Methods and systems for optimized visual summarization for sequences of temporal event data

Abstract

A method for generating a graphical depiction of summarized event sequences includes receiving a plurality of event sequences, each event sequence in the plurality of event sequences including a plurality of events, and generating a plurality of clusters using a minimum description length (MDL) optimization process. Each cluster in the plurality of clusters including a set of at least two event sequences in the plurality of event sequences that maps to a pattern in each cluster. The pattern in each cluster further includes a plurality of events included in at least one event sequence in the set of at least two event sequences in the cluster. The method includes generating a graphical depiction of a first cluster in the plurality of clusters, the graphical depiction including a graphical depiction of a first plurality of events in the pattern of the first cluster.

Description

[0001] priority claim [0002] This application claims the benefit of U.S. Provisional Application No. 62 / 537,621, entitled "Sequence Synopsis: Optimize Visual Summary of Temporal Event Data," and filed July 27, 2017, the entire contents of which are hereby incorporated by reference. technical field [0003] The present disclosure relates generally to the field of computer graphics, and more particularly, to systems and methods for generating overview graphical displays of data sequences. Background technique [0004] Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. [0005] Sequence of events data (ie, multiple series of time-stamped or ordered events) is increasingly common across a wide range of domains. Website clickstreams, user interaction logs in software applications, electronic health records (EHRs) in healthcare, and vehi...

AI Technical Summary

Problems solved by technology

However, while modern computing hardware can produce graphical depictions of very large collections of event sequences, the display of such complex information often overwhelms human users, making the data less useful for analysis

The display of large collections of event sequences of real-world data often produces visual "clutter" due to the noisy and complex nature of event sequences with high event cardinality, which presents challenges for constructing concise yet comprehensive reviews on such data. took the challenge

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