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| Autores principales: | , , , |
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| Formato: | Recurso educativo Open Access |
| Lenguaje: | en |
| Publicado: |
2001
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| Materias: | |
| Acceso en línea: | https://eric.ed.gov/?id=ED459829 |
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| _version_ | 1867181750881353728 |
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| author | Yu, Clement Sharma, Prasoon Meng, Weiyi Qin, Yan |
| author_facet | Yu, Clement Sharma, Prasoon Meng, Weiyi Qin, Yan Yu, Clement Sharma, Prasoon Meng, Weiyi Qin, Yan |
| collection | Education Resources Information Center |
| contents | Database Selection for Processing k Nearest Neighbors Queries in Distributed Environments. Yu, Clement Sharma, Prasoon Meng, Weiyi Qin, Yan Data Processing Databases Electronic Libraries Information Processing Information Retrieval Information Seeking Online Searching Online Systems This paper considers the processing of digital library queries, consisting of a text component and a structured component in distributed environments. The paper concentrates on the processing of the structured component of a distributed query. A method is proposed to identify the databases that are likely to be useful for processing any given query and to determine the tuples from each useful site which are necessary for answering the query. In this way, both the communication cost and the local processing costs are saved. One common characteristic of these "k" nearest neighbors queries is that it is not necessary to obtain all the "k" nearest neighbors; it is often sufficient to get most of the "k" neighbors. Experimental results are provided to demonstrate that most of the "k" nearest neighbors (85% to 100%) are obtained using this approach. An average accuracy rate of 94.7% is achieved when the 20 closest neighbors are desired. (Contains 15 references.) (AEF) |
| format | Recurso educativo Open Access |
| id | eric_ED459829 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2001 |
| record_format | eric |
| spellingShingle | Database Selection for Processing k Nearest Neighbors Queries in Distributed Environments. Yu, Clement Sharma, Prasoon Meng, Weiyi Qin, Yan Data Processing Databases Electronic Libraries Information Processing Information Retrieval Information Seeking Online Searching Online Systems Database Selection for Processing k Nearest Neighbors Queries in Distributed Environments. Yu, Clement Sharma, Prasoon Meng, Weiyi Qin, Yan Data Processing Databases Electronic Libraries Information Processing Information Retrieval Information Seeking Online Searching Online Systems This paper considers the processing of digital library queries, consisting of a text component and a structured component in distributed environments. The paper concentrates on the processing of the structured component of a distributed query. A method is proposed to identify the databases that are likely to be useful for processing any given query and to determine the tuples from each useful site which are necessary for answering the query. In this way, both the communication cost and the local processing costs are saved. One common characteristic of these "k" nearest neighbors queries is that it is not necessary to obtain all the "k" nearest neighbors; it is often sufficient to get most of the "k" neighbors. Experimental results are provided to demonstrate that most of the "k" nearest neighbors (85% to 100%) are obtained using this approach. An average accuracy rate of 94.7% is achieved when the 20 closest neighbors are desired. (Contains 15 references.) (AEF) |
| title | Database Selection for Processing k Nearest Neighbors Queries in Distributed Environments. |
| topic | Data Processing Databases Electronic Libraries Information Processing Information Retrieval Information Seeking Online Searching Online Systems |
| url | https://eric.ed.gov/?id=ED459829 |