TL;DR: It is demonstrated that the proposed automated techniques can estimate fairly accurately the quantity of editors' contributions across various authorship categories, and that the visualizations introduced can clearly convey this information to users.
TL;DR: G-Store is designed and implemented which uses a key-value store as an underlying substrate to provide efficient, scalable, and transactional multi key access, and preserves the desired properties of key- Value stores.
Abstract: Cloud computing has emerged as a preferred platform for deploying scalable web-applications. With the growing scale of these applications and the data associated with them, scalable data management systems form a crucial part of the cloud infrastructure. Key-Value stores -- such as Bigtable, PNUTS, Dynamo, and their open source analogues-- have been the preferred data stores for applications in the cloud. In these systems, data is represented as Key-Value pairs, and atomic access is provided only at the granularity of single keys. While these properties work well for current applications, they are insufficient for the next generation web applications -- such as online gaming, social networks, collaborative editing, and many more -- which emphasize collaboration. Since collaboration by definition requires consistent access to groups of keys, scalable and consistent multi key access is critical for such applications. We propose the Key Group abstraction that defines a relationship between a group of keys and is the granule for on-demand transactional access. This abstraction allows the Key Grouping protocol to collocate control for the keys in the group to allow efficient access to the group of keys. Using the Key Grouping protocol, we design and implement G-Store which uses a key-value store as an underlying substrate to provide efficient, scalable, and transactional multi key access. Our implementation using a standard key-value store and experiments using a cluster of commodity machines show that G-Store preserves the desired properties of key-value stores, while providing multi key access functionality at a very low overhead.
TL;DR: In this article, the authors describe a collaborative editing system that allows multiple collaboration participants to create and edit content in real-time and in a way that allows the content to converge to a desirable intermediate state.
Abstract: A collaborative editing system described allows multiple collaboration participants to create and edit content in real-time and in a way that allows the content to converge to a desirable intermediate state. In addition, the system supports large content teams in which many collaboration participants use the system at the same time. The system distinguishes between real-time collaborative presentation and traditional real-time collaborative editing. Collaborative presentation occurs when the system displays a first collaborator's changes to a second collaborator, without altering the content that the second collaborator is working on. In this way, the second collaborator is aware of the first collaborator's changes, but the changes of other participants do not directly impact the second collaborator's work. The collaborative editing system also separates participants by task so that not all content modifications are shared with all participants.
TL;DR: The Logoot approach is evaluated and compared to others using a corpus of all the edits applied on a set of the most edited and the biggest pages of Wikipedia to provide a peer-to-peer collaborative editing system.
Abstract: Massive collaborative editing becomes a reality through leading projects such as Wikipedia. This massive collaboration is currently supported with a costly central service. In order to avoid such costs, we aim to provide a peer-to-peer collaborative editing system. Existing approaches to build distributed collaborative editing systems either do not scale in terms of number of users or in terms of number of edits. We present the Logoot approach that scales in these both dimensions while ensuring causality, consistency and intention preservation criteria. We evaluate the Logoot approach and compare it to others using a corpus of all the edits applied on a set of the most edited and the biggest pages of Wikipedia.
TL;DR: A computer-implemented collaborative editing method includes receiving input from a user of a browser-based document editing application on a document displayed by the application and identifying a current location in the document for a cursor of a first user executing the application as mentioned in this paper.
Abstract: A computer-implemented collaborative editing method includes receiving input from a user of a browser-based document editing application on a document displayed by the application; identifying a current location in the document for a cursor of a first user executing the application; receiving from a central server system data that reflects changes made to the document by one or more users other than the first user and current positions in the document of cursors for the one or more other user; updating a document model stored on a computing device that is executing the browser-based application and rendering at least a portion of the model to the browser; and rendering the current positions of the cursors for the one or more other users to the browser.