Meaningful change detection in structured data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Hancock: a language for processing very large-scale data
Proceedings of the 2nd conference on Domain-specific languages
Evolution and change in data management — issues and directions
ACM SIGMOD Record
Hancock: a language for extracting signatures from data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Research Issues in Spatio-temporal Database Systems
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Issues in data stream management
ACM SIGMOD Record
Dynamic Histograms: Capturing Evolving Data Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Efficient and effective explanation of change in hierarchical summaries
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
A framework for flexible clustering of multiple evolving data streams
International Journal of Advanced Intelligence Paradigms
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
Multivariable stream data classification using motifs and their temporal relations
Information Sciences: an International Journal
HE-Tree: a framework for detecting changes in clustering structure for categorical data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient decision tree construction for mining time-varying data streams
CASCON '09 Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research
Anomaly intrusion detection for evolving data stream based on semi-supervised learning
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Towards subspace clustering on dynamic data: an incremental version of PreDeCon
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Time-Slice Density Estimation for Semantic-Based Tourist Destination Suggestion
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Tracing evolving clusters by subspace and value similarity
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Summarizing cluster evolution in dynamic environments
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Density based subspace clustering over dynamic data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
The algorithm APT to classify in concurrence of latency and drift
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Tracing Evolving Subspace Clusters in Temporal Climate Data
Data Mining and Knowledge Discovery
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Discovering emerging topics in unlabelled text collections
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments
International Journal of Data Warehousing and Mining
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. This results in databases which grow without limit at a rapid rate. This data can often show important changes in trends over time. In such cases, it is useful to understand, visualize, and diagnose the evolution of these trends. In this paper, we introduce the concept of velocity density estimation, a technique used to understand, visualize, and determine trends in the evolution of fast data streams. We show how to use velocity density estimation in order to create both temporal velocity profiles and spatial velocity profiles at periodic instants in time. These profiles are then used in order to predict three kinds of data evolution: dissolution, coagulation, and shift. Methods are proposed to visualize the changing data trends in a single online scan of the data stream and a computational requirement which is linear in the number of data points. The visualization techniques can also be used to provide online animations which show the changes in the data characteristics while they occur. In addition, batch processing techniques are proposed in order to quantify the level of change across different combinations of dimensions. This quantification is then used in order to determine dimensional combinations with significant evolution. The techniques discussed in this paper can be easily extended to spatiotemporal data, changes in data snapshots at fixed instances in time, or any other data which has a temporal component during its evolution.