The visual display of quantitative information
The visual display of quantitative information
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive query processing for time-series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Towards effective and interpretable data mining by visual interaction
ACM SIGKDD Explorations Newsletter
Monotony of surprise and large-scale quest for unusual words
Proceedings of the sixth annual international conference on Computational biology
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
Indexing and Mining of the Local Patterns in Sequence Database
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Interactive Exploration of Time Series Data
DS '01 Proceedings of the 4th International Conference on Discovery Science
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Efficient Searches for Similar Subsequences of Different Lengths in Sequence Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Visualizing Time-Series on Spirals
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Aligning temporal data by sentinel events: discovering patterns in electronic health records
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LiveRAC: interactive visual exploration of system management time-series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Discovering patterns in categorical time series using IFS
Computational Statistics & Data Analysis
Efficiently finding unusual shapes in large image databases
Data Mining and Knowledge Discovery
Stock time series visualization based on data point importance
Engineering Applications of Artificial Intelligence
An efficient stream mining technique
WSEAS Transactions on Information Science and Applications
An efficient time series data mining technique
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Visual data mining of multimedia data for social and behavioral studies
Information Visualization
Relaxed selection techniques for querying time-series graphs
Proceedings of the 22nd annual ACM symposium on User interface software and technology
A data mining framework for time series estimation
Journal of Biomedical Informatics
A framework for time-series analysis
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
SciQL: bridging the gap between science and relational DBMS
Proceedings of the 15th Symposium on International Database Engineering & Applications
Representing unevenly-spaced time series data for visualization and interactive exploration
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
On the dataset shift problem in software engineering prediction models
Empirical Software Engineering
A method of similarity measure and visualization for long time series using binary patterns
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Distributed distance matrix generator based on agents
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
A spectral visualization system for analyzing financial time series data
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
Time series visualization based on shape features
Knowledge-Based Systems
Trace selection for interactive evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
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Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of dollars, not including the cost in morale and other more intangible detriments. The Aerospace Corporation is responsible for providing engineering assessments critical to the go/no-go decision for every Department of Defense space vehicle. These assessments are made by constantly monitoring streaming telemetry data in the hours before launch. We will introduce VizTree, a novel time-series visualization tool to aid the Aerospace analysts who must make these engineering assessments. VizTree was developed at the University of California, Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry. The use of a single tool for both aspects of the task allows a natural and intuitive transfer of mined knowledge to the monitoring task. Our visualization approach works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic datasets.