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Title: The Sea of Stuff : a model to manage shared mutable data in a distributed environment
Author: Conte, Simone Ivan
ISNI:       0000 0004 7656 8540
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2019
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Managing data is one of the main challenges in distributed systems and computer science in general. Data is created, shared, and managed across heterogeneous distributed systems of users, services, applications, and devices without a clear and comprehensive data model. This technological fragmentation and lack of a common data model result in a poor understanding of what data is, how it evolves over time, how it should be managed in a distributed system, and how it should be protected and shared. From a user perspective, for example, backing up data over multiple devices is a hard and error-prone process, or synchronising data with a cloud storage service can result in conflicts and unpredictable behaviours. This thesis identifies three challenges in data management: (1) how to extend the current data abstractions so that content, for example, is accessible irrespective of its location, versionable, and easy to distribute; (2) how to enable transparent data storage relative to locations, users, applications, and services; and (3) how to allow data owners to protect data against malicious users and automatically control content over a distributed system. These challenges are studied in detail in relation to the current state of the art and addressed throughout the rest of the thesis. The artefact of this work is the Sea of Stuff (SOS), a generic data model of immutable self-describing location-independent entities that allow the construction of a distributed system where data is accessible and organised irrespective of its location, easy to protect, and can be automatically managed according to a set of user-defined rules. The evaluation of this thesis demonstrates the viability of the SOS model for managing data in a distributed system and using user-defined rules to automatically manage data across multiple nodes.
Supervisor: Dearle, Alan ; Kirby, Graham N. C. Sponsor: Adobe Systems ; Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Distributed storage ; Data model ; Data management ; Peer-to-peer ; QA76.9D5C76 ; Virtual storage (Computer science) ; Electronic data processing--Distributed processing ; Information storage and retrieval systems ; Peer-to-peer architecture (Computer networks) ; Database management