Scalable processing of snapshot and continuous nearest-neighbor queries over one-dimensional uncertain data

Jinchuan Chen, Reynold Cheng, Mohamed Mokbel, Chi Yin Chow

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

In several emerging and important applications, such as location-based services, sensor monitoring and biological databases, the values of the data items are inherently imprecise. A useful query class for these data is the Probabilistic Nearest-Neighbor Query (PNN), which yields the IDs of objects for being the closest neighbor of a query point, together with the objects' probability values. Previous studies showed that this query takes a long time to evaluate. To address this problem, we propose the Constrained Nearest-Neighbor Query (C-PNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. We show that the C-PNN can be answered efficiently with verifiers. These are methods that derive the lower and upper bounds of answer probabilities, so that an object can be quickly decided on whether it should be included in the answer. We design five verifiers, which can be used on uncertain data with arbitrary probability density functions. We further develop a partial evaluation technique, so that a user can obtain some answers quickly, without waiting for the whole query evaluation process to be completed (which may incur a high response time). In addition, we examine the maintenance of a long-standing, or continuous C-PNN query. This query requires any update to be applied to the result immediately, in order to reflect the changes to the database values (e.g., due to the change of the location of a moving object). We design an incremental update method based on previous query answers, in order to reduce the amount of I/O and CPU cost in maintaining the correctness of the answers to such a query. Performance evaluation on realistic datasets show that our methods are capable of yielding timely and accurate results.

Original languageEnglish
Pages (from-to)1219-1240
Number of pages22
JournalVLDB Journal
Volume18
Issue number5
DOIs
Publication statusPublished - 1 Oct 2009
Externally publishedYes

Fingerprint

Processing
Location based services
Probability density function
Program processors
Monitoring
Sensors
Costs

Keywords

  • Continuous query
  • Incremental evaluation
  • Partial evaluation
  • Probabilistic nearest-neighbor query
  • Uncertain data

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture

Cite this

Scalable processing of snapshot and continuous nearest-neighbor queries over one-dimensional uncertain data. / Chen, Jinchuan; Cheng, Reynold; Mokbel, Mohamed; Chow, Chi Yin.

In: VLDB Journal, Vol. 18, No. 5, 01.10.2009, p. 1219-1240.

Research output: Contribution to journalArticle

Chen, Jinchuan ; Cheng, Reynold ; Mokbel, Mohamed ; Chow, Chi Yin. / Scalable processing of snapshot and continuous nearest-neighbor queries over one-dimensional uncertain data. In: VLDB Journal. 2009 ; Vol. 18, No. 5. pp. 1219-1240.
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