Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Preserving Time Decompositions of Time Stamped Documents*
Data Mining and Knowledge Discovery
A segmentation-based approach for temporal analysis of software version repositories
Journal of Software Maintenance and Evolution: Research and Practice
Efficient algorithms for segmentation of item-set time series
Data Mining and Knowledge Discovery
Extracting hot spots of topics from time-stamped documents
Data & Knowledge Engineering
Efficient algorithms for constructing time decompositions of time stamped documents
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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Constructing time decompositions of time stamped documents is an important first step in extracting temporal information from a document set. Efficient algorithms are described for computing optimal lossy decompositions for a given document set, where the loss of information is constrained to be within a specified bound. A novel and efficient algorithm is proposed for computing information loss values required to construct optimal lossy decompositions. Experimental results are reported comparing optimal lossy decompositions and equal length decompositions in terms of a number of parameters such as information loss. In particular, our results show that optimal lossy decompositions outperform equal length decompositions by preserving more of the information content of the underlying document set. The results also demonstrate that permitting even small amounts of variability in the length of the subintervals of a decomposition results in capturing more of the temporal information content of a document set when compared to equal length decompositions. This paper builds upon our earlier work on time decompositions where the problem of computing optimal lossy decomposition of the time period associated with a document set was first formulated.