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Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking
Published on Jul 30, 20142648 Views
Generators for synthetic RDF datasets are very important for testing and benchmarking various semantic data management tasks (e.g. querying, storage, update, compare, integrate). How ever, the current
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Chapter list
Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking 00:00
Outline00:09
Motivation - 100:32
Motivation - 201:44
Motivation - 302:34
Motivation - 403:03
Motivation03:18
Related work04:11
Requirements05:18
Used Schema (Over a social network)05:58
Instance of person06:39
BC Component08:48
Controlling the complexity09:41
Controlling the complexity: Morphology10:03
Controlling the complexity: Clustering coefficient - 110:34
Controlling the complexity: Clustering coefficient - 211:02
Controlling the complexity: Clustering coefficient - 311:14
Controlling the complexity: Clustering coefficient - 411:21
Controlling the complexity: Clustering coefficient - 511:47
Controlling the complexity: Clustering coefficient - 611:54
Controlling the complexity: Density - 112:15
Controlling the complexity: Density - 212:39
Controlling the complexity: Similarity mode12:50
BGen: Input parameters - 113:37
BGen: Input parameters - 213:41
BGen: Input parameters - 314:06
BGen: Input parameters - 414:08
BGen: the algorithm14:19
BGem: Preparation phase14:27
BGen: Instance Generation phase14:34
BGen: Connection phase14:46
Experimental Evaluation14:55
Experimental Evaluation: Resources15:16
Experimental Evaluation: Complexity - 115:25
Experimental Evaluation: Complexity - 215:27
Experimental Evaluation: Complexity - 315:41
Experimental Evaluation: Complexity - 415:46
Using the generated datasets: Signature alg - 116:04
Using the generated datasets: Signature alg - 216:45
Using the generated datasets: Signature alg - 317:20
Using the generated datasets: Signature alg - 417:31
Conclusion17:43
Future Work18:13
Thank you!18:32