Abstract: A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.

Abstract: A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.

Abstract: A building risk analysis system including one or more memory devices storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive threats, each of the threats including a location, wherein each of the threats are threats of a particular threat category, determine a number of threats for each of geographic areas based on the location of each of the threats, and generate a distribution based on the number of threats for each of the geographic areas. The instructions further cause the one or more processors to determine a risk score for each of the geographic areas based on one or more characteristics of the distribution and the number of threats for each of the geographic areas.

Abstract: A building system including one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to generate building recommendations based on recommendation requests with a model of a first model type when less than a predefined amount of model training data is available and receive feedback data on the recommendation requests generated by the model of the first model type. The instructions cause the one or more processors to transition from generating the building recommendations by the model of the first model type to a second model of a second model type by comparing performance of the first model type to the second model type based on the feedback data.

Abstract: A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.

Abstract: A building system including one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a statistical model based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the statistical model, wherein the statistical model outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms, wherein the statistical model implements a many to many mapping between the particular plurality of acronyms and a plurality of target tags.

Abstract: A building risk analysis system including one or more memory devices storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive threats, each of the threats including a location, wherein each of the threats are threats of a particular threat category, determine a number of threats for each of geographic areas based on the location of each of the threats, and generate a distribution based on the number of threats for each of the geographic areas. The instructions further cause the one or more processors to determine a risk score for each of the geographic areas based on one or more characteristics of the distribution and the number of threats for each of the geographic areas.

Abstract: A building schema mapping system, the system including one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to receive strings in a first schema, each string representing at least one of a point, building equipment, or a building space, extract relationships from the strings, each relationship of the relationships indicating a particular relationship between a first string of the strings and a second string of the strings, label each of the strings based on characters of each of the strings, and generate a graph data structure of a second schema based on the relationships and the label of each of the strings.