Answering heterogeneous database queries with degrees of uncertainty

Tseng, Frank S. C.; Chen, Arbee L. P.; Yang, Wei‐Pang

doi:10.1007/bf01263334

Cited by 52 publications

(19 citation statements)

References 24 publications

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“…We further generalize the concept of partial values into probabilistic partial values (Tseng et al, 1993a) to resolve more interoperability problems, and to join relations on incompatible keys (Tsai and Chen, 1993) in multidatabase systems.…”

Section: Introductionmentioning

confidence: 99%

Searching a minimal semantically-equivalent subset of a set of partial values

1993

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Abstract. Imprecise data exist in databases due to their unavailability or to data/ schema incompatibilities in a multidatabase system. Partial values have been used to represent imprecise data. Manipulation of partial values is therefore necessary to process queries involving imprecise data. In this article, we study the problem of eliminating redundant partial values that result from a projection on an attribute with partial values. The redundancy of partial values is defined through the interpretation of a set of partial values. This problem is equivalent to searching a minimal semantically-equivalent subset of a set of partial values. A semanticallyequivalent subset contains exactly the same information as the original set. We derive a set of useful properties and apply a graph matching technique to develop an efficient algorithm for searching such a minimal subset and therefore eliminating redundant partial values. By this process, we not only provide a concise answer to the user, but also reduce the communication cost when partial values are requested to be transmitted from one site to another site in a distributed environment. Moreover, further manipulation of the partial values can be simplified. This work is also extended to the case of multi-attribute projections.

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Section: Introductionmentioning

confidence: 99%

Searching a minimal semantically-equivalent subset of a set of partial values

1993

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“…Although data integration is regarded as an important application area for probabilistic databases, much work focuses on modeling the uncertainty in schema matching [Dong et al 2009] or in the merge result of conflicting tuples [Tseng et al 1993]. Modeling the uncertainty around possible duplicate representations, is much less researched.…”

Section: Contributionmentioning

confidence: 99%

“…In [de Keijzer and van Keulen 2008] a semi-structured probabilistic model is used for handling ambiguities in deduplication of XML data. Tseng [Tseng et al 1993] already used probabilistic values to resolve conflicts between two or more certain relational values. None of the studies, however, handle the uncertainty of duplicate decisions in detecting relational duplicates.…”

Section: Related Workmentioning

confidence: 99%

Indeterministic Handling of Uncertain Decisions in Deduplication

Panse

Keulen

Ritter

2013

J. Data and Information Quality

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In current research and practice, deduplication is usually considered as a deterministic approach in which tuples are either declared to be duplicates or not. In ambiguous situations, however, it is often not completely clear-cut, which tuples represent the same real-world entity. In deterministic approaches, many realistic possibilities may be ignored, which in turn can lead to false decisions. In this paper, we present an indeterministic approach for deduplication by using a probabilistic target model including techniques for proper probabilistic interpretation of similarity matching results. Thus, instead of deciding for a most likely situation, all realistic situations are modeled in the resultant data. This approach minimizes the negative impact of false decisions. Furthermore, the deduplication process becomes almost fully automatic and human effort can be reduced to a large extent. To increase applicability, we introduce several semi-indeterministic methods that heuristically reduce the set of indeterministically handled decisions in a several meaningful ways. We also describe a full-indeterministic method for theoretical and presentational reasons.

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“…However, no details were given on how to map conflicting values in to the common domain of virtual attributes. Tseng, Chen, Yang further generalized the concept of partial values into probabilistic partial values to capture the uncertainty in attributes values [37]: The possible values of an attribute are listed and probabilities are given to indicate their likelihood. Lim, Srisvastava and Shekhar proposed an extended relational model based on Dempster-Shafer Theory of Evidence to deal with the situation where uncertain information arises when the database integration process requires information not directly represented in the component databases but can be obtained through some of the data [22].…”

Section: Related Workmentioning

confidence: 99%

DIRECT: A System for Mining Data Value Conversion Rules from Disparate Data Sources

et al. 2003

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The successful integration of data from autonomous and heterogeneous systems calls for the resolution of semantic conflicts that may be present. Such conflicts are often reflected by discrepancies in attribute values of the same data object. In this paper, we describe a recently developed prototype system, DIRECT (DIscovering and REconciling ConflicT s).The system mines data value conversion rules in the process of integrating business data from multiple sources. The system architecture and functional modules are described. The process of discovering conversion rules from sales data of a trading company is presented as an illustrative example.

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Answering heterogeneous database queries with degrees of uncertainty

Cited by 52 publications

References 24 publications

Searching a minimal semantically-equivalent subset of a set of partial values

Searching a minimal semantically-equivalent subset of a set of partial values

Indeterministic Handling of Uncertain Decisions in Deduplication

DIRECT: A System for Mining Data Value Conversion Rules from Disparate Data Sources

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