Propagation of Data Fusion

Bronselaer, Antoon; Britsom, Daan Van; Tré, Guy De

doi:10.1109/tkde.2014.2365807

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…One particular problem with fusion by means of relational operators, is that it does not deal well with errors in the data. To solve this problem, attribute-level fusion functions can be used to deal with linear conversions [35,36], specificity of information [37] and multi-valued data [38,39]. A good overview of possible attribute-level fusion functions can be found in [1].…”

Section: Repairs In Data Fusionmentioning

confidence: 99%

Parker: Data fusion through consistent repairs using edit rules under partial keys

Bronselaer

Acosta

2023

Information Fusion

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Section: Repairs In Data Fusionmentioning

confidence: 99%

Parker: Data fusion through consistent repairs using edit rules under partial keys

Bronselaer

Acosta

2023

Information Fusion

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“…One particular problem with fusion by means of relational operators, is that it does not deal well with errors in data. To that extent, attributelevel fusion functions can be used to deal with linear conversions [22,23], specificity of information [24] and multi-valued data [25,26]. A good overview of possible attribute-level fusion functions can be found in [27].…”

Section: Related Workmentioning

confidence: 99%

Consistent data fusion with Parker

Bronselaer¹,

Acosta²

2022

Preprint

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When combining data from multiple sources, inconsistent data complicates the production of a coherent result. In this paper, we introduce a new type of constraints called edit rules under a partial key (EPKs). These constraints can model inconsistencies both within and between sources, but in a loosely-coupled matter. We show that we can adapt the well-known set cover methodology to the setting of EPKs and this yields an efficient algorithm to find minimal cost repairs of sources. This algorithm is implemented in a repair engine called Parker. Empirical results show that Parker is several orders of magnitude faster than state-of-the-art repair tools. At the same time, the quality of the repairs in terms of F 1 -score ranges from comparable to better compared to these tools.

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“…Incomplete data sets due to missing values have been reported as one of the challenges in data duplication [10,27], along with other issues. These issues are typographical errors, abbreviations, and different representations of the same logical values [12,23,[28][29][30][31]. Records with missing entries are of two forms: fully missing (all attributes' values except the primary key are missing) or partially missing (some of the attributes' values are missing).…”

Section: Related Workmentioning

confidence: 99%

Missing values compensation in duplicates detection using hot deck method

2021

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Duplicate record is a common problem within data sets especially in huge volume databases. The accuracy of duplicate detection determines the efficiency of duplicate removal process. However, duplicate detection has become more challenging due to the presence of missing values within the records where during the clustering and matching process, missing values can cause records deemed similar to be inserted into the wrong group, hence, leading to undetected duplicates. In this paper, duplicate detection improvement was proposed despite the presence of missing values within a data set through Duplicate Detection within the Incomplete Data set (DDID) method. The missing values were hypothetically added to the key attributes of three data sets under study, using an arbitrary pattern to simulate both complete and incomplete data sets. The results were analyzed, then, the performance of duplicate detection was evaluated by using the Hot Deck method to compensate for the missing values in the key attributes. It was hypothesized that by using Hot Deck, duplicate detection performance would be improved. Furthermore, the DDID performance was compared to an early duplicate detection method namely DuDe, in terms of its accuracy and speed. The findings yielded that even though the data sets were incomplete, DDID was able to offer a better accuracy and faster duplicate detection as compared to DuDe. The results of this study offer insights into constraints of duplicate detection within incomplete data sets.

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Propagation of Data Fusion

Cited by 10 publications

References 12 publications

Parker: Data fusion through consistent repairs using edit rules under partial keys

Parker: Data fusion through consistent repairs using edit rules under partial keys

Consistent data fusion with Parker

Missing values compensation in duplicates detection using hot deck method

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