A Data Lake Metadata Enrichment Mechanism via Semantic Blueprints - Pingos, Michalis; Andreou, Andreas S. (My University, 2022)
One of the greatest challenges in Smart Big Data Processing nowadays revolves around handling multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data through Data Lakes. The latter requires a disciplined approach to collect, store and retrieve/analyse data to enable efficient predictive and prescriptive modelling, as well as the development of other advanced analytics applications on top of it. The present paper addresses this highly complex problem and proposes a novel standardization framework that combines mainly the 5Vs Big Data characteristics, blueprint ontologies and Data Lakes with ponds architecture, to offer a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval from a Data Lake. The proposed mechanism is compared qualitatively against existing metadata systems using a set of functional characteristics or properties, with the results indicating that it is indeed a promising approach.
Keywords: 5Vs Big Data Characteristics; Data Blueprints; Data Lakes; Deep Insight; Heterogeneous Data Sources; Metadata Mechanism; Smart Data Processing