Mapped Inc.

Jason Koh

Shaun P Cooley

Jose De Castro

Jose Vazquez-canteli

Disclosed are methods and systems for predicting relationships between points in automated environments by retrieving a plurality of data points from a plurality of data sources associated with an automated environment, wherein the plurality of data points comprises timeseries data structure; detecting a plurality of events based on the plurality of data points, wherein an event of the plurality of data points is indicative of an anomaly reading of one or more states associated with the plurality of data sources; generating a correlation matrix of events across the plurality of data points; suppressing non-physical relation factors in the correlation matrix by normalization to generate a normalized matrix; and clustering the data points based on the normalized matrix, wherein the clustered data points represent the data points belonging to a same data source.

Automatic link prediction for points in commercial and industrial environments

Described are platforms, systems, and methods to combine counts of activity correlations over time with a link salience method to identify collections of digital devices in an automated environment to identify sub-systems comprised of portions of the overall environment. The platforms, systems, and methods detect activity in a plurality of data sources associated with an automation environment; determine correlation in the detected activity between two or more of the data sources; store records of determined correlation in the detected activity over time in a data storage system; apply a link salience algorithm to the stored records of determined correlation in the detected activity to determine a salience property; and identify one or more subsystems in the automation environment based on the salience property.

Automatic discovery of automated digital systems through link salience

Described are platforms, systems, and methods to automatically discover, extract, map, merge, and enrich data found in on-premises in automated industrial and commercial environments and cloud systems for purposes of providing developers access to normalized, merged, and enriched data through an API. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; retrieve data from at least one of the identified data sources; apply a first algorithm to map the retrieved data to a predetermined ontology; merge the mapped data into a data store comprising timeseries of the mapped data; apply a second algorithm to identify patterns in the merged data and enriching the data based on one or more identified patterns; and provide one or more APIs or one or more real-time streams to provide access to the enriched data.

Automated data integration pipeline with storage and enrichment

Described herein are systems and methods for utilizing ensemble learning methods to extract semantics of data in buildings by retrieving a plurality of data sets from a plurality of data sources associated with an automated environment; labeling a subset of the plurality of data sets by applying Natural Language Processing (NLP) on manufacturer specifications to generate a plurality of labels associated with the subset of the plurality of data sets, respectively; training a learning model on the subset of the plurality of data sets and the plurality of labels; and applying the learning model on remanding subset of the plurality of data sets to generate a semantic map indicative of semantic arrangement of the plurality of data sources associated with the automated environment.

Ensemble learning for extracting semantics of data in building systems

Described are platforms, systems, and methods to discover relationships among equipment in automated industrial or commercial environments by looking for synchrony in state changes among the equipment. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; detect one or more events or one or more state changes in the data sources; store the detected events or state changes; detect synchrony in the detected events or state changes by performing operations comprising: identifying combinatorial pairs of data sources having an event or state change within a predetermined time window; and conducting pairwise testing for each identified combinatorial pair of data sources by applying an algorithm to the stored detected events or state changes; and determine one or more relationships for at least one identified combinatorial pair of data sources.

Automatic discovery of relationships among equipment through observation over time

Described are platforms, systems, and methods to discover relationships among equipment in automated industrial or commercial environments by cycling each individual piece of equipment while observing sensors in all other equipment in order to measure how each part reacts to each other part. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; issue one or more commands to cycle a current data source in the a plurality of data sources; monitor the automation environment for events or state changes in the data sources; detect one or more events or one or more state changes in one or more other data sources in the a plurality of data sources; and determine one or more relationships between the current data source and the one or more other data sources.

Automatic discovery of relationships among equipment through automated cycling and observation

Described are platforms, systems, and methods for mapping data found in connected equipment from a manufacturer's selected schema, format, and protocol to a normalized data model. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; retrieve data from at least one of the identified data sources; generate a plurality of data source mapping profiles, each data source mapping profile specific to a particular data source configuration; maintain a data store comprising the plurality of data source mapping profiles; select a data source mapping profile specific to the at least one identified data source configuration; and apply an algorithm to map the retrieved data to a predetermined ontology based on the selected data source mapping profile for the at least one identified data source.

Data mapping based on device profiles

Described are platforms, systems, and methods to discover subtended devices through introspection of executive or supervisory controllers. The platforms, systems, and methods maintain a plurality of introspection directives, each introspection directive comprising a sequence of instructions specific to a communications protocol, the sequence of instructions comprising instructions to send at least one command to at least one controller associated with an automation environment in accordance with the communications protocol, instructions to receive a response to the at least one command, and instructions to parse the response; identify an appropriate introspection directive for the at least one controller; and execute the sequence of instructions with respect to the at least one controller to perform operations comprising: sending at least one command to at least one controller; receiving a response; and parsing the response.

Subtended device mapping through controller introspection

Described are platforms, systems, and methods for real-time enrichment of vertices, edges, and related data within a graph database. The platforms, systems, and methods maintain a graph database comprising a representation of a current state of an automation environment comprising a plurality of data sources, wherein the data sources are represented as vertices in the graph database and relationships between the individual data sources are represented as edges in the graph database; operate a plurality of software agents, each software agent configured to perform operations comprising: applying an algorithm to identify patterns in the graph database; and generating a specific data enrichment based on one or more identified patterns; and contribute the generated data enrichment back to the graph database.

Growing community of inventors

San Diego, CA, United States of America

Jason Koh