Spatio-textual similarity joins

Bouros, Panagiotis; Ge, Shen; Mamoulis, Nikos

doi:10.14778/2428536.2428537

Cited by 101 publications

(117 citation statements)

References 34 publications

(68 reference statements)

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“…Some other heuristics are introduced in [34] and [21]. However, as compared in [32], these heuristics cannot significantly improve the efficiency of the prefix-filter based algorithms.…”

Section: Algorithm 1: Prefix-filter Based Frameworkmentioning

confidence: 99%

“…Next, we apply the first step, complementary candidates probe, by probing the inverted lists I(e) for e ∈ r i \ r i as discussed above (line 10-15). Then, we apply the second step, current candidates update, by probing the inverted lists I(e) for e ∈ r i \ r i (line 16-18) and e ∈ r i \ r i (line [19][20][21], and updating A(r i ) based on A(r i ) (line 22-24) as discussed above. Finally, we output A(r i ) as A(r i ).…”

Section: Answer-level Skippingmentioning

confidence: 99%

“…Exact set similarity join has been extensively studied in the literature [18,19,20,21,28,31,32,33,34,35,40,44,45]. As we have introduced in Section 3, existing solutions all follow the filtering-verification framework and can be divided into two categories based on the filtering mechanism, namely, prefix-filter based algorithms and partition-filter based algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al [42] devised adaptive length prefix to strengthen filtering power. Other prefix-filter based algorithms include [34] to remove the useless entries in the inverted lists of elements, and [21] to group the records with the same prefix. However, they cannot significantly improve the algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Leveraging set relations in exact set similarity join

et al. 2017

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Exact set similarity join, which finds all the similar set pairs from two collections of sets, is a fundamental problem with a wide range of applications. The existing solutions for set similarity join follow a filtering-verification framework, which generates a list of candidate pairs through scanning indexes in the filtering phase, and reports those similar pairs in the verification phase. Though much research has been conducted on this problem, set relations, which we find out is quite effective on improving the algorithm efficiency through computational cost sharing, have never been studied. Therefore, in this paper, instead of considering each set individually, we explore the set relations in different levels to reduce the overall computational costs. First, it has been shown that most of the computational time is spent on the filtering phase, which can be quadratic to the number of sets in the worst case for the existing solutions. Thus we explore index-level set relations to reduce the filtering cost to be linear to the size of the input while keeping the same filtering power. We achieve this by grouping related sets into blocks in the index and skipping useless index probes in joins. Second, we explore answer-level set relations to further improve the algorithm based on the intuition that if two sets are similar, their answers may have a large overlap. We derive an algorithm which incrementally generates the answer of one set from an already computed answer of another similar set rather than compute the answer from scratch to reduce the computational cost. Finally, we conduct extensive performance studies using 21 real datasets with various data properties from a wide range of domains. The experimental results demonstrate that our algorithm outperforms all the existing algorithms across all datasets and can achieve more than an order of magnitude speedup against the stateof-the-art algorithms.

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“…Some other heuristics are introduced in [34] and [21]. However, as compared in [32], these heuristics cannot significantly improve the efficiency of the prefix-filter based algorithms.…”

Section: Algorithm 1: Prefix-filter Based Frameworkmentioning

confidence: 99%

Section: Answer-level Skippingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Leveraging set relations in exact set similarity join

et al. 2017

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“…As noted in [BGM12], finding pairs that satisfy a threshold may be enough for these tasks. However, in this work we are interested in extracting knowledge from the best match.…”

Section: Selecting the Best Matchmentioning

confidence: 99%

Mixed Spatial and Nonspatial Problems in Location Based Services

Ballesteros¹

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All rights reserved.iii DEDICATION Esta Disertacion va dedicada muy especialmente a mi familia. Primero mis padres, Elssy y Jaime, que me dieron todas las herramientas necesarias para poder cumplir mis metas. El amor, el sacrificio y dedicacion con que nos formaron a mis hermanos y a mi seran siempre motivo de inspiracion para continuar adelante. A mis hermanos Lili, Meli, Randy e Isabella, por apoyarme y darme muchas alegrias y motivos de orgullo.Por supuesto, no podria faltar la dedicacion a mi hermosa esposa, Pily. Desde que comenzamos juntos este viaje, siempre ha estado conmigo compartiendo todos los momentos, lo mas felices y los mas dificiles y nunca ha desistido. Para ella, que es mi fuerza inspiradora y mi amor, va dedicada esta disertacion. Finalmente, quisiera dedicar este logro a mi Maestro. El sin duda me ha dado todo lo que tengo y esto es simplemente parte de su obra. Gracias por todo. Special thanks goes to Dr. Bogdan Carbunar, who has been a great collaborator and teacher, and to Mr. Mahmudur Rahman. We all created such an exceptional team and we were able to contribute in several aspects in our research. Their valuable insights and feedback were the key to publish our work. problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases.Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. vi iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features.Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses.Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe t...

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Authentication of spatio‐textual similarity join queries in untrusted cloud environments

Han

Cheng

Wang

et al. 2016

Security Comm Networks

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In this paper, we study the problem of authentication of spatio‐textual similarity join queries in untrusted cloud environments. To solve this problem, we first propose a pairwise authentication scheme, where object pairs are considered as basic processing units. In this scheme, an object hash tree is proposed for the data owner (DO) to build the authenticated data structure (ADS) over the data collection. Moreover, during join processing, a pair table is introduced for the service provider (SP) to construct the verification object (VO) for client's authentication. However, when clients authenticate the join results, they have large number of pairs to examine, which leads to excessive computation cost. Therefore, we then propose an optimized MR*‐tree‐based scheme. In this scheme, the DO spatially organizes the objects in an ADS called MR*‐tree, which combines the concepts of the Merkle hash tree and the R*‐tree. Based on the MR*‐tree, the SP employs the depth‐first search strategy to find the join results and constructs the VO according to the visited nodes of the MR*‐tree. Our extensive experiments on the real‐world dataset verify the effectiveness and efficiency of our proposed schemes on several performance metrics, including the data preprocessing time, running time, VO size, and authentication time. Copyright © 2016 John Wiley & Sons, Ltd.

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Spatio-textual similarity joins

Cited by 101 publications

References 34 publications

Leveraging set relations in exact set similarity join

Leveraging set relations in exact set similarity join

Mixed Spatial and Nonspatial Problems in Location Based Services

Authentication of spatio‐textual similarity join queries in untrusted cloud environments

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