Efficient continuous skyline computation

Morse, Michael D.; Patel, Jignesh M.; Grosky, William I.

doi:10.1016/j.ins.2007.02.033

Cited by 84 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence it is mapped to bitvector 011. Similarly it has highest value in the second dimension (11), and highest value in the third dimension as well (11). Hence the bitmap corresponding to P 2 f2; 1; 2g ¼ f011; 11; 11g.…”

Section: Bitmap Algorithm For Computing Skylinesmentioning

confidence: 98%

“…Skyline queries involving partially ordered domains such as interval and categorical data have been considered in [2,3]. Morse et al [11] introduced an algorithm to compute a continuous skyline. Morse et al [12] also proposed an algorithm optimized for low-cardinality domains but it is for single skyline computation and not directly applicable for skycube computation.…”

Section: Pricementioning

confidence: 99%

See 1 more Smart Citation

Efficient skycube computation using point and domain-based filtering

Kailasam¹,

Lee²,

Rhee³

et al. 2010

Information Sciences

View full text Add to dashboard Cite

Section: Bitmap Algorithm For Computing Skylinesmentioning

confidence: 98%

Section: Pricementioning

confidence: 99%

Efficient skycube computation using point and domain-based filtering

Kailasam¹,

Lee²,

Rhee³

et al. 2010

Information Sciences

View full text Add to dashboard Cite

“…Paper [22] firstly proposed the skyline query in a data stream, the data in the sliding window were managed in R-tree, and the skyline set was maintained by the interval tree. Paper [23] presented algorithm LOOK-OUT to compute skyline in a data stream. In paper [24], the data in the sliding window were managed based on a multi-layer grid structure.…”

Section: Related Workmentioning

confidence: 99%

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Dong

Cui

et al. 2018

Information

View full text Add to dashboard Cite

Abstract:Along with the application of the sensor network, there will be large amount of dynamic data coming from sensors. How to dig the useful information from such data is significant. Skyline query is aiming to identify the interesting points from a large dataset. The group-based skyline query is to find the outstanding Pareto Optimal groups which cannot be g-dominated by any other groups with the group same size. However, the existing algorithms of group-based skyline (G-Skyline) focus on the static data set, how to conduct advanced research on data stream remains an open problem at large. In this paper, we propose the group-based skyline query over the data stream. In order to compute G-Skyline efficiently, we present a sharing strategy, and based on which we propose two algorithms to efficiently compute the G-Skyline over the data stream: the point-arriving algorithm and the point-expiring algorithm. In our experiments, three synthetic data sets are used to test our algorithms; the experiments results show that our algorithms perform efficiently over a data stream.

show abstract

“…Chaudhuri et al [12] and Godfrey [18] developed techniques for estimating the skyline cardinality; Godfrey [26] and Sarkas et al [39] studied continuous maintenance of the skyline over a data stream; Morse et al [29] examine continuous time-interval skyline queries.…”

Section: Related Workmentioning

confidence: 99%

Finding superior skyline points for multidimensional recommendation applications

Yang

Fung

et al. 2011

World Wide Web

View full text Add to dashboard Cite

In a typical Web recommendation system, objects are often described by many attributes. It also needs to serve many users with a diversified range of preferences. In other words, it must be capable to efficiently support high dimensional preference queries that allow the user to explore the data space effectively without imposing specific preference weightings for each dimension. The skyline query, which can produce a set of objects guaranteed to contain all top ranked objects for any linear attribute preference combination, has been proposed to support this type of recommendation applications. However, it suffers from the problem known as 'dimensionality curse' as the size of skyline query result set can grow exponentially 34 World Wide Web (2012) 15:33-60 with the number of dimensions. Therefore, when the dimensionality is high, a large percentage of objects can become skyline points. This problem makes such a recommendation system less usable for users. In this paper, we propose a stronger type of skyline query, called core skyline query, that adopts a new quality measure called vertical dominance to return only an interesting subset of the traditional skyline points. An efficient query processing method is proposed to find core skyline points using a novel indexing structure called Linked Multiple B'-trees (LMB). Our approach can find such superior skyline points progressively without the need of computing the entire set of skyline points first.

show abstract

Efficient continuous skyline computation

Cited by 84 publications

References 30 publications

Efficient skycube computation using point and domain-based filtering

Efficient skycube computation using point and domain-based filtering

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Finding superior skyline points for multidimensional recommendation applications

Contact Info

Product

Resources

About