The SQL Standard

Michels, Jan-Eike; Kulkarni, Krishna; Farrar, C. M.; Eisenberg, Andrew; Mattos, Nelson Mendonça; Darwen, Hugh

doi:10.1016/b978-0-12-800631-3.50019-8

Cited by 69 publications

(85 citation statements)

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“…An important feature of real-life queries is aggregation which, in fact, is present in most of the TPC-H queries. However, here our understanding of correctness of answers is quite poor; SQL's rules for aggregation and nulls are rather ad-hoc and have been persistently criticized [4,9]. Therefore, much theoretical work is needed in this direction before practical algorithms emerge.…”

Section: Outlook and Open Problemsmentioning

confidence: 99%

“…The way incomplete information is handled in commercial DBMSs, specifically by SQL, has been heavily criticized for producing counter-intuitive and just plain incorrect answers [4,9]. This is often blamed on SQL's 3-valued logic (3VL), and there are multiple discussions in the literature on the relative merits of SQL's 3VL and some alternatives; see, e.g., [11,33,6].…”

Section: Introductionmentioning

confidence: 99%

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Correctness of SQL Queries on Databases with Nulls

Guagliardo

Libkin

2017

SIGMOD Rec.

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Multiple issues with SQL's handling of nulls have been well documented. Having efficiency as its main goal, SQL disregards the standard notion of correctness on incomplete databases -certain answers -due to its high complexity. As a result, the evaluation of SQL queries on databases with nulls may produce answers that are just plain wrong. However, SQL evaluation can be modified, at least for relational algebra queries, to approximate certain answers, i.e., return only correct answers. We examine recently proposed approximation schemes for certain answers and analyze their complexity, both theoretical bounds and real-life behavior.

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Section: Outlook and Open Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correctness of SQL Queries on Databases with Nulls

Guagliardo

Libkin

2017

SIGMOD Rec.

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“…1 the "automaton" table will have 20 entries; starting states are coded as (0,1) and (0,4), final states -as (3,0) and (5,0). Here are the contents of table "automaton" {(0,1), (1,5), (1,3), (1,2), (1,6), (2,2), (2,3), (2,5), (2,6), (0,4), (4,2), (4,6), (4,5), (4,3), (6,2), (6,6), (6,3), (6,5), (3,0), (5,0)}.…”

Section: Coding the Machine In The Parent-child Modelmentioning

confidence: 99%

“…There are some special cases when it comes to organizing relations, such as managing temporal data [2], hierarchical data [3], and the combination of the two [4]. The mentioned cases bring structural limitations to the model.…”

Section: Problem Formulationmentioning

confidence: 99%

Employing finite-state machines in data integrity problems

2016

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Abstract. This paper explores the issue of group integrity of tuple subsets regarding corporate integrity constraints in relational databases. A solution may be found by applying the finite-state machine theory to guarantee group integrity of data. We present a practical guide to coding such an automaton. After creating SQL queries to manipulate data and control its integrity for real data domains, we study the issue of query performance, determine the level of transaction isolation, and generate query plans.

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“…The Java programming language [8] is another popular system that uses mobile code, though its mainly used to augment client functionality. SQL [5] can be viewed as a remote evaluation system, since it allows clients to skip intermediate results by submitting complex, nested queries to the database server.…”

Section: Related Workmentioning

confidence: 99%