Continuous monitoring of top-k queries over sliding windows

Mouratidis, Kyriakos; Bakiras, Spiridon; Papadias, Dimitris

doi:10.1145/1142473.1142544

Cited by 199 publications

(192 citation statements)

References 32 publications

(26 reference statements)

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“…Our main difference towards these approaches is that they consider the ranking functions to be static while the values of the objects are changing while we consider the exact opposite. Other work related to top-k and time includes processing of top-k queries on temporal data where the aim is finding the top-k objects at a particular time [10], as well as monitoring top-k queries over sliding windows [12].…”

Section: Related Workmentioning

confidence: 99%

Discovering Influential Data Objects over Time

Gkorgkas

Vlachou

Doulkeridis

et al. 2013

Advances in Spatial and Temporal Databases

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Abstract. In applications such as market analysis, it is of great interest to product manufacturers to have their products ranked as highly as possible for a significant number of customers. However, customer preferences change over time, and product manufacturers are interested in monitoring the evolution of the popularity of their products, in order to discover those products that are consistently highly ranked. To take into account the temporal dimension, we define the continuous influential query and present algorithms for efficient processing and retrieval of continuous influential data objects. Furthermore, our algorithms support incremental retrieval of the next continuous influential data object in a natural way. To evaluate the performance of our algorithms, we conduct a detailed experimental study for various setups.

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Section: Related Workmentioning

confidence: 99%

Discovering Influential Data Objects over Time

Gkorgkas

Vlachou

Doulkeridis

et al. 2013

Advances in Spatial and Temporal Databases

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“…In [29] the authors improve the performance of ranked join indices based on the concept of dominating sets. In [26] a method for continuous top-k queries over streams is presented that monitors the top-k objects by using the K-skyband on a two dimensional transformed score-time space. Recently, reverse top-k queries [30,31] have been introduced, as a query that identifies the scoring functions (i.e., weighting vectors) that make a point a top-k result.…”

Section: Top-k Queries In Centralized Databasesmentioning

confidence: 99%

Distributed top-k query processing by exploiting skyline summaries

Vlachou

Doulkeridis

Nørvåg

2012

Distrib Parallel Databases

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Recently, a trend has been observed towards supporting rank-aware query operators, such as top-k, that enable users to retrieve only a limited set of the most interesting data objects. As data nowadays is commonly stored distributed over multiple servers, a challenging problem is to support rank-aware queries in distributed environments. In this paper, we propose a novel approach, called DiTo, for efficient top-k processing over multiple servers, where each server stores autonomously a fraction of the data. Towards this goal, we exploit the inherent relationship of top-k and skyline objects, and we employ the skyline objects of servers as a data summarization mechanism for efficiently identifying the servers that store top-k results. Relying on a thresholding scheme, DiTo retrieves the top-k result set progressively, while the number of queried servers and transferred data is minimized. Furthermore, we extend DiTo to support data summarizations of bounded size, thus restricting the cost of summary distribution and maintenance. To this end, we study the challenging problem of finding an abstraction of the skyline set of fixed size that influences the performance of DiTo only slightly. Our experimental evaluation shows that DiTo performs efficiently and provides a viable solution when a high degree of distribution is required.

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“…The problem has been extensively studied [6], [7], [8], [9]. Ilyas et al [10] give a comprehensive survey of top-k query processing techniques.…”

Section: ) K Closest Pairs Queriesmentioning

confidence: 99%

A unified approach for computing top-k pairs in multidimensional space

Cheema

Lin

Wang

et al. 2011

2011 IEEE 27th International Conference on Data Engineering

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Abstract-Top-k pairs queries have many real applications. k closest pairs queries, k furthest pairs queries and their bichromatic variants are some of the examples of the top-k pairs queries that rank the pairs on distance functions. While these queries have received significant research attention, there does not exist a unified approach that can efficiently answer all these queries. Moreover, there is no existing work that supports top-k pairs queries based on generic scoring functions. In this paper, we present a unified approach that supports a broad class of top-k pairs queries including the queries mentioned above. Our proposed approach allows the users to define a local scoring function for each attribute involved in the query and a global scoring function that computes the final score of each pair by combining its scores on different attributes. We propose efficient internal and external memory algorithms and our theoretical analysis shows that the expected performance of the algorithms is optimal when two or less attributes are involved. Our approach does not require any pre-built indexes, is easy to implement and has low memory requirement. We conduct extensive experiments to demonstrate the efficiency of our proposed approach.

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Continuous monitoring of top-k queries over sliding windows

Cited by 199 publications

References 32 publications

Discovering Influential Data Objects over Time

Discovering Influential Data Objects over Time

Distributed top-k query processing by exploiting skyline summaries

A unified approach for computing top-k pairs in multidimensional space

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