Oracle International Corporation

Jeevan Gheevarghese Joseph

Vernon W Hui

Kendra Mariko Chen

John Patrick Connelly

Anping Wang

Matthew Perry Abrams

Adison Hana Wongkar

Hyunjung Lee

Sanjeev Khodaskar

Padmanabhan Natarajan

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.

Object count estimation by live object simulation

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.

Object count prediction using distributed processing

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.

Fuzzy management of high-volume concurrent processes

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Dublin, CA, United States of America

Jeevan Gheevarghese Joseph