Identifying and Measuring Associations of Temporal Events (original) (raw)
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Finding patterns of events over time is important in searching patient histories, web logs, news stories, and criminal activities. This paper presents PatternFinder, an integrated interface for query and result-set visualization for search and discovery of temporal patterns within multivariate and categorical data sets. We define temporal patterns as sequences of events with interevent time spans. PatternFinder allows users to specify the attributes of events and time spans to produce powerful pattern queries that are difficult to express with other formalisms. We characterize the range of queries PatternFinder supports as users vary the specificity at which events and time spans are defined. Pattern Finder's query capabilities together with coupled ball-andchain and tabular visualizations enable users to effectively query, explore and analyze event patterns both within and across data entities (e.g. patient histories, terrorist groups, web logs, etc.).
A method for discovery and analysis of temporal patterns in complex event data
International Journal of Geographical Information Science, 2015
Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011-2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.
A Visual Analytics Approach to Comparing Cohorts of Event Sequences
2016
Title of dissertation: A VISUAL ANALYTICS APPROACH TO COMPARING COHORTS OF EVENT SEQUENCES Sana Malik, Doctor of Philosophy, 2016 Dissertation directed by: Professor Ben Shneiderman Department of Computer Science Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the str...
ICE: Identify and Compare Event Sequence Sets through Multi-Scale Matrix and Unit Visualizations
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Comparative analysis of event sequence data is essential in many application domains, such as website design and medical care. However, analysts often face two challenges: they may not always know which sets of event sequences in the data are useful to compare, and the comparison needs to be achieved at different granularity, due to the volume and complexity of the data. This paper presents, ICE, an interactive visualization that allows analysts to explore an event sequence dataset, and identify promising sets of event sequences to compare at both the pattern and sequence levels. More specifically, ICE incorporates a multi-level matrix-based visualization for browsing the entire dataset based on the prefixes and suffixes of sequences. To support comparison at multiple levels, ICE employs the unit visualization technique, and we further explore the design space of unit visualizations for event sequence comparison tasks. Finally, we demonstrate the effectiveness of ICE with three real...
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Artificial Intelligence in Medicine, 1994
We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of these objects. We found the tools well-suited to define scenarios of events that are evidence of inappropriate use of prescription drugs, using Medicaid administrative data that describe medical events. The tools' usefulness in research might be considerably more general.
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arXiv (Cornell University), 2020
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 network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.
Data & Knowledge Engineering, 2007
Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. In this paper we outline a new algorithm, ARMADA, to discover frequent temporal patterns and to generate richer interval-based temporal association rules. In addition, we introduce a maximum gap time constraint that can be used to get rid of insignificant patterns and rules so that the number of generated patterns and rules can be reduced. Synthetic datasets are utilized to assess the performance of the algorithm.
Discovering Richer Temporal Association Rules from Interval-Based Data
Lecture Notes in Computer Science, 2005
Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. In this paper we outline a new algorithm to discover frequent temporal patterns and to generate richer interval-based temporal association rules. We introduce a maximum gap time constraint to reduce the number of generated patterns. Synthetic datasets are utilized to assess the performance of the algorithm.
Simplifying Overviews of Temporal Event Sequences
Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2016
Beginning the analysis of new data is often difficult as modern datasets can be overwhelmingly large. With visual analytics in particular, displays of large datasets quickly become crowded and unclear. Through observing the practices of analysts working with the event sequence visualization tool EventFlow, we identified three techniques to reduce initial visual complexity by reducing the number of event categories resulting in a simplified overview. For novice users, we suggest an initial pair of event categories to display. For advanced users, we provide six ranking metrics and display all pairs in a ranked list. Finally, we present the Event Category Matrix (ECM), which simultaneously displays overviews of every event category pair. In this work, we report on the development of these techniques through two formative usability studies and the improvements made as a result. The goal of our work is to investigate strategies that help users overcome the challenges associated with initial visual complexity and to motivate the use of simplified overviews in temporal event sequence analysis.