A temporal relational model and a query language
Information Sciences: an International Journal
Introduction to algorithms
Supporting fast search in time series for movement patterns in multiple scales
Proceedings of the seventh international conference on Information and knowledge management
Aggregates in the Temporal Query Language TQuel
IEEE Transactions on Knowledge and Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Efficient Algorithms for Large-Scale Temporal Aggregation
IEEE Transactions on Knowledge and Data Engineering
Supporting Content-Based Searches on Time Series via Approximation
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Incremental computation and maintenance of temporal aggregates
The VLDB Journal — The International Journal on Very Large Data Bases
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
A time machine for text search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A Greedy Approach Towards Parsimonious Temporal Aggregation
TIME '08 Proceedings of the 2008 15th International Symposium on Temporal Representation and Reasoning
Multi-dimensional aggregation for temporal data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Parsimonious temporal aggregation
The VLDB Journal — The International Journal on Very Large Data Bases
Aggregating and disaggregating flexibility objects
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Hi-index | 0.00 |
Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n2pc) time and O(n2) space, where n is the size of the input relation and p is the number of aggregation functions in the query. Second, the more efficient gPTA algorithm computes an approximate solution by greedily merging the most similar ITA result tuples, which, however, does not guarantee a compression with a minimal total error. gPTA intermingles the two steps of PTA and avoids large intermediate results. The compression step of gPTA runs in O(np log(c + δ)) time and O(c + δ) space, where δ is a small buffer for "look ahead". An empirical evaluation shows good results: considerable reductions of the result size introduce only small errors, and gPTA scales to large data sets and is only slightly worse than the exact solution of PTA.