Multidimensional binary search trees used for associative searching
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Divide-and-conquer in multidimensional space
STOC '76 Proceedings of the eighth annual ACM symposium on Theory of computing
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Customizable parallel execution of scientific stream queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
StreamGlobe: processing and sharing data streams in grid-based P2P infrastructures
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A dynamically adaptive distributed system for processing complex continuous queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Processing High-Volume Stream Queries on a Supercomputer
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
ST--ACTS: a spatio-temporal activity simulator
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Using stream queries to measure communication performance of a parallel computing environment
ICDCSW '07 Proceedings of the 27th International Conference on Distributed Computing Systems Workshops
Spatio–temporal rule mining: issues and techniques
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A new approach for cluster detection for large datasets with high dimensionality
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
WEtransport: a context-based ride sharing platform
Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct
Frequent route based continuous moving object location- and density prediction on road networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Scalable splitting of massive data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
Noah: a dynamic ridesharing system
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Online, deviation-constrained capacitated vehicle routing
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Incremental Frequent Route Based Trajectory Prediction
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
Hi-index | 0.00 |
Transportation-related problems, like road congestion, parking, and pollution, are increasing in most cities. In order to reduce traffic, recent work has proposed methods for vehicle sharing, for example for sharing cabs by grouping "closeby" cab requests and thus minimizing transportation cost and utilizing cab space. However, the methods published so far do not scale to large data volumes, which is necessary to facilitate large-scale collective transportation systems, e.g., ride-sharing systems for large cities. This paper presents highly scalable trip grouping algorithms, which generalize previous techniques and support input rates that can be orders of magnitude larger. The following three contributions make the grouping algorithms scalable. First, the basic grouping algorithm is expressed as a continuous stream query in a data stream management system to allow for a very large flow of requests. Second, following the divide-and-conquer paradigm, four space-partitioning policies for dividing the input data stream into sub-streams are developed and implemented using continuous stream queries. Third, using the partitioning policies, parallel implementations of the grouping algorithm in a parallel computing environment are described. Extensive experimental results show that the parallel implementation using simple adaptive partitioning methods can achieve speed-ups of several orders of magnitude without significantly degrading the quality of the grouping.