Pinaki Laskar on LinkedIn: #BigData #DataScience #machinelearning

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why Real #BigData is impossible without Data Ontology? Mathematics is key to DO. It is dealing with ontological entities but as mathematical objects, as quantities, changes, and relationships (numbers, magnitudes, multitudes, spaces, manifolds) and their functional relationships, as listed below: Number theory: numbers, operations Combinatorics: permutations, derangements, combinations Set theory: sets, set partitions; functions, and relations Geometry: points, lines, line segments, polygons, circles, ellipses, parabolas, hyperbolas, polyhedra, spheres, ellipsoids, paraboloids, hyperboloids, cylinders, cones Graph theory: graphs, trees, nodes, edges Topology: topological spaces and manifolds Linear algebra: scalars, vectors, matrices, tensors Abstract algebra: groups, rings, modules, fields, vector spaces, group-theoretic lattices, and order-theoretic lattices Category Theory (a general theory of functions): categories, objects, edges Datalogy, as #DataScience and technology summarized as follows: Easy Architectural Changes: Applying structural changes to relational databases is a cumbersome process. Something as simple as changing a property from being single-valued to multi-valued could mean having to add a new table and foreign key reference to the original table, possibly compromising existing queries to it. With an ontology, you could simply modify the semantic concept underpinning the property.