PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ARIMA time series modeling and forecasting for adaptive I/O prefetching
ICS '01 Proceedings of the 15th international conference on Supercomputing
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Supporting frequent updates in R-trees: a bottom-up approach
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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This paper introduces a new approach for efficiently indexing and querying constantly evolving data. Traditional data index structures suffer from frequent updating cost and result in unsatisfactory performance when data changes constantly. Existing approaches try to reduce index updating cost by using a simple linear or recursive function to define the data evolution, however, in many applications, the data evolution is far too complex to be accurately described by a simple function. We propose to take each constantly evolving data as a time series and use the ARIMA (Autoregressive Integrated Moving Average) methodology to analyze and model it. The model enables making effective forecasts for the data. The index is developed based on the forecasting intervals. As long as the data changes within its corresponding forecasting interval, only its current value in the leaf node needs to be updated and no further update needs to be done to the index structure. The model parameters and the index structure can be dynamically adjusted. Experiments show that the forecasting interval index (FI-Index) significantly outperforms traditional indexes in a high updating environment.