37,712 research outputs found
Scalable XML Collaborative Editing with Undo short paper
Commutative Replicated Data-Type (CRDT) is a new class of algorithms that
ensures scalable consistency of replicated data. It has been successfully
applied to collaborative editing of texts without complex concurrency control.
In this paper, we present a CRDT to edit XML data. Compared to existing
approaches for XML collaborative editing, our approach is more scalable and
handles all the XML editing aspects : elements, contents, attributes and undo.
Indeed, undo is recognized as an important feature for collaborative editing
that allows to overcome system complexity through error recovery or
collaborative conflict resolution
On Coordinating Collaborative Objects
A collaborative object represents a data type (such as a text document)
designed to be shared by a group of dispersed users. The Operational
Transformation (OT) is a coordination approach used for supporting optimistic
replication for these objects. It allows the users to concurrently update the
shared data and exchange their updates in any order since the convergence of
all replicas, i.e. the fact that all users view the same data, is ensured in
all cases. However, designing algorithms for achieving convergence with the OT
approach is a critical and challenging issue. In this paper, we propose a
formal compositional method for specifying complex collaborative objects. The
most important feature of our method is that designing an OT algorithm for the
composed collaborative object can be done by reusing the OT algorithms of
component collaborative objects. By using our method, we can start from correct
small collaborative objects which are relatively easy to handle and
incrementally combine them to build more complex collaborative objects.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499
Data management in cloud environments: NoSQL and NewSQL data stores
: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages
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