Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems, devices, and methods are provided for capturing and outputting data regarding a bodily characteristic. In one embodiment, a hardware device can operate as a stethoscope with sensors to detect bodily characteristics such as heart sounds, lung sounds, abdominal sounds, and other bodily sounds and other characteristics such as temperature and ultrasound. The stethoscope can be configured to work independently with built solid state memory or SIM card. The stethoscope can be configured to pair via a wireless communication protocol with one or more electronic devices, and upon pairing with the electronic device(s), can be registered in a network resident in the cloud and can thereby create a network of users of like stethoscopes.

Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.

Abstract: A distributed replicated database system having a plurality of federated database systems and methods of updating and reading database records from the distributed replicated database system are disclosed. Each federated database system contains a complete copy of a database. Moreover, each federated database system comprises at least one server divided into at least one logical partition. A logical partition contains records of the database and all logical partitions in a federated database system cumulatively stores all records in the database. A data structure is maintained which indicates whether the records in a given logical partition are up-to-date. When an update or insert request is received, the data structure is modified to indicate that all logical partitions storing a copy of the record to be updated, or partitions into which the new record is to be inserted, are not up-to-date.

Abstract: A distributed replicated database system having a plurality of federated database systems and methods of updating and reading database records from the distributed replicated database system are disclosed. Each federated database system contains a complete copy of a database. Moreover, each federated database system comprises at least one server divided into at least one logical partition. A logical partition contains records of the database and all logical partitions in a federated database system cumulatively stores all records in the database. A data structure is maintained which indicates whether the records in a given logical partition are up-to-date. When an update or insert request is received, the data structure is modified to indicate that all logical partitions storing a copy of the record to be updated, or partitions into which the new record is to be inserted, are not up-to-date.