In situ graph querying and analytics with graphgen
Amol Deshpande · 2018
After several decades of research but limited adoption in practice, graph querying and analytics are finally starting to gain a foothold in the data management landscape. This is driven to a large degree by the increasing desire to model and query the key entities and the interconnections between them explicitly; and the observation that the use of network science, graph algorithms, and graph mining can lead to crucial new insights that are not accessible without reasoning about those interconnections in a holistic and collective manner. In addition to the traditional application domains like social media, Web, biological networks, and RDF knowledge bases, graph data models are a natural fit for new and important application domains like personal data management, provenance, metadata management, machine learning, and others. Even data that is not naturally graph-structured is increasingly viewed from the graph lense, examples being shopping transaction data, healthcare data, source code repositories, parcel shipment data, etc.