Intervention Events Detection and Prediction in Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Lacking Labels in the Stream: Classifying Evolving Stream Data with Few Labels
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Short-term prediction models for server management in Internet-based contexts
Decision Support Systems
An algorithmic approach to event summarization
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Adaptive system anomaly prediction for large-scale hosting infrastructures
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
An efficient approach for mining segment-wise intervention rules in time-series streams
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Classification and novel class detection of data streams in a dynamic feature space
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Finding semantics in time series
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
INFORMS Journal on Computing
Cloud-based malware detection for evolving data streams
ACM Transactions on Management Information Systems (TMIS)
Online outlier detection for data streams
Proceedings of the 15th Symposium on International Database Engineering & Applications
Classification and novel class detection in data streams with active mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Content-based crowd retrieval on the real-time web
Proceedings of the 21st ACM international conference on Information and knowledge management
Automated Anomaly Detector Adaptation using Adaptive Threshold Tuning
ACM Transactions on Information and System Security (TISSEC)
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Many applications are driven by evolving data - patterns in Web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction models for evolving data becomes an important and challenging task. Currently, most approaches work by "chasing trends", that is, they keep learning or updating models from the evolving data, and use these impromptu models for online prediction. In many cases, this proves to be both costly and ineffective - much time is wasted on re-learning recurring concepts, yet the classifier may remain one step behind the current trend all the time. In this paper, we propose to mine high-order models in evolving data. More often than not, there are a limited number of concepts, or stable distributions, in the data stream, and concepts switch between each other constantly. We mine all such concepts offline from a historical stream, and build high quality models for each of them. At run time, combining historical concept change patterns and cues provided by an online training stream, we find the most likely current concept and use its corresponding models to classify data in an unlabeled stream. The primary advantage of the high-order model approach is its high accuracy. Experiments show that in benchmark datasets, classification error of the high-order model is only a small fraction of that of the current best approaches. Another important benefit is that, unlike state-of-the-art approaches, our approach does not require users to tune any parameters to achieve a satisfying result on streams of different characteristics.