| 1. |
- Gidofalvi, Gyözö, 1975-, et al.
(författare)
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Highly scalable trip grouping for large-scale collective transportation systems
- 2008
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Ingår i: Advances in Database Technology - EDBT 2008 - 11th International Conference on Extending Database Technology, Proceedings. - New York, NY, USA : ACM Press. - 9781595939265 ; , s. 678-689
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Gidófalvi, Gyözö, et al.
(författare)
-
Highly Scalable Trip Grouping for Large Scale Collective Transportation Systems
- 2008
-
Ingår i: Proc. 11th International Conference on Extending Database Technology, EDBT 2008.
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Konferensbidrag (refereegranskat)abstract
- Transportation–related problems, like road congestion, park-ing, and pollution are increasing in most cities. In order toreduce traffic, recent work has proposed methods for vehiclesharing, for example for sharing cabs by grouping “closeby”cab requests and thus minimizing transportation cost andutilizing cab space. However, the methods proposed so fardo not scale to large data volumes, which is necessary tofacilitate large scale collective transportation systems, e.g.,ride–sharing systems for large cities.This paper presents highly scalable “trip grouping” algo-rithms, that generalize previous techniques and support in-put rates that can be orders of magnitude larger. The follow-ing three contributions make the grouping algorithms scal-able. First, the basic grouping algorithm is expressed as acontinuous stream query in a data stream management sys-tem to allow for very large flows of requests. Second, follow-ing the divide–and–conquer paradigm, four space–partition-ing policies for dividing the input data stream into sub–streams are developed and implemented using continuousstream queries. Third, using the partitioning policies, par-allel implementations of the grouping algorithm in a paral-lel computing environment are described. Extensive experi-mental results show that the parallel implementation usingsimple adaptive partitioning methods can achieve speed–upsof several orders of magnitudes without significantly effect-ing the quality of the grouping.
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| 3. |
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| 4. |
- Zeitler, Erik, et al.
(författare)
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Processing High-Volume Stream Queries on a Supercomputer
- 2006
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Ingår i: Processing High-Volume Stream Queries on a Supercomputer. - 0769525717 ; , s. 147-
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Konferensbidrag (refereegranskat)abstract
- Scientific instruments, such as radio telescopes, colliders, sensor networks, and simulators generate very high volumes of data streams that scientists analyze to detect and understand physical phenomena. The high data volume and the need for advanced computations on the streams require substantial hardware resources and scalable stream processing. We address these challenges by developing data stream management technology to support high-volume stream queries utilizing massively parallel computer hardware. We have developed a data stream management system prototype for state-of-the-art parallel hardware. The performance evaluation uses real measurement data from LOFAR, a radio telescope antenna array being developed in the Netherlands.
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| 5. |
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| 6. |
- Zeitler, Erik
(författare)
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Working with Stella
- 2006
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Ingår i: Astronnews. - 1871-6644. ; :1, s. 12-13
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Tidskriftsartikel (populärvet., debatt m.m.)
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