Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

Abstract: A method includes obtaining data associated with operation of a monitored system. The method also includes using one or more first machine learning models to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using one or more second machine learning models to classify each of at least some of the identified anomalies into one of multiple classifications. Different ones of the classifications are associated with different types of cyberthreats to the monitored system, and the identified anomalies are classified based on risk scores determined using the one or more second machine learning models. In addition, the method includes identifying, for each of at least some of the anomalies, one or more actions to be performed in order to counteract the cyberthreat associated with the anomaly.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

Abstract: Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

Abstract: Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Abstract: Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.