Abstract: In general, various aspects of the techniques are directed to causal analysis using large scale time series data. A computing system may convert large scale time series data to first time period records and second time period records according to a multi-scale time resolution. The computing system may implement a hierarchical machine learning model to generate embeddings that capture temporal characteristics of features of the large scale time series data. The computing system may generate a graph data structure indicating cause and effect correlations between features of the large scale time series data based on temporal dynamics captured in the cause and second time period records and/or the embeddings.

Abstract: In general, the disclosure describes techniques for joint spatiotemporal Artificial Intelligence (AI) models that can encompass multiple space and time resolutions through self-supervised learning. In an example, a method includes for each of a plurality of multimodal data, generating, by a computing system, using a first machine learning model, a respective modality feature vector representative of content of the multimodal data, wherein each of the generated modality feature vectors has a different modality; processing, by the computing system, each of generated modality feature vectors with a second machine learning model comprising an encoder model to generate event data comprising a plurality of events and/or activities of interest; and analyzing, by the computing system, the event data to generate anomaly data indicative of detected anomalies in the multimodal data.

Abstract: An IP-to-Domain (IP2D) resolution system predicts which domain is most likely associated with an IP address. The resolution system generates unique source vote features (FSV) from (IP, domain, source) data. The FSV features are used to train a machine learning model that predicts which domain is most likely associated with an IP address. The domain predictions can then be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated company surges.

Abstract: Inferring multimodal content intent in a common geometric space in order to improve recognition of influential impacts of content includes mapping the multimodal content in a common geometric space by embedding a multimodal feature vector representing a first modality of the multimodal content and a second modality of the multimodal content and inferring intent of the multimodal content mapped into the common geometric space such that connections between multimodal content result in an improvement in recognition of the influential impact of the multimodal content.

Abstract: Embedding multimodal content in a common geometric space includes for each of a plurality of content of the multimodal content, creating a respective, first modality feature vector representative of content of the multimodal content having a first modality using a first machine learning model; for each of a plurality of content of the multimodal content, creating a respective, second modality feature vector representative of content of the multimodal content having a second modality using a second machine learning model; and semantically embedding the respective, first modality feature vectors and the respective, second modality feature vectors in a common geometric space that provides logarithm-like warping of distance space in the geometric space to capture hierarchical relationships between seemingly disparate, embedded modality feature vectors of content in the geometric space; wherein embedded modality feature vectors that are related, across modalities, are closer together in the geometric space than un