The threshold join algorithm for top-k queries in distributed sensor networks

Zeinalipour-Yazti, Demetrios; Vagena, Zografoula; Gunopulos, Dimitrios; Kalogeraki, Vana; Tsotras, Vassilis J.; Vlachos, Michail; Koudas, Nick; Srivastava, Divesh

doi:10.1145/1080885.1080896

Cited by 66 publications

(52 citation statements)

References 12 publications

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“…Assume a data set [13], [16], [17] and arrival timestamp (used in slide window queries), respectively. Let a user-defined query function be…”

Section: A the Problemmentioning

confidence: 99%

“…Among all those aggregates, top-k query is preliminary for many sensor network applications [16], [17]. Some previous studies strive to propose energy-efficient processing approach for topk query such that the list of k highest (or lowest) sensor readings are retried.…”

Section: Related Workmentioning

confidence: 99%

“…Zeinalipour et. al [17] proposed to use non-uniform thresholds on the queried attribute in order to minimize the number of tuples transferred towards the querying node. Similarly, Wu et.…”

Section: Related Workmentioning

confidence: 99%

“…Each of them has his own preference; they not only put different weights on different dimensions of data but also on different time periods of data. This multi-dimensional nature has made many algorithms [2], [16], [17] developed for snapshot, one-dimensional top-k queries unsuitable or less efficient if a naive extension is made. With multi-dimensional sensor data, they have to pose as many queries as the number of user requests since each user could assign a set of weights representing his own preference, which results in huge communication overhead.…”

Section: Introductionmentioning

confidence: 99%

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Continuous multi-dimensional top-k query processing in sensor networks

Jiang

Cheng

Wang

et al. 2011

2011 Proceedings IEEE INFOCOM

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Abstract-Top-k query has long been an important topic in many fields of computer science. Efficient implementation of the top-k queries is the key for information searching. With the new frontier such as the cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, different users may request different set of information, with different priorities and at different times. Thus, the top-k search not only should be multi-dimensional, but also across time domain. From the system's perspective, the data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resourceconstrained and system efficiency is of paramount importance.In this paper, we develop a framework that can effectively satisfy the two ends. The sensor network maintains an efficient dominant graph data structure for data readings. A simple topk extraction algorithm is used for the user query processing and two schemes are proposed to further reduce communication cost. Our proposed methods can be used for top-k query with any linear convex query function. To the best of our knowledge, this is the first work for continuous multi-dimensional top-k query processing in sensor networks; and our simulation results show that our schemes can reduce the total communication cost by up to 90%, compared with the centralized scheme or a straightforward extension from previous top-k algorithm on one-dimensional sensor data.

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“…Assume a data set [13], [16], [17] and arrival timestamp (used in slide window queries), respectively. Let a user-defined query function be…”

Section: A the Problemmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Zeinalipour et. al [17] proposed to use non-uniform thresholds on the queried attribute in order to minimize the number of tuples transferred towards the querying node. Similarly, Wu et.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous multi-dimensional top-k query processing in sensor networks

Jiang

Cheng

Wang

et al. 2011

2011 Proceedings IEEE INFOCOM

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“…As expected from an algorithm which never errs, the performance of TPUT also depends crucially on the way the scores are partitioned among nodes. Other related work include variations of TA and TPUT optimized for certain network models [18,25,5].…”

Section: Previous Workmentioning

confidence: 99%

Approximate distributed top-k queries

Patt-Shamir

Shafrir

2008

Distrib. Comput.

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We consider a distributed system where each node keeps a local count for items (similar to elections where nodes are ballot boxes and items are candidates). A top-k query in such a system asks which are the k items whose global count, across all nodes in the system, is the largest. In this paper we present a MonteCarlo algorithm that outputs, with high probability, a set of k candidates which approximates the top-k items. The algorithm is motivated by sensor networks in that it focuses on reducing the individual communication complexity. In contrast to previous algorithms, the communication complexity depends only on the global scores and not on the partition of scores among nodes. If the number of nodes is large, our algorithm dramatically reduces the communication complexity when compared with deterministic algorithms. We show that the complexity of our algorithm is close to a lower bound on the cell-probe complexity of any non-interactive top-k approximation algorithm. We show that for some natural global distributions (such as the Geometric or Zipf distributions), our algorithm needs only polylogarithmic number of communication bits per node.

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Approximate Top-k Queries in Sensor Networks

Patt-Shamir

Shafrir

2006

Structural Information and Communication Complexity

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The threshold join algorithm for top-k queries in distributed sensor networks

Cited by 66 publications

References 12 publications

Continuous multi-dimensional top-k query processing in sensor networks

Continuous multi-dimensional top-k query processing in sensor networks

Approximate distributed top-k queries

Approximate Top-k Queries in Sensor Networks

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