Abstract: Techniques are provided to quickly process stored objects to generate an accurate estimate of the number of live objects that will be responsive to a client's filtering request defined at least in part based on a filtering parameter. Stored objects generated from user interactions with webpages are modified to simulate live objects and processed by a live object-intake simulator. The simulated results are then filtered using the filtering parameter to form a subset of matching objects. An estimate of the number of live objects produced by the filtering parameter is generated using at least the count of the objects in the subset. A confidence metric of the estimate is computed, in some cases using a statistical model. Under certain conditions, the estimate is delivered, the confidence metric is delivered, and/or live object intake based on the filtering parameter is initiated.

Abstract: Techniques are provided for accurately and quickly processing distributed stored objects to provide a timely and accurate prediction of the number of live objects a parameterized file request will produce. Stored objects representing previous user webpage visit interactions are stored in different storage locations in a data store. The stored objects at each storage location are processed in parallel by hashing stored objects with a hash function such that they are spread somewhat uniformly into buckets. Sub-buckets in each bucket are formed that correspond to selected category identifiers. Also in parallel, K-minimum values are computed for each sub-bucket to estimate the count of stored objects in the data store. The K-minimum values for sub-buckets corresponding to the same category ID across all buckets are combined, in some cases harmonically, and used to generate a predicted number of live objects responsive to a parameterized file request.

Abstract: Embodiments relate to dynamically configuring a swarm of processing bots to autonomously execute tasks corresponding to a request. A communications fabric enables broadcasts of processing and status data from individual bots to other bots, which can locally determine whether and/or how the communications are to affect its processing.

Abstract: Embodiments relate to dynamically configuring a swarm of processing bots to autonomously execute tasks corresponding to a request. A communications fabric enables broadcasts of processing and status data from individual bots to other bots, which can locally determine whether and/or how the communications are to affect its processing.

Abstract: Techniques are provided for accurately and quickly processing distributed stored objects to provide a timely and accurate prediction of the number of live objects a parameterized file request will produce. Stored objects representing previous user webpage visit interactions are stored in different storage locations in a data store. The stored objects at each storage location are processed in parallel by hashing stored objects with a hash function such that they are spread somewhat uniformly into buckets. Sub-buckets in each bucket are formed that correspond to selected category identifiers. Also in parallel, K-minimum values are computed for each sub-bucket to estimate the count of stored objects in the data store. The K-minimum values for sub-buckets corresponding to the same category ID across all buckets are combined, in some cases harmonically, and used to generate a predicted number of live objects responsive to a parameterized file request.

Abstract: Techniques are provided to quickly process stored objects to generate an accurate estimate of the number of live objects that will be responsive to a client's filtering request defined at least in part based on a filtering parameter. Stored objects generated from user interactions with webpages are modified to simulate live objects and processed by a live object-intake simulator. The simulated results are then filtered using the filtering parameter to form a subset of matching objects. An estimate of the number of live objects produced by the filtering parameter is generated using at least the count of the objects in the subset. A confidence metric of the estimate is computed, in some cases using a statistical model. Under certain conditions, the estimate is delivered, the confidence metric is delivered, and/or live object intake based on the filtering parameter is initiated.