An Analytics-Aware Conceptual Model For Evolving Graphs by Amine Ghrab, Sabri Skhiri, Salim Jouili, and Esteban Zimanyi.
Abstract:
Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge for graph data management, many graph database solutions were developed. These databases are commonly classified as NoSQL graph databases, and they provide better support for graph data management than their relational counterparts. However, each of these databases implement their own operational graph data model, which differ among the products. Further, there is no commonly agreed conceptual model for graph databases.
In this paper, we introduce a novel conceptual model for graph databases. The aim of our model is to provide analysts with a set of simple, well-defined, and adaptable conceptual components to perform rich analysis tasks. These components take into account the evolving aspect of the graph. Our model is analytics-oriented, flexible and incremental, enabling analysis over evolving graph data. The proposed model provides a typing mechanism for the underlying graph, and formally defines the minimal set of data structures and operators needed to analyze the graph.
The authors concede that much work remains to be done, both theoretical and practical on their proposal.
With the rise of distributed computing, every “fact” depends upon a calculated moment of now. What was a “fact” five minutes ago may not longer be considered as a “fact” but as an “error.”
Who is responsible for changes in “facts,” warranties for “facts,” who gives and gets notices about changes in “facts,” all remain to be determined.
Models for evolving graphs may assist in untangling the rights, obligations and relationships that are nearly upon us with distributed computing.