• Foundations for data organization and database theory underpin later data science, emphasizing relational models, data management, and semantic structuring of data, including representations of structure and similarity data [5], [11], [13], [18], [19].

• Clustering and pattern discovery matured as core data science methods: early unsupervised clustering approaches, model-based clustering, document clustering, and the beginnings of knowledge discovery in databases (KDD) [2], [3], [7], [8], [9], [16].

• Distances, proximities, and similarity measures organize data representations, guiding clustering and retrieval; MD scaling with an unknown distance function, similarity data representations, and fuzzy-set validity shape evaluation [1], [4], [10], [12], [19].

• Pattern recognition and image analysis provide benchmark problems and algorithmic ideas for automated perception; this includes pattern classification, linear pattern matching algorithms, and scene analysis [4], [8], [20].

• Algorithmic infrastructure for scalable data processing includes efficient range-search data structures, inverted database structures, and data representation diagraphs that support storage and querying [6], [13], [17], [18].