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CyCLaDEs: A Decentralized Cache for Triple Pattern Fragments
Published on Jul 28, 20161176 Views
The Linked Data Fragment (LDF) approach promotes a new trade-off between performance and data availability for querying Linked Data. If data providers’ HTTP caches plays a crucial role in LDF performa
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Chapter list
CyCLaDEs: A Decentralized Cache for Triple Pattern Fragments00:00
Context - 100:16
Context - 200:40
Triple Pattern Fragment & Caches01:08
What happens if clients collaborate?02:05
What if clients collaborate?02:08
Related Works - 102:43
Related Works - 203:20
CyCLaDEs Approach03:52
LDF: Approach Overview - 104:34
LDF: Approach Overview - 204:49
LDF: Approach Overview - 305:31
LDF: Approach Overview - 406:08
How to profile nodes?06:52
Node profile - Algorithm - 107:17
Node profile - Algorithm - 207:22
Node profile - Algorithm - 307:29
Node profile - Algorithm - 407:33
Node profile - Algorithm - 507:36
Node profile - Algorithm - 607:39
Node profile - Algorithm - 707:47
Node profile - Algorithm - 807:52
Computing Profile - 108:02
Computing Profile - 208:32
Computing Profile - 308:38
Computing Profile - 408:43
Computing Profile - 508:48
Computing Profile - 608:50
Computing Profile - 708:51
Computing Profile - 808:57
Computing Profile - 909:01
How to compare neighbors?09:38
C5 shuffling with C6 : #RPS=2, #CON=410:17
Queries with CyCLaDEs11:11
Experiments11:58
Experiments - Parameters12:27
BSBM 1M, cache = 1000, profile size = 1012:57
10 clients, RPS = 4, CON = 9, cache = 100014:02
CyCLaDEs with 2 Communities14:41
2 BSBM 1M datasets, 50 clients per data set, cache = 1000 15:07
Conclusion15:49
Future Works16:16
CyCLaDEs: A Decentralized Cache for Triple Pattern Fragments16:46