Abstract: An electronic device includes a flexible screen, a support mechanism, and a screen holder. The flexible screen includes a deformation area. The support mechanism includes a door plate corresponding to the deformation area, and the door plate is provided with an avoidance slot facing the flexible screen. In an unfolded state, a holding part is at least partially filled between a holding plate and the door plate corresponding to a main shaft module in the avoidance slot. In a folded state, the holding part slides out of the avoidance slot, so that the deformation area of the flexible screen in the folded state can enter the avoidance slot. In the folded state, the holding part slides out of the avoidance slot to increase available space in the avoidance slot, so that the deformation area of the flexible screen enters the avoidance slot.

Abstract: A computer system monitors for a change in a first data output generated by execution of a predefined dataflow. In accordance with a determination that the first data output has changed and the first data output meets triggering criteria, the computer system triggers execution of a predefined second dataflow distinct from the first dataflow. The execution of the second dataflow is dependent on the change in the first data output generated by the first dataflow. In accordance with a determination that the first data output has changed and the first data output does not meet triggering criteria, the computer system forgoes triggering execution of the second data flow. In accordance with a determination that the first data output has not changed, the computer system forgoes triggering execution of the second dataflow.

Abstract: Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule. User/source datasets may become out of sync and one or both environments may operate on stale data. One way to resolve this problem is to define triggers that, for example, monitor for changes to datasets (or other items of interest) by analytics or other activity and automatically run dataflows, recipes, or the like that are related to the changed datasets (or other items of interest).

Abstract: Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule, User / source datasets may become out of sync and one or both environments may operate on stale data.

Abstract: Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule. User/source datasets may become out of sync and one or both environments may operate on stale data. One way to resolve this problem is to define triggers that, for example, monitor for changes to datasets (or other items of interest) by analytics or other activity and automatically run dataflows, recipes, or the like that are related to the changed datasets (or other items of interest).

Abstract: Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule. User/source datasets may become out of sync and one or both environments may operate on stale data. One way to resolve this problem is to define triggers that, for example, monitor for changes to datasets (or other items of interest) by analytics or other activity and automatically run dataflows, recipes, or the like that are related to the changed datasets (or other items of interest).

Abstract: Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule. User/source datasets may become out of sync and one or both environments may operate on stale data. One way to resolve this problem is to define triggers that, for example, monitor for changes to datasets (or other items of interest) by analytics or other activity and automatically run dataflows, recipes, or the like that are related to the changed datasets (or other items of interest).