Nowadays, real-time editing systems are catching on. Tools such as Etherpad or Google Docs enable multiple authors at dispersed locations to collaboratively write shared documents. In such systems, a replication mechanism is required to ensure consistency when merging concurrent changes performed on the same document. Current editing systems make use of operational transformation (OT), a traditional replication mechanism for concurrent document editing.Recently, Commutative Replicated Data Types (CRDTs) were introduced as a new class of replication mechanisms whose concurrent operations are designed to be natively commutative. CRDTs, such as WOOT, Logoot, Treedoc, and RGAs, are expected to be substitutes of replication mechanisms in collaborative editing systems.This paper demonstrates the suitability of CRDTs for real-time collaborative editing. To reflect the tendency of decentralised collaboration, which can resist censorship, tolerate failures, and let users have control over documents, we collected editing logs from real-time peer-to-peer collaborations. We present our experiment results obtained by replaying those editing logs on various CRDTs and an OT algorithm implemented in the same environment.

Abstract. Research in collaborative editing tends to have been undertaken in isolation rather than as part of a general information or application infrastructure. Our goal is to develop a universal information platform that can support collaboration in a range of application domains. Since not all user groups have the same conventions and not all tasks have the same requirements, this implies that it should be possible to customize the collaborative editor at the level of both communities and individual tasks. One of the keys to customization is to use a structured rather than linear representation of documents that can be applied to both textual and graphical editors. In this paper, we propose the treeOPT (tree OPerational Transformation) algorithm that, relying on a tree representation of documents, applies the operational transformation mechanism recursively over the different document levels. Applications using this algorithm achieve better efficiency, the possibility of working at different granularity levels and improvements in the semantic consistency.

As Wikipedia became the largest human knowledge repository, quality measurement of its articles received a lot of attention during the last decade. Most research efforts focused on classification of Wikipedia articles quality by using a different feature set. However, so far, no "golden feature set" was proposed. In this paper, we present a novel approach for classifying Wikipedia articles by analysing their content rather than by considering a feature set. Our approach uses recent techniques in natural language processing and deep learning, and achieved a comparable result with the state-of-the-art.

Shared data is usually fragmented into smaller atomic elements that can only be added or removed. Coarse-grained data leads to the possibility of conflicting updates while fine-grained data requires more metadata. In this paper we offer a solution for handling an adaptable granularity for shared data that overcomes the limitations of fixed-grained data approaches. Our approach defines data at a coarse granularity when it is created and refines its granularity only for facing possible conflicting updates on this data. We exhibit three implementations of our algorithm and compare their performances with other algorithms in various scenarios.