Abstract: Various embodiments of a distributed numeric sequence generation system and method are described. In particular, some embodiments provide high-scale, high-availability, low-cost and low-maintenance numeric sequence generation in a non-Relational Database Management System (“non-RBMS”) system by sacrificing monotonicity. The distributed numeric sequence generation system comprises a plurality of hosts, wherein individual hosts implement a cache for caching a plurality of numeric sequences. A host can access master numeric sequence data at a separate system to obtain values for numeric sequences to store in its cache. A host can receive a request from a client for values of a numeric sequence, and provide to the client the values for the numeric sequence from its cache. Some embodiments of the distributed numeric sequence generation system and method are also equipped to vend recyclable and bounded numeric sequences.

Abstract: Various embodiments of a distributed numeric sequence generation system and method are described. In particular, some embodiments provide high-scale, high-availability, low-cost and low-maintenance numeric sequence generation. The distributed numeric sequence generation system comprises one or more hosts, wherein individual hosts implement a cache for caching a plurality of numeric sequences. The hosts receive a maximum gap size limit for a numeric sequence, in some embodiments, and determine a total cache size of the cache associated with the one or more hosts to store the values of the numeric sequence, such that if the values in the cache were lost then the maximum gap size limit would not be exceeded. The hosts limit the number of values of the numeric sequence in the cache associated with the one or more hosts to the determined total cache size for the values of the numeric sequence, in some embodiments.

Abstract: A device may receive data from a user. The device may automatically evaluate the data to generate a set of recommendations, where each of the recommendations relates to a recommended action to be taken by the user. The device may select a recommendation from the set of recommendations based on a set of decision criteria. The set of decision criteria may relate to a feasibility of implementing the recommendation. The device may identify a plurality of users to implement the recommendation selected from the set of recommendations. The device may provide, to each respective user of the plurality of users, respective information identifying a respective portion of the recommendation that is to be implemented by the respective user.

Abstract: A device may receive data from a user. The device may automatically evaluate the data to identify a set of attributes that are associated with classifying one or more data entries as being associated with a threshold risk. The device may automatically evaluate the data to generate, based on an attribute, of the set of attributes, associated with a data entry, of the one or more data entries, a recommendation associated with reducing a risk associated with the data entry, such that the risk does not exceed the threshold risk. The device may provide, to the user, information identifying the recommendation.