Fundamentals of speech recognition
Fundamentals of speech recognition
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Characteristics of Scientific Databases
VLDB '84 Proceedings of the 10th International Conference on Very Large Data Bases
Algebraic Optimization of Computations over Scientific Databases
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Scientific Databases - State of the Art and Future Directions
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Multilevel Grid File - A Dynamic Hierarchical Multidimensional File Structure
Proceedings of the Second International Symposium on Database Systems for Advanced Applications
A review on time series data mining
Engineering Applications of Artificial Intelligence
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In order to handle time-series patterns efficiently, a database system needs an index mechanism that will help it retrieve patterns quickly according to their temporal and spatial characteristics. Time-series patterns are represented as a sequence of points in multidimensional spaces, however traditional indexing methods are not well suited to support this requirement. In this paper, we propose a dynamic index structure called a TIP-index (TIme-series Pattern index) for efficient manipulation of time-series pattern databases. The TIP-index is developed by improving the extended multidimensional dynamic index file(EMDF). We present the structure and give algorithms for searching and inserting in it. We compare the performance of TIP-index with that of EMDF. The results indicate that insertion and search performance is improved, and we conclude that the TIP-index is efficient for applications in time-series patterns.