Abstract: An endless loop in an inference engine processing a set of rules according to facts stored in a database may be detected by: (a) analyzing the set of rules to identify a subset of the set of rules comprising rules that are triggered by an updated fact; (b) executing the rules of the subset; (c) updating at least one of the facts based on the execution of the rules; (d) storing an identifier for each executed rule of the subset; (e) associating each stored identifier with a subset number for the subset in a consecutive sequence of subset numbers for executed subsets; and repeating steps (a)-(e) until the identifier for a rule, to be executed, is associated with an excessive number of subset numbers that are equally offset from each other in the sequence of subset numbers for executed subsets.

Abstract: A motivational system computer platform may retrieve a current motivational rule set (e.g., a program's motivational mechanism) from a motivational rule set data store. The system may then arrange to execute the program such that inputs from a remote user device are applied to the current motivational rule set resulting in current motivational feedback that is stored into a current motivational feedback data store and transmitted to the user device. The motivational system computer platform may also retrieve a shadow motivational rule set from the motivational rule set data store. The system may then arrange to execute the program such that inputs from the user device are also applied to the shadow motivational rule set, in parallel with the current motivational rule set, resulting in shadow motivational feedback that is stored into a shadow motivational feedback data store without being transmitted to the user device.

Abstract: The present disclosure involves systems, software, and computer-implemented methods for asynchronous session storing. An example method includes receiving, by a first thread of a data processing apparatus, a backup trigger for backing up session of an application being executed by a second, separate thread of the data processing apparatus; serializing, by the first thread of the data processing apparatus, session, wherein serializing the session comprises blocking execution of the application by the second thread of the data processing apparatus; storing the serialized session into a database; and validating the stored session.

Abstract: A motivational system computer platform may retrieve a current motivational rule set (e.g., a program's motivational mechanism) from a motivational rule set data store. The system may then arrange to execute the program such that inputs from a remote user device are applied to the current motivational rule set resulting in current motivational feedback that is stored into a current motivational feedback data store and transmitted to the user device. The motivational system computer platform may also retrieve a shadow motivational rule set from the motivational rule set data store. The system may then arrange to execute the program such that inputs from the user device are also applied to the shadow motivational rule set, in parallel with the current motivational rule set, resulting in shadow motivational feedback that is stored into a shadow motivational feedback data store without being transmitted to the user device.

Abstract: The present disclosure involves systems, software, and computer implemented methods for database scaling for multi-tenant applications. One example method includes receiving an indication to migrate a tenant of an application from a default database to a scale database. The tenant is locked. At least one stateful component that includes state information for the tenant is identified. The state information is serialized. The identified stateful component(s) are shut down. Data for the tenant is copied from the default database to the scale database. The tenant is assigned to the scale database. The serialized state information is de-serialized to generate de-serialized state information, and the de-serialized state information is stored in the scale database. The stateful component(s) are restarted and the tenant is unlocked.

Abstract: The present disclosure involves systems, software, and computer implemented methods for database scaling for multi-tenant applications. One example method includes receiving an indication to migrate a tenant of an application from a default database to a scale database. The tenant is locked. At least one stateful component that includes state information for the tenant is identified. The state information is serialized. The identified stateful component(s) are shut down. Data for the tenant is copied from the default database to the scale database. The tenant is assigned to the scale database. The serialized state information is de-serialized to generate de-serialized state information, and the de-serialized state information is stored in the scale database. The stateful component(s) are restarted and the tenant is unlocked.

Abstract: The present disclosure involves systems, software, and computer-implemented methods for asynchronous session storing. An example method includes receiving, by a first thread of a data processing apparatus, a backup trigger for backing up session of an application being executed by a second, separate thread of the data processing apparatus; serializing, by the first thread of the data processing apparatus, session, wherein serializing the session comprises blocking execution of the application by the second thread of the data processing apparatus; storing the serialized session into a database; and validating the stored session.

Abstract: An endless loop in an inference engine processing a set of rules according to facts stored in a database may be detected by: (a) analyzing the set of rules to identify a subset of the set of rules comprising rules that are triggered by an updated fact (b) executing the rules of the subset; (c) updating at least one of the facts based on the execution of the rules; (d) storing an identifier for each executed rule of the subset; (e) associating each stored identifier with a subset number for the subset in a consecutive sequence of subset numbers for executed subsets; and repeating steps (a)-(e) until the identifier for a rule, to be executed, is associated with an excessive number of subset numbers that are equally offset from each other in the sequence of subset numbers for executed subsets.