A frequency-domain analysis of head-motion prediction
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Compiler-based prefetching for recursive data structures
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
On caching and prefetching of virtual objects in distributed virtual environments
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Dependence based prefetching for linked data structures
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Prefetch policies for large objects in a web-enabled GIS application
Data & Knowledge Engineering
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Adaptation of a Neighbor Selection Markov Chain for Prefetching Tiled Web GIS Data
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
A Prefetching Technique for Object-Oriented Databases
BNCOD 15 Proceedings of the 15th British National Conferenc on Databases: Advances in Databases
A Motion Prediction Method for Mouse-Based Navigation
CGI '01 Computer Graphics International 2001
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
A Bayesian framework for automated dataset retrieval in Geographic Information Systems
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Improving Hash Join Performance through Prefetching
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficient query processing on unstructured tetrahedral meshes
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A History-Based Motion Prediction Method for Mouse-Based Navigation in 3D Digital City
GCC '08 Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing
Informed data distribution selection in a self-predicting storage system
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
k-Means Has Polynomial Smoothed Complexity
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Intelligent-based latency reduction in 3D walkthrough
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
Organization of data in non-convex spatial domains
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
On trip planning queries in spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Accelerating Range Queries for Brain Simulations
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Data-driven neuroscience: enabling breakthroughs via innovative data management
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simulations is a major task. Yet, they lack the tools to efficiently work with data of this size. One problem many scientists share is the analysis of the massive spatial models they build. For several types of analysis they need to interactively follow the structures in the spatial model, e.g., the arterial tree, neuron fibers, etc., and issue range queries along the way. Each query takes long to execute, and the total time for executing a sequence of queries significantly delays data analysis. Prefetching the spatial data reduces the response time considerably, but known approaches do not prefetch with high accuracy. We develop SCOUT, a structure-aware method for prefetching data along interactive spatial query sequences. SCOUT uses an approximate graph model of the structures involved in past queries and attempts to identify what particular structure the user follows. Our experiments with neuro-science data show that SCOUT prefetches with an accuracy from 71% to 92%, which translates to a speedup of 4x-15x. SCOUT also improves the prefetching accuracy on datasets from other scientific domains, such as medicine and biology.