May 8, 2018
Knowledge Integrity, Inc.
While traditional relational database management systems (RDBMS) can be used to represent relationships among parties (such as connections between individuals, warehouses and delivery points, or computers in a network), the characteristics of those relationships are not easily captured in the relational model. However, organizations are increasingly dealing with a growing breadth of both structured and unstructured data sources, including data streams continuously fed by Internet-connected devices, sensors, and the human-generated content emanating from wikis, web communities, and social media networks.
These data sources reflect information about transactions and statuses within the context of networks of connections and relationships, requiring a more flexible means for capturing and analyzing information about entities along with their relationships. Yet the limitations of the relational model have become more acute, creating an opportunity for disruptive approaches like graph data management to be employed.
Graph databases and analytics systems rely on an alternative approach to data representation that not only capture information about entities and their attributes, but also elevates the relationships among the entities to be first-class objects. In this talk we look at how graphs provide a different view into relationship information and explore emerging and growing opportunities for using graph databases and graph analytics to explore analytics in the context of connectivity.
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