Prioritized repairing and consistent query answering in relational databases

Staworko, Sławek; Chomicki, Jan; Marcinkowski, Jerzy

doi:10.1007/s10472-012-9288-8

Cited by 79 publications

(109 citation statements)

References 32 publications

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“…A prioritized repair [32] is an extension of the concept of a repair in inconsistent relational databases which accounts for priorities among conflicting tuples. Unlike our preference rules, the work of [32] assumes that an explicit priority relation, which defines preferences between tuples, is available.…”

Section: Temporal Queries Over the In-tegrated Entitiesmentioning

confidence: 99%

“…Unlike our preference rules, the work of [32] assumes that an explicit priority relation, which defines preferences between tuples, is available. They studied various notions of optimality of instances with respect to a given priority relation under the class of denial constraints, which includes the class of key constraints as a special case.…”

Section: Temporal Queries Over the In-tegrated Entitiesmentioning

confidence: 99%

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Preference-aware integration of temporal data

Alexe

Roth

Tan³

2014

Proc. VLDB Endow.

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A complete description of an entity is rarely contained in a single data source, but rather, it is often distributed across different data sources. Applications based on personal electronic health records, sentiment analysis, and financial records all illustrate that significant value can be derived from integrated, consistent, and queryable profiles of entities from different sources. Even more so, such integrated profiles are considerably enhanced if temporal information from different sources is carefully accounted for.We develop a simple and yet versatile operator, called PRAWN, that is typically called as a final step of an entity integration workflow. PRAWN is capable of consistently integrating and resolving temporal conflicts in data that may contain multiple dimensions of time based on a set of preference rules specified by a user (hence the name PRAWN for preference-aware union). In the event that not all conflicts can be resolved through preferences, one can enumerate each possible consistent interpretation of the result returned by PRAWN at a given time point through a polynomialdelay algorithm. In addition to providing algorithms for implementing PRAWN, we study and establish several desirable properties of PRAWN. First, PRAWN produces the same temporally integrated outcome, modulo representation of time, regardless of the order in which data sources are integrated. Second, PRAWN can be customized to integrate temporal data for different applications by specifying application-specific preference rules. Third, we show experimentally that our implementation of PRAWN is feasible on both "small" and "big" data platforms in that it is efficient in both storage and execution time. Finally, we demonstrate a fundamental advantage of PRAWN: we illustrate that standard query languages can be immediately used to pose useful temporal queries over the integrated and resolved entity repository.

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Section: Temporal Queries Over the In-tegrated Entitiesmentioning

confidence: 99%

Section: Temporal Queries Over the In-tegrated Entitiesmentioning

confidence: 99%

Preference-aware integration of temporal data

Alexe

Roth

Tan³

2014

Proc. VLDB Endow.

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“…The database research community has proposed principled ways to capture, manage and resolve data inconsistency [3,5,6,[15][16][17]29,38]. Here, the identification and capturing of inconsistencies is usually done through the use of specialized constraints and dependencies [3,15,16,29,38].…”

Section: Introductionmentioning

confidence: 99%

“…A prominent formalism for specifying inconsistencies in this respect are the denial constraints [3,38] that can specify, for example that no two persons can have the same driver's license number, or that no single person can have multiple residential addresses. To manage and resolve data inconsistencies, the database research community has proposed a wide variety of methods, ranging from consistent query answering [3], to formalisms for the declarative specification of conflict resolution by means of prioritized repairs [38], to data cleaning and data fusion tools that introduce domain-specific operators for removing data inconsistencies [5,6,17].…”

Section: Introductionmentioning

confidence: 99%

“…); those cleaning policies imply not only which sets of facts are in conflict, but also which facts should remain and which facts should be dropped. To accommodate preferences, we adopt the prioritized repairs of Staworko et al [38], which extend the concept of repairs with priorities among facts that, eventually, translate into priorities among repairs. More precisely, Staworko et al assume that in addition to denial constraints, the inconsistent database is associated with a binary relation ą, called priority, over the facts (where f1 ą f2 means that f1 has priority over f2).…”

Section: Introductionmentioning

confidence: 99%

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Cleaning inconsistencies in information extraction via prioritized repairs

Fagin

Kimelfeld²,

Reiss

et al. 2014

Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems

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The population of a predefined relational schema from textual content, commonly known as Information Extraction (IE), is a pervasive task in contemporary computational challenges associated with Big Data. Since the textual content varies widely in nature and structure (from machine logs to informal natural language), it is notoriously difficult to write IE programs that extract the sought information without any inconsistencies (e.g., a substring should not be annotated as both an address and a person name). Dealing with inconsistencies is hence of crucial importance in IE systems. Industrial-strength IE systems like GATE and IBM SystemT therefore provide a built-in collection of cleaning operations to remove inconsistencies from extracted relations. These operations, however, are collected in an ad-hoc fashion through use cases. Ideally, we would like to allow IE developers to declare their own policies. But existing cleaning operations are defined in an algorithmic way and, hence, it is not clear how to extend the built-in operations without requiring low-level coding of internal or external functions.We embark on the establishment of a framework for declarative cleaning of inconsistencies in IE, though principles of database theory. Specifically, building upon the formalism of document spanners for IE, we adopt the concept of prioritized repairs, which has been recently proposed as an extension of the traditional database repairs to incorporate priorities among conflicting facts. We show that our framework captures the popular cleaning policies, as well as the POSIX semantics for extraction through regular expressions. We explore the problem of determining whether a cleaning declaration is unambiguous (i.e., always results in a single repair), and whether it increases the expressive power of the extraction language. We give both positive and negative results, some of which are general, and some of which apply to policies used in practice.

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Measuring and Computing Database Inconsistency via Repairs

Bertossi

2018

Lecture Notes in Computer Science

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Prioritized repairing and consistent query answering in relational databases

Cited by 79 publications

References 32 publications

Preference-aware integration of temporal data

Preference-aware integration of temporal data

Cleaning inconsistencies in information extraction via prioritized repairs

Measuring and Computing Database Inconsistency via Repairs

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