Explaining missing answers to SPJUA queries

Herschel, Melanie; Hernández, Mauricio A.

doi:10.14778/1920841.1920869

Cited by 81 publications

(91 citation statements)

References 16 publications

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“…), relate them to other approximate query answering notions, both in databases [16,23,15,34] and in AI [27,32], and to existing approaches that explain why tuples do not appear in query answers [36,21]. As for quality of approximations of certain answers, these are best confirmed experimentally, as was demonstrated recently [19].…”

Section: Resultsmentioning

confidence: 73%

Negative Knowledge for Certain Query Answers

Libkin

2016

Web Reasoning and Rule Systems

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Abstract. Querying incomplete data usually amounts to finding answers we are certain about. Standard approaches concentrate on positive information about query answers, and miss negative knowledge, which can be useful for two reasons. First, sometimes it is the only type of knowledge one can infer with certainty, and second, it may help one find good and efficient approximations of positive certain answers. Our goal is to consider a framework for defining both positive and negative certain knowledge about query answers and to show two applications of it. First, we demonstrate that it naturally leads to a way of representing certain information that has hitherto not been used in querying incomplete databases. Second, we show that approximations of such certain information can be computed efficiently for all first-order queries over relational databases.

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Section: Resultsmentioning

confidence: 73%

Negative Knowledge for Certain Query Answers

Libkin

2016

Web Reasoning and Rule Systems

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“…queries can be classified into two groups according to the kinds of explanations they provide: provenancebased and query rewriting. In the provenance-based methods, query-based [15,16], instance-based [17][18][19], and hybrid explanations [20] are generated. Query-based explanations [15] study which operators of a query tree are responsible for the rejection of interesting tuples from the result set.…”

Section: Problem Of Unexpected Answers In Relational Database Systemsmentioning

confidence: 99%

Answering “Why Empty?” and “Why So Many?” queries in graph databases

Vasilyeva¹,

Thiele

Bornhövd³

et al. 2016

Journal of Computer and System Sciences

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Graph databases provide schema-flexible storage and support complex, expressive queries. However, the flexibility and expressiveness in these queries come at additional costs: queries can result in unexpected empty answers or too many answers, which are difficult to resolve manually. To address this, we introduce subgraph-based solutions for graph queries "Why Empty?" and "Why So Many?" that give an answer about which part of a graph query is responsible for an unexpected result. We also extend our solutions to consider the specifics of the used graph model and to increase efficiency and experimentally evaluate them in an in-memory column database.

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“…Given a violation for a certain by DCs. However, more complex repair models for missing tuples, such as [11], can be supported with extensions. rule, this function outputs an update to the database to satisfy the violations identified by the corresponding detect.…”

Section: Target Errors Detectionmentioning

confidence: 99%

“…Several proposals tackled the problem of verifying the semantic correctness of data transformations by pointing at anomalies in the results. These have been mainly termed as "Why questions" [2] and "Why-Not questions" [11,19]. In the first case, the system finds the origin of some tuples or cells in the results.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Descriptive and prescriptive data cleaning

Chalamalla

Ilyas

Ouzzani

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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Data cleaning techniques usually rely on some quality rules to identify violating tuples, and then fix these violations using some repair algorithms. Oftentimes, the rules, which are related to the business logic, can only be defined on some target report generated by transformations over multiple data sources. This creates a situation where the violations detected in the report are decoupled in space and time from the actual source of errors. In addition, applying the repair on the report would need to be repeated whenever the data sources change. Finally, even if repairing the report is possible and affordable, this would be of little help towards identifying and analyzing the actual sources of errors for future prevention of violations at the target. In this paper, we propose a system to address this decoupling. The system takes quality rules defined over the output of a transformation and computes explanations of the errors seen on the output. This is performed both at the target level to describe these errors and at the source level to prescribe actions to solve them. We present scalable techniques to detect, propagate, and explain errors. We also study the effectiveness and efficiency of our techniques using the TPC-H Benchmark for different scenarios and classes of quality rules.

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Explaining missing answers to SPJUA queries

Cited by 81 publications

References 16 publications

Negative Knowledge for Certain Query Answers

Negative Knowledge for Certain Query Answers

Answering “Why Empty?” and “Why So Many?” queries in graph databases

Descriptive and prescriptive data cleaning

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