Moreover, we aim to conduct more realistic case studies to further evaluate the effectiveness and scalability of the different models in our system.…”

Navigating Complexities: Agent-Based Modeling to Support Research, Governance, and Management in Small-Scale Fisheries

“…Agent-based modeling 1 is a dynamic, computational approach that represents the actions and interactions of agents and their environment, and simulates how these result in emergent patterns and relationships (Sterman, 2001). In ABMs it is possible to represent agents of different types, their heterogeneity, and the interactions between agents and their environment over time (Box 1).…”

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

“…This identification of regular configurations and distributions over time is represented by a total number of events and behaviors extracted from a chosen spatial scale. Personal mobility behaviors and movement patterns [73]- [81], behaviors of animals [82], [83], pattern changes in climate (weather) and the ozone layer [81], [84]- [90], and behavior capture data made through time at often uniform time intervals [91]- [96] can be regarded as instances for this type of data structure that take a place in specific spatial identification.…”

“…From a data mining perspective, Aghabozorgi and Shirkhorshidi [12] state that Euclidean Distance and DTW are the most popular distance measures in time series data; however, Euclidian Distance is the most widely used distance measure in the surveyed visual analytics papers e.g. [34], [43], [44], [50], [52], [53], [56], [57], [59], [60], [66], [67], [69], [70], [75], [81], [85], [88]- [91], [93], [95], [96], [113]- [115] as it is the most straightforward distance measure compared to others. DTW has only been used in [48], [53], [56], [79] to calculate the similarity of time series data, and papers [34], [35], [61], [72], [83], [85], [86] use correlation and cross-correlation in their works.…”