Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd 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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
The 8 requirements of real-time stream processing
ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Automatic outlier detection for time series: an application to sensor data
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique
ICNS '07 Proceedings of the Third International Conference on Networking and Services
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting distance-based outliers in streams of data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Using Data Mining to Estimate Missing Sensor Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Detecting Current Outliers: Continuous Outlier Detection over Time-Series Data Streams
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Detection and Exploration of Outlier Regions in Sensor Data Streams
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
ORDEN: outlier region detection and exploration in sensor networks
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
An adaptive outlier detection technique for data streams
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Online outlier detection for data streams
Proceedings of the 15th Symposium on International Database Engineering & Applications
Efficient estimation of dynamic density functions with an application to outlier detection
Proceedings of the 21st ACM international conference on Information and knowledge management
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Data stream is a newly emerging data model for applications like environment monitoring, Web click stream, network traffic monitoring, etc. It consists of an infinite sequence of data points accompanied with timestamp coming from external data source. Typically data sources are located onsite and very vulnerable to external attacks and natural calamities, thus outliers are very common in the datasets. Existing techniques for outlier detection are inadequate for data streams because of its metamorphic data distribution and uncertainty. In this paper we propose an outlier detection technique, called Distance-Based Outline Detection for Data Streams (DBOD-DS) based on a novel continuously adaptive probability density function that addresses all the new issues of data streams. Extensive experiments on a real dataset for meteorology applications show the supremacy of DBOD-DS over existing techniques in terms of accuracy.