Abstract: A system for cross-domain adaptation includes an analysis module, a set configuration module, a set multiplexer, a scalable bloom filter, a mapping module, and an aggregation module. The cross-domain adaptation system may allow for bloom filter adaptation across industries and domains with minimal increases in necessary hardware and consequentially minimal increases in power consumption and cost of area. A method for multi-domain adaptation in datasets includes performing multi-domain semantic analysis on a set of data, providing two complementary domain adaptive sets, processing the complementary domain adaptive sets to provide a multi-fractal representation of the dataset, processing the multi-fractal representation of the dataset with a scalable bloom filter to provide two filtered domain adaptive sets, providing a bijective holomorphic map of the filtered domain adaptive sets, and processing the bijective holomorphic map to provide multi-domain insight aggregator output.

Abstract: A system for cross-domain adaptation includes an analysis module, a set configuration module, a set multiplexer, a scalable bloom filter, a mapping module, and an aggregation module. The cross-domain adaptation system may allow for bloom filter adaptation across industries and domains with minimal increases in necessary hardware and consequentially minimal increases in power consumption and cost of area. A method for multi-domain adaptation in datasets includes performing multi-domain semantic analysis on a set of data, providing two complementary domain adaptive sets, processing the complementary domain adaptive sets to provide a multi-fractal representation of the dataset, processing the multi-fractal representation of the dataset with a scalable bloom filter to provide two filtered domain adaptive sets, providing a bijective holomorphic map of the filtered domain adaptive sets, and processing the bijective holomorphic map to provide multi-domain insight aggregator output.

Abstract: Aspects of the present invention disclose a method for event discovery based on semantic patterns. The method includes one or more processors receiving data from one or more devices. The method further includes one or more processors generating one or more semantic patterns from the data. The method further includes one or more processors comparing the one or more new semantic patterns to one or more existing semantic patterns. The method further includes one or more processors determining whether the one or more new semantic patterns are substantially unique based on the comparison.