Frequent Itemset Discovery with SQL Using Universal Quantification

Rantzau, Ralf

doi:10.1007/978-3-540-44497-8_10

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…However, it was surprising that K-Way-Join performed best for this (admittedly small) dataset, unlike reports in related work mentioned in Section 2.1. We provide more information on this comparison in [19].…”

Section: Commercial Dbmsmentioning

confidence: 98%

“…The SQL statement used for this phase is quite lengthy, therefore we do not present it in this paper. It is shown in [19] together with an equivalent query in tuple relational calculus to emphasize the use of universal quantification (the universal quantifier "∀"). The frequent itemset counting phase of Quiver uses universal quantification as well.…”

Section: The Quiver Algorithmmentioning

confidence: 99%

“…We have recently introduced a new SQL-based algorithm for deriving frequent itemsets called Quiver (QUantified Itemset discoVERy) [19]. The basic idea of the algorithm is to use a vertical layout for representing itemsets in a relation in the same way as transactions are represented, i.e., the relations storing candidate and frequent itemsets have the same schema (itemset, pos, item), similar to the schema for transactions T(transaction, item).…”

Section: The Quiver Algorithmmentioning

confidence: 99%

“…taken from[19]) illustrates the difference between the horizontal and the vertical layout for transactions and itemsets. Quiver's SQL statements employ universal quantification in both the candidate generation and the support counting phase.…”

mentioning

confidence: 99%

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Processing frequent itemset discovery queries by division and set containment join operators

Rantzau

2003

Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

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SQL-based data mining algorithms are rarely used in practice today. Most performance experiments have shown that SQL-based approaches are inferior to main-memory algorithms. Nevertheless, database vendors try to integrate analysis functionalities to some extent into their query execution and optimization components in order to narrow the gap between data and processing. Such a database support is particularly important when data mining applications need to analyze very large datasets or when they need access current data, not a possibly outdated copy of it. We investigate approaches based on SQL for the problem of finding frequent itemsets in a transaction table, including an algorithm that we recently proposed, called Quiver, which employs universal and existential quantifications. This approach employs a table schema for itemsets that is similar to the commonly used vertical layout for transactions: each item of an itemset is stored in a separate row. We argue that expressing the frequent itemset discovery problem using quantifications offers interesting opportunities to process such queries using set containment join or set containment division operators, which are not yet available in commercial database systems. Initial performance experiments reveal that Quiver cannot be processed efficiently by commercial DBMS. However, our experiments with query execution plans that use operators realizing set containment tests suggest that an efficient processing of Quiver is possible.

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Section: Commercial Dbmsmentioning

confidence: 98%

Section: The Quiver Algorithmmentioning

confidence: 99%

Section: The Quiver Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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Processing frequent itemset discovery queries by division and set containment join operators

Rantzau

2003

Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

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“…Results of Section 4 confirm that these multiple-joins queries lack of efficiency for subset querying. Notice that we do not address here the use of SQL for mining the frequent itemsets [11,10] that also needs for subset query evaluation. Figure 1 shows an example of a SQL query retrieving the supersets of the itemset {5, 8}.…”

Section: Introductionmentioning

confidence: 99%

Optimizing subset queries

Masson

Robardet

Boulicaut

2004

Proceedings of the 2004 ACM Symposium on Applied Computing

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Storing sets and querying them (e.g., subset queries that provide all supersets of a given set) is known to be difficult within relational databases. We consider that being able to query efficiently both transactional data and materialized collections of sets by means of standard query language is an important step towards practical inductive databases. Indeed, data mining query languages like MINE RULE extract collections of association rules whose components are sets into relational tables. Post-processing phases often use extensively subset queries and cannot be efficiently processed by SQL servers. In this paper, we propose a new way to handle sets from relational databases. It is based on a data structure that partially encodes the inclusion relationship between sets. It is an extension of the hash group bitmap key proposed by Morzy et al. [8]. Our experiments show an interesting improvement for these useful subset queries.

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Shaping SQL-Based Frequent Pattern Mining Algorithms

Sidló

Lukács

2006

Lecture Notes in Computer Science

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Frequent Itemset Discovery with SQL Using Universal Quantification

Cited by 8 publications

References 14 publications

Processing frequent itemset discovery queries by division and set containment join operators

Processing frequent itemset discovery queries by division and set containment join operators

Optimizing subset queries

Shaping SQL-Based Frequent Pattern Mining Algorithms

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