Efficient processing of containment queries on nested sets

Ibrahim, Ahmed S.; Fletcher, George H. L.

doi:10.1145/2452376.2452404

Cited by 4 publications

(4 citation statements)

References 42 publications

(61 reference statements)

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“…The study in [23] focused on containment queries on nested sets, and proposes an evaluation mechanism that relies on an inverted file which is populated with information for the placement of an element in the tree of nested sets. The above methods use classic inverted files or extend them either by trading update and creation costs for response time [1,14,37,38] or by adding information that is needed for more complex queries [23,42]. Employing these extended inverted files for set containment joins (i.e., in place of our I S ) is orthogonal to our work.…”

Section: Comparison Of the Join Methodsmentioning

confidence: 99%

“…The proposed solution relies on an inverted file where postings are populated with the item's probability of belonging to a certain object. The study in [23] focused on containment queries on nested sets, and proposes an evaluation mechanism that relies on an inverted file which is populated with information for the placement of an element in the tree of nested sets. The above methods use classic inverted files or extend them either by trading update and creation costs for response time [1,14,37,38] or by adding information that is needed for more complex queries [23,42].…”

Section: Set Containment Queriesmentioning

confidence: 99%

“…Sets are ubiquitous in computer science and most importantly in the field of data management; they model among others transactions and scientific data, click streams and Web search data, text. Contemporary data management systems allow the definition of set-valued (or multi-valued) data attributes and support operations such as containment queries [1,23,37,38,42]. Joins are also extended to include predicates on sets (containment, similarity, equality, etc.)…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Set containment join revisited

Bouros

Mamoulis

et al. 2015

Knowl Inf Syst

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Given two collections of set objects R and S, the R ⊆ S set containment join returns all object pairs (r, s) ∈ R × S such that r ⊆ s. Besides being a basic operator in all modern data management systems with a wide range of applications, the join can be used to evaluate complex SQL queries based on relational division and as a module of data mining algorithms. The state-of-the-art algorithm for set containment joins (PRETTI) builds an inverted index on the right-hand collection S and a prefix tree on the left-hand collection R that groups set objects with common prefixes and thus, avoids redundant processing. In this paper, we present a framework which improves PRETTI in two directions. First, we limit the prefix tree construction by proposing an adaptive methodology based on a cost model; this way, we can greatly reduce the space and time cost of the join. Second, we partition the objects of each collection based on their first contained item, assuming that the set objects are internally sorted. We show that we can process the partitions and evaluate the join while building the prefix tree and the inverted index progressively. This allows us to significantly reduce not only the join cost, but also the maximum memory requirements during the join.

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Section: Comparison Of the Join Methodsmentioning

confidence: 99%

Section: Set Containment Queriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Set containment join revisited

Bouros

Mamoulis

et al. 2015

Knowl Inf Syst

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“…Nested sets are popular in XML, JSON and other circumstances. One example for this is the set containment search based on nested sets [17]. Executing SSS or SSJ on nested set is rather expensive and more pioneer works are urgently called in this field.…”

Section: Challenges Of Data Complexitymentioning

confidence: 99%

A Survey on Set Similarity Search and Join

Jia¹

2018

IJPE

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Set similarity search and join operations have a wide range of applications, including e-commerce, information retrieval, bioinformatics, and so on. Although extensive techniques have been suggested for set similarity search and join, there is literature on systematically categorizing these techniques and comprehensively comparing them. To bridge this gap, this paper provides a comprehensive survey of set similarity search and join. The survey starts from the basic definitions, framework, ordering and similarity functions of set similarity search and join. Next, it discusses the main filtering techniques used in the state-of-the-art algorithms, and analyses the pros and cons of these algorithms in detail. For set similarity join algorithms, we divide them into 2 main categories based on the key underlying techniques they use: prefix filtering based algorithms and partition based algorithms. Prefix filtering is the most dominant technique, so algorithms based on prefix filtering and their recent variants are analyzed thoroughly. Partition is also a promising technique as it can exploit comparisons between multiple partitions. Furthermore, some efforts on MapReduce based set similarity join algorithms are also discussed briefly. In the end, the open challenges and questions are presented for future pursue.

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