Abstract: Methods and systems are presented for providing a secured electronic transaction processing framework that enables online service providers to process electronic transactions for users while allowing the users to retain control over their user data. The secured electronic transaction processing framework includes a data access system configured to dynamically access user data, that is stored on user devices and controlled by users, on an as-needed basis. When a service provider server receives a request for processing a transaction through a user account, the service provider serer may use the data access system to dynamically obtain user data required for processing the transaction from a wallet application of a user device. The wallet application may include data access policies that specify data access settings for different service providers. After processing the transaction using the user data, the service provider server may remove the user data.

Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Abstract: A device is disclosed that includes a circuit block coupled to a local power node, and a power gating circuit coupled between the local power node and a global power supply. In one embodiment, the power gating circuit includes a first plurality of first switching devices with a first threshold voltage, and a second plurality of second switching devices with a second threshold voltage that is different from the first voltage threshold. The power gating circuit may isolate the local power node from the global power supply based on an isolation signal.

Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.