Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
An introduction to wavelets
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Disk aware discord discovery: finding unusual time series in terabyte sized datasets
Knowledge and Information Systems
ACM Computing Surveys (CSUR)
IBM infosphere streams for scalable, real-time, intelligent transportation services
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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The ability to process and analyze large volumes of time-series data is in increasing demand in various domains including health care, finance, energy and utilities, transportation, and cybersecurity. Despite the broad use of time-series data worldwide, the design of a system to easily manage, analyze, and visualize large multidimensional time series, with dimensions on the order of hundreds of thousands, is still a challenging endeavor. This paper describes the Streaming Time-Series Analysis and Management (STAM) system as a solution to this problem. STAM provides the capability to glean actionable information from continuously changing time series with thousands of dimensions, in real time. STAM exploits the IBM InfoSphere® Streams platform and allows for general-purpose large-scale time-series analytics for applications including anomaly detection, modeling, smoothing, forecasting, and tracking. In addition, the system provides user-friendly tools for managing, deploying, and initiating analytics on large-scale data streams of interest, and provides a web-based graphical visualization interface that allows highlighting of events of interest with interactive menus. In this paper, we describe the system and illustrate its use in a large-scale system-monitoring application.