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All figures (25)
Table 4: Sixteen string similarity measures according to six groups
Figure 3: Graphical notation of a Unified Cube
Figure 11: Combining query results from the DW and the LOD1 dataset
Table 5: Ranking of string similarity measures based on their occurrence among the top five ones according to matching setups
Figure 12: Combining Rtemp with query result from the LOD2 dataset
Figure 13: Analysis result about number of submitted and accepted applications by applicant’s status and housing district, region
Figure 5: Snapshot of instantiated metamodel
Table 3: Details of the datasets used in our experimental assessments
Figure 20: Average runtime of similarity measures in geographic and bibliographic datasets
Table 2: Analysis needs with corresponding measures and attributes
Figure 1: An extract of data in a DW and two LOD datasets
Figure 16: Top 5 F-measures with the corresponding optimal thresholds according to matching setup
Figure 17: Highest F-measure according to similarity measuresgroups and matching setups
Figure 8: A snapshot of the table of correspondences
Figure 7: Identifying equivalent attribute instances within the level lGeo.District
Figure 10: An example of query result
Figure 15: Graphical interface of the multisource analysis framework
Figure 4: Class diagram of the metamodel for Unified Cubes
Table 1: SPARQL queries and their algebraic representation.
Figure 18: F-measure produced by each similarity measure in different datasets
Figure 9: Mediator-based approach versus analysis processing process for Unified Cube
Figure 21: Similarity measures classified according to selection criteria
Figure 14: Architecture of the multisource analysis framework
Figure 19: Standard deviation of each measures highest F-measure obtained from DBLP0 to DBLP50 datasets
Figure 6: Process of matching between two sets of instances from different sources
Journal Article
•
DOI
•
A Unified Approach to Multisource Data Analyses
[...]
Franck Ravat
,
Jiefu Song
01 Jan 2018
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Fundamenta Informaticae