Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
Time series: theory and methods
Time series: theory and methods
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Data mining on an OLTP system (nearly) for free
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
MEMS-based integrated-circuit mass-storage systems
Communications of the ACM
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
Characterizing memory requirements for queries over continuous data streams
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Wavelet synopses with error guarantees
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Temporal Aggregation over Data Streams Using Multiple Granularities
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Cost-efficient mining techniques for data streams
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Finding hot query patterns over an XQuery stream
The VLDB Journal — The International Journal on Very Large Data Bases
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
AutoLag: Automatic Discovery of Lag Correlations in Stream Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
One-pass wavelet synopses for maximum-error metrics
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ACM SIGMOD Record
Finding Periodic Outliers over a Monogenetic Event Stream
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
WARP: Time Warping for Periodicity Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Online clustering of parallel data streams
Data & Knowledge Engineering
Time series compressibility and privacy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient instance-based learning on data streams
Intelligent Data Analysis
Asynchronous in-network prediction: Efficient aggregation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Two heads better than one: pattern discovery in time-evolving multi-aspect data
Data Mining and Knowledge Discovery
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Identifying Similar Subsequences in Data Streams
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Data Streaming with Affinity Propagation
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Adaptive burst detection in a stream engine
Proceedings of the 2009 ACM symposium on Applied Computing
DynaMMo: mining and summarization of coevolving sequences with missing values
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
BGP-lens: patterns and anomalies in internet routing updates
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
MG-join: detecting phenomena and their correlation in high dimensional data streams
Distributed and Parallel Databases
Fast Discovery of Group Lag Correlations in Streams
ACM Transactions on Knowledge Discovery from Data (TKDD)
Sequential Modeling of Topic Dynamics with Multiple Timescales
ACM Transactions on Knowledge Discovery from Data (TKDD)
Resource adaptive periodicity estimation of streaming data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Pattern-based event detection in sensor networks
Distributed and Parallel Databases
Fast mining and forecasting of complex time-stamped events
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern discovery in data streams under the time warping distance
The VLDB Journal — The International Journal on Very Large Data Bases
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
On clustering large number of data streams
Intelligent Data Analysis
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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Sensor devices and embedded processors are becoming ubiquitous. Their limited resources (CPU, memory and/or communication bandwidth and power) pose some interesting challenges. We need both powerful and concise "languages" to represent the important features of the data, which can (a) adapt and handle arbitrary periodic components, including bursts, and (b) require little memory and a single pass over the data. We propose AWSOM (Arbitrary Window Stream mOdeling Method), which allows sensors in remote or hostile environments to efficiently and effectively discover interesting patterns and trends. This can be done automatically, i.e., with no user intervention and expert tuning before or during data gathering. Our algorithms require limited resources and can thus be incorporated in sensors, possibly alongside a distributed query processing engine [9, 5, 22]. Updates are performed in constant time, using logarithmic space. Existing, state of the art forecasting methods (SARIMA, GARCH, etc) fall short on one or more of these requirements. To the best of our knowledge, AWSOM is the first method that has all the above characteristics. Experiments on real and synthetic datasets demonstrate that AWSOM discovers meaningful patterns over long time periods. Thus, the patterns can also be used to make long-range forecasts, which are notoriously difficult to perform. In fact, AWSOM outperforms manually set up auto-regressive models, both in terms of long-term pattern detection and modeling, as well as by at least 10× in resource consumption.