Coping with Incomplete Data: Recent Advances

Console, Marco; Guagliardo, Paolo; Libkin, Leonid; Toussaint, Etienne

doi:10.1145/3375395.3387970

Cited by 13 publications

(17 citation statements)

References 66 publications

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“…One way of resolving this is to assume that conditions involving NULL evaluate to false rather than unknown. The idea in itself is not new, it was present in old query languages such as Quel, and was recently studied in connection with various proposals on using 2-valued logic in place of SQL's 3VL, see [23,24]. In such a logic, the above MANDATORY condition would not hold for a person whose passport expiry is NULL, thus fulfilling our intuition about these constraints.…”

Section: Null Valuesmentioning

confidence: 98%

PG-Keys: Keys for Property Graphs

Angles

Bonifati

Dumbrava

et al. 2021

Proceedings of the 2021 International Conference on Management of Data

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We report on a community effort between industry and academia to shape the future of property graph constraints. The standardization for a property graph query language is currently underway through the ISO Graph Query Language (GQL) project. Our position is that this project should pay close attention to schemas and constraints, and should focus next on key constraints.The main purposes of keys are enforcing data integrity and allowing the referencing and identifying of objects. Motivated by use cases from our industry partners, we argue that key constraints should be able to have different modes, which are combinations of basic restriction that require the key to be exclusive, mandatory, and singleton. Moreover, keys should be applicable to nodes, edges, and properties since these all can represent valid real-life entities. Our result is PG-Keys, a flexible and powerful framework for defining key constraints, which fulfills the above goals.PG-Keys is a design by the Linked Data Benchmark Council's Property Graph Schema Working Group, consisting of members from industry, academia, and ISO GQL standards group, intending to bring the best of all worlds to property graph practitioners. PG-Keys aims to guide the evolution of the standardization efforts towards making systems more useful, powerful, and expressive.

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Section: Null Valuesmentioning

confidence: 98%

PG-Keys: Keys for Property Graphs

Angles

Bonifati

Dumbrava

et al. 2021

Proceedings of the 2021 International Conference on Management of Data

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“…This corresponds to the open-world semantics of incompleteness. Therefore, we assume that the the target domain of queries always uses the OWA semantics, while the source can use either the CWA or OWA semantics; see (Console et al 2020).…”

Section: Relational Databasesmentioning

confidence: 99%

“…All these notions are traditionally tested against a single setting: first-order (or closely related) queries over relational databases, interpreted under set semantics. While understanding certainty and its computational properties in this setting was very useful (Console et al 2020), it nonetheless falls short of what one needs to deal with in realistic everyday queries, like those written in SQL.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Preserving Certain Answers for SQL-like Queries

Toussaint

Guagliardo

Libkin

2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

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Answering queries over incomplete data is based on finding answers that are certainly true, independently of how missing values are interpreted. This informal description has given rise to several different mathematical definitions of certainty. To unify them, a framework based on "explanations", or extra information about incomplete data, was recently proposed. It partly succeeded in justifying query answering methods for relational databases under set semantics, but had two major limitations. First, it was firmly tied to the set data model, and a fixed way of comparing incomplete databases with respect to their information content. These assumptions fail for real-life database queries in languages such as SQL that use bag semantics instead. Second, it was restricted to queries that only manipulate data, while in practice most analytical SQL queries invent new values, typically via arithmetic operations and aggregation. To leverage our understanding of the notion of certainty for queries in SQL-like languages, we consider incomplete databases whose information content may be enriched by additional knowledge. The knowledge order among them is derived from their semantics, rather than being fixed a priori. The resulting framework allows us to capture and justify existing notions of certainty, and extend these concepts to other data models and query languages. As natural applications, we provide for the first time a well-founded definition of certain answers for the relational bag data model and for value-inventing queries on incomplete databases, addressing the key shortcomings of previous approaches.

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“…Analyzing uncertain data without accounting for its uncertainty can create hard to trace errors with severe real world implications. Incomplete database techniques [23] have emerged as a principled way to model and manage uncertainty in data 1 . An incomplete database models uncertainty by encoding a set of possible worlds, each of which is one possible state of the real world.…”

Section: Introductionmentioning

confidence: 99%

“…Many computational problems are intractable over incomplete databases. Even approximations (e.g., [28,31,46]) are often still not efficient enough, are insufficiently expressive, or exclude useful answers [23,26]. Thus, typical database users resort to a cruder, but more practical alternative: resolving uncertainty using heuristics and then treating the result as a deterministic database [67].…”

Section: Introductionmentioning

confidence: 99%

Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)

Feng,

Huber,

Glavic

et al. 2021

Preprint

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Certain answers are a principled method for coping with the uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Prior work introduced Uncertainty Annotated Databases (UA-DBs), which combine an under-and overapproximation of certain answers. UA-DBs combine the reliability of certain answers based on incomplete K-relations with the performance of classical deterministic database systems. However, UA-DBs only support a limited class of queries and do not support attribute-level uncertainty which can lead to inaccurate underapproximations of certain answers. In this paper, we introduce attribute-annotated uncertain databases (AU-DBs) which extend the UA-DB model with attribute-level annotations that record bounds on the values of an attribute across all possible worlds. This enables more precise approximations of incomplete databases. Furthermore, we extend UA-DBs to encode an compact over-approximation of possible answers which is necessary to support non-monotone queries including aggregation and set difference. We prove that query processing over AU-DBs preserves the bounds on certain and possible answers and investigate algorithms for compacting intermediate results to retain efficiency. Through an compact encoding of possible answers, our approach also provides a solid foundation for handling missing data. Using optimizations that trade accuracy for performance, our approach scales to complex queries and large datasets, and produces accurate results. Furthermore, it significantly outperforms alternative methods for uncertain data management.

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Coping with Incomplete Data: Recent Advances

Cited by 13 publications

References 66 publications

PG-Keys: Keys for Property Graphs

PG-Keys: Keys for Property Graphs

Knowledge-Preserving Certain Answers for SQL-like Queries

Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)

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