Scalable XML collaborative editing with undo
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Evaluating CRDTs for real-time document editing
Proceedings of the 11th ACM symposium on Document engineering
Conflict-free replicated data types
SSS'11 Proceedings of the 13th international conference on Stabilization, safety, and security of distributed systems
Synchronizing semantic stores with commutative replicated data types
Proceedings of the 21st international conference companion on World Wide Web
Authenticating operation-based history in collaborative systems
Proceedings of the 17th ACM international conference on Supporting group work
A string-wise CRDT for group editing
Proceedings of the 17th ACM international conference on Supporting group work
LSEQ: an adaptive structure for sequences in distributed collaborative editing
Proceedings of the 2013 ACM symposium on Document engineering
srCE: a collaborative editing of scalable semantic stores on P2P networks
International Journal of Computer Applications in Technology
Live linked data: synchronising semantic stores with commutative replicated data types
International Journal of Metadata, Semantics and Ontologies
A group Undo/Redo method in 3D collaborative modeling systems with performance evaluation
Journal of Network and Computer Applications
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Peer-to-peer systems provide scalable content distribution for cheap and resist to censorship attempts. However, P2P networks mainly distribute immutable content and provide poor support for highly dynamic content such as produced by collaborative systems. A new class of algorithms called CRDT (Commutative Replicated Data Type), which ensures consistency of highly dynamic content on P2P networks, is emerging. However, if existing CRDT algorithms support the "edit anywhere, anytime” feature, they do not support the "undo anywhere, anytime” feature. In this paper, we present the Logoot-Undo CRDT algorithm, which integrates the "undo anywhere, anytime” feature. We compare the performance of the proposed algorithm with related algorithms and measure the impact of the undo feature on the global performance of the algorithm. We prove that the cost of the undo feature remains low on a corpus of data extracted from Wikipedia.