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| 講演名 | 2008-09-22 13:30 ［招待講演］Processing Windowed Top-k Queries on Uncertain Streams ○Cheqing Jin（East China Univ. of Science & Tech.）  DE2008-50 |
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| 抄録 | （和） | It has become an important issue to process uncertain (probabilistic) data in many applications, such as sensor networks and objects tracking. For a given uncertain dataset, there are many possible instances called worlds, and the possible worlds semantics has been widely used. Top-k queries have been recently studied in the setting of uncertain data. Given a ranking function, the goal is to find the top-k ranked tuples in a given uncertain dataset. Soliman et al. defined two types of top-k queries over a uncertain dataset, called U-Topk and U-kRanks. Hua et al. defined a probabilistic threshold top-k query, denoted PT-k. The UTop k query returns the top-k tuples in all possible worlds with maximum probability. The U-kRanks query returns the winner for the i-th rank for all 1 <= i <= k. The PT-k query returns tuples with probability of being top-k in all possible worlds greater than a user-given threshold p. All the existing approaches for processing top-k queries are designed for static uncertain datasets, and are incapable of handling streaming data. One major challenge is that the number of possible worlds that change as the window slides for one timestamp is huge. The goal of this paper is to design a unified framework for processing sliding-window top-k queries, which can be coupled with any of the definitions above, not to argue which definition is better than the others, or to propose yet another definition. We study a slight variation of PT-k, namely, the probabilistic k top-k, or simply Pk-topk, which returns the k most probable tuples of being the top-k among all. |
| （英） | It has become an important issue to process uncertain (probabilistic) data in many applications, such as sensor networks and objects tracking. For a given uncertain dataset, there are many possible instances called worlds, and the possible worlds semantics has been widely used. Top-k queries have been recently studied in the setting of uncertain data. Given a ranking function, the goal is to find the top-k ranked tuples in a given uncertain dataset. Soliman et al. defined two types of top-k queries over a uncertain dataset, called U-Topk and U-kRanks. Hua et al. defined a probabilistic threshold top-k query, denoted PT-k. The UTop k query returns the top-k tuples in all possible worlds with maximum probability. The U-kRanks query returns the winner for the i-th rank for all 1 <= i <= k. The PT-k query returns tuples with probability of being top-k in all possible worlds greater than a user-given threshold p. All the existing approaches for processing top-k queries are designed for static uncertain datasets, and are incapable of handling streaming data. One major challenge is that the number of possible worlds that change as the window slides for one timestamp is huge. The goal of this paper is to design a unified framework for processing sliding-window top-k queries, which can be coupled with any of the definitions above, not to argue which definition is better than the others, or to propose yet another definition. We study a slight variation of PT-k, namely, the probabilistic k top-k, or simply Pk-topk, which returns the k most probable tuples of being the top-k among all. |
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| キーワード | （和） | top-k query / uncertain streams / sliding-window model / / / / / |
| （英） | top-k query / uncertain streams / sliding-window model / / / / / | |
| 文献情報 | 信学技報, vol. 108, no. 211, DE2008-50, pp. 41-42, 2008年9月. | |
| 資料番号 | DE2008-50 | |
| 発行日 | 2008-09-14 (DE) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | DE2008-50 |
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| 研究会情報 | |
| 研究会 | DE |
| 開催期間 | 2008-09-21 - 2008-09-22 |
| 開催地（和） | 飯坂ホテル聚楽 |
| 開催地（英） | |
| テーマ（和） | 招待講演・ポスター英語ディスカッション |
| テーマ（英） | |
| 講演論文情報の詳細 | |
| 申込み研究会 | DE |
| 会議コード | 2008-09-DE |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Processing Windowed Top-k Queries on Uncertain Streams |
| サブタイトル（英） | |
| キーワード(1)（和/英） | top-k query / top-k query |
| キーワード(2)（和/英） | uncertain streams / uncertain streams |
| キーワード(3)（和/英） | sliding-window model / sliding-window model |
| キーワード(4)（和/英） | / |
| キーワード(5)（和/英） | / |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | Cheqing Jin / Cheqing Jin / |
| 第1著者 所属（和/英） | East China University of Science and Technology (略称： East China Univ. of Science & Tech.) East China University of Science and Technology (略称： East China Univ. of Science & Tech.) |
| 講演者 | 第1著者 |
| 発表日時 | 2008-09-22 13:30:00 |
| 発表時間 | 30分 |
| 申込先研究会 | DE |
| 資料番号 | DE2008-50 |
| 巻番号（vol） | vol.108 |
| 号番号（no） | no.211 |
| ページ範囲 | pp.41-42 |
| ページ数 | 2 |
| 発行日 | 2008-09-14 (DE) |
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