| 31. |
- Gidofalvi, Gyözö, 1975-, et al.
(författare)
-
Highly scalable trip grouping for large-scale collective transportation systems
- 2008
-
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
-
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.
|
|
| 32. |
- 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.
-
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.
|
|
| 33. |
|
|
| 34. |
|
|
| 35. |
- Ivanova, Milena, et al.
(författare)
-
Customizable parallel execution of scientific stream queries
- 2005
-
Ingår i: 31st International Conference on Very Large Data Bases.
-
Konferensbidrag (refereegranskat)abstract
- Scientific applications require processing high-volume on-line streams of numerical data from instruments and simulations. We present an extensible stream database system that allows scalable and flexible continuous queries on such streams. Application dependent streams and query functions are defined through an object-relational model. Distributed execution plans for continuous queries are described as high-level data flow distribution templates. Using a generic template we define two partitioning strategies for scalable parallel execution of expensive stream queries: window split and window distribute. Window split provides operators for parallel execution of query functions by reducing the size of stream data units using application dependent functions as parameters. By contrast, window distribute provides operators for customized distribution of entire data units without reducing their size. We evaluate these strategies for a typical high volume scientific stream application and show that window split is favorable when expensive queries are executed on limited resources, while window distribution is better otherwise.
|
|
| 36. |
- Ivanova, Milena, et al.
(författare)
-
Customizable Parallel Execution of Scientific Stream Queries
- 2005
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Scientific applications require processing high-volume on-line streams of numerical data from instruments and simulations. We present an extensible stream database system that allows scalable and flexible continuous queries on such streams. Application dependent streams and query functions are defined through an Object-Relational model. Distributed execution plans for continuous queries are described as high-level data flow distribution templates. Using a generic template we define two partitioning strategies for scalable parallel execution of expensive stream queries: window split and window distribute. Window split provides operators for customized parallel execution of query functions whose complexity depends on size of the data units on which they are applied. It reduces the size of stream data units using application dependent functions as parameters. By contrast, window distribute provides operators for customized distribution of entire data units without reducing their size. We evaluated these strategies for a typical high volume scientific stream application and show that window split is favorable when computational resources are limited, while window distribute is better when there are sufficient resources.
|
|
| 37. |
- Ivanova, Milena, 1967-
(författare)
-
Scalable Scientific Stream Query Processing
- 2005
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Scientific applications require processing of high-volume on-line streams of numerical data from instruments and simulations. In order to extract information and detect interesting patterns in these streams scientists need to perform on-line analyses including advanced and often expensive numerical computations. We present an extensible data stream management system, GSDM (Grid Stream Data Manager) that supports scalable and flexible continuous queries (CQs) on such streams. Application dependent streams and query functions are defined through an object-relational model. Distributed execution plans for continuous queries are specified as high-level data flow distribution templates. A built-in template library provides several common distribution patterns from which complex distribution patterns are constructed. Using a generic template we define two customizable partitioning strategies for scalable parallel execution of expensive stream queries: window split and window distribute. Window split provides parallel execution of expensive query functions by reducing the size of stream data units using application dependent functions as parameters. By contrast, window distribute provides customized distribution of entire data units without reducing their size. We evaluate these strategies for a typical high volume scientific stream application and show that window split is favorable when expensive queries are executed on limited resources, while window distribution is better otherwise. Profile-based optimization automatically generates optimized plans for a class of expensive query functions. We further investigate requirements for GSDM in Grid environments. GSDM is a fully functional system for parallel processing of continuous stream queries. GSDM includes components such as a continuous query engine based on a data-driven data flow paradigm, a compiler of CQ specifications into distributed execution plans, stream interfaces and communication primitives. Our experiments with real scientific streams on a shared-nothing architecture show the importance of both efficient processing and communication for efficient and scalable distributed stream processing.
|
|
| 38. |
|
|
| 39. |
- Johanson, Mathias, et al.
(författare)
-
Relaying Controller Area Network Frames over Wireless Internetworks for Automotive Testing Applications
- 2009
-
Ingår i: Proc. 4th International Conference on Systems and Networks Communications. - Piscataway, NJ : IEEE. - 9781424447725 ; , s. 1-5
-
Konferensbidrag (refereegranskat)abstract
- In this paper we describe how Controller Area Network (CAN) frames can be relayed over a wireless Internet connection, enabling remote access to the CAN buses of vehicles for applications in automotive testing. This opens up many new possibilities for automotive diagnostics, monitoring, testing, analysis and verification, which we believe can significantly reduce the time required for the testing and verification phases of automotive development. A CAN-over-IP tunneling protocol is described and the design and implementation of a generic system for remote access to the CAN bus of vehicles is presented. Examples of applications of the technology are given and implications in terms of new possibilities and challenges in automotive engineering are discussed.
|
|
| 40. |
|
|
