FDC Cache: Semantics driven Federated Caching and Querying for Big Data
To deliver business value, most data driven enterprises and applications require data to be extracted and merged from otherwise siloed data storage platforms. FDC Cache has been designed and developed to enable the fusion and caching of data drawn from multiple small and/or Big Data stores. This capability executes a sequence of queries, wherein the results from one query may be used to constrain subsequent queries. The results of each query are linked with results from previous queries, incrementally building a cache of semantically linked data that can be used to support multiple independent data requests. FDC Cache uses Semantic Web technologies, and knowledge graphs in particular, to describe the relevant data and relationships in a computable model. This enables applications to reason over the graph, for example to dynamically retrieve targeted subsets of data comprised of previously disparate information. We have successfully applied FDC Cache to two distinct industrial use cases: (i) merging data across multiple sources to assemble information about current parts in a gas turbine, and (ii) dynamically aligning siloed data from electric grid transmission and distribution networks to an industry standard common model, in which the cache creation time has been shown to scale sub linearly with the number of data elements. FDC Cache has been open sourced as part of the GE developed open source Semantics Toolkit.
2020 IEEE International Conference on Big Data (Big Data)