Clustering a DAG for CAD Databases
IEEE Transactions on Software Engineering
Chorochronos: a research network for spatiotemporal database systems
ACM SIGMOD Record
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
CCAM: A Connectivity-Clustered Access Method for Networks and Network Computations
IEEE Transactions on Knowledge and Data Engineering
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
ACM SIGGRAPH 2007 courses
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Time-Aggregated Graphs for Modeling Spatio-temporal Networks
Journal on Data Semantics XI
Spatio-temporal network databases and routing algorithms: a summary of results
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Multilevel algorithms for partitioning power-law graphs
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
High performance multimodal networks
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Computer Science Review
Multiway partitioning via geometric embeddings, orderings, and dynamic programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A critical-time-point approach to all-start-time lagrangian shortest paths: a summary of results
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Spatial big-data challenges intersecting mobility and cloud computing
MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Efficient reachability query evaluation in large spatiotemporal contact datasets
Proceedings of the VLDB Endowment
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Given a set of operators and a spatio-temporal network, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient data storage method that minimizes disk I/O access costs. Storing and accessing spatio-temporal networks is increasingly important in many societal applications such as transportation management and emergency planning. This problem is challenging due to strains on traditional adjacency list representations when storing temporal attribute values from the sizable increase in length of the time-series. Current approaches for the SSTN problem focus on orthogonal partitioning (e.g., snapshot, longitudinal, etc.), which may produce excessive I/O costs when performing traversal-based spatio-temporal network queries (e.g., route evaluation, arrival time prediction, etc) due to the desired nodes not being allocated to a common page. We propose a Lagrangian-Connectivity Partitioning (LCP) technique to efficiently store and access spatio-temporal networks that utilizes the interaction between nodes and edges in a network. Experimental evaluation using the Minneapolis, MN road network showed that LCP outperforms traditional orthogonal approaches.