Sun Microsystems, Inc.

Oracle International Corporation

Oracle America, Inc.

Box Limited

Other

Maplebear Inc.

David Vengerov

Garret Frederick Swart

Sesh Jalagam

Victor De Vansa Vikramaratne

Anton Rang

Ilya Gluhovsky

Andrew B Hastings

Harriet G Coverston

Mohamed Zait

Kave Eshghi

J William Lee

Dmitry Mikhailovich Potapov

Neil MacNaughton

Tejaswi Tenneti

Sunil Parrokkaren Chakkappen

Declan J Murphy

Bharat Chandra Baddepudi

Alok Ojha

Seth T Proctor

Adam Y Lee

Andrew Vakhutinsky

Vipin Gokhale

John Richard Busch

Kishore Kumar Pusukuri

Vinesh Reddy Gudla

Alexandra Fedorova

Benjamin Draffin

Douglas Neale

Kresimir Mihic

Savvas Gitzenis

Andre Menck

A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system. The user query is described by one or more query features. The system obtains one or more contextual features describing a context of the user query. The system applies a contextual bandit model to the query features and the contextual features to select a query processing model from a plurality of query processing models. The system applies the selected query processing model to the user query to obtain query results. The system transmits the query results for display on the client device.

Contextual bandit model for query processing model selection

Content object operations over content objects of a content management system are prioritized to be performed immediately, or at a later time. The immediate scheduling of an operation is determined by policies, rules, and/or predictive model outcomes. The determination for later time scheduling is based on analysis of a history of events on content objects. If the content object operation is deemed to be at least potentially delayable to a later time, then a scheduling model is consulted to determine an urgency of performing the content object operation on the content object. The urgency value resulting from consulting the scheduling model is combined with then-current resource availability to determine a timeframe for performance of the content object operation on the content object relative to other entries in a continuously updated list of to-be-performed operations. The performance of the content object operation on the content object is initiated in due course.

Prioritizing operations over content objects of a content management system

Methods, systems and computer program products for recommendation systems. Embodiments commence by gathering a set of pathnames that refer to content objects of a collaboration system. A tokenizer converts at least some of the pathnames into vectors. The vectors comprise hierarchical path components such as folder names or file names, which vectors are labeled with an indication as to whether or not the folder or file referred to in a particular vector had been clicked on by one or more users. Some portion of the labeled vectors are used to train a predictive model. Another portion of the vectors are used to validate the predictive model. When the model exhibits sufficient precision and recall, the predictive model is then used to predict the probability that a particular user would have an interest in a particular folder or file. The folder name or file name is presented as a collaboration recommendation.

Determining collaboration recommendations from file path information

Methods, systems and computer program products for computing system security. Techniques for classifying a potentially unauthorized user as an authorized user involve comparisons of two or more access request times that occur at different computing devices in different geographical locations. Based on those comparisons and the distance between the geographical locations of the different computing devices, a determination is made as to whether or not travel (e.g., via overland travel, via air travel, etc.) between those different geographical locations can be reasonably accomplished within a given time period. If it is determined that the required time for travel between the different geographical locations is greater than the time between the access request times&#x2014;thus suggesting a spoofing attack or other malfeasance&#x2014;then the potentially unauthorized (i.e., only potentially malfeasant) access can still be deemed as an authorizable access request by analyzing browser configurations and activity patterns of the potentially unauthorized user.

Avoiding user session misclassification using configuration and activity fingerprints

A system is provided that generates a list of most common items and respective item frequencies from a data set that includes a plurality of items. The system creates a first list of items from the data set and monitors an estimate count for each item in the first list, where the first list is sorted by the estimate count. The system further transfers items that exceed a threshold hit count from the first list to a second list. The system further monitors a hit count for the items in the second list, where the items in the second list are sorted by the hit count. The system further transfers the items that are displaced from the second list back to the first list.

Frequent items detection in data sets

A system performs database management. The system receives a request for a query of a plurality of tables under a join condition on an attribute and determines a uniform mapping that maps a domain of the attribute into a set of numbers, where the domain of the attribute includes all attribute values taken by the attribute in the tables. Then, for a row in a table, the system includes the row into a synopsis of the table if the row includes an attribute value that is mapped by the uniform mapping to a number less than an inclusion probability associated with the table. The system obtains a plurality of synopsis for the tables by repeating the including for all rows in the table and for all tables, and determines, based on the plurality of synopsis, an estimate join size of the tables under the join condition.

One-pass join size estimation with correlated sampling

Techniques are provided for managing cached data objects in a mixed workload environment. In an embodiment, a database system receives request to access a target data object. The database system determines whether the request to access the target data object is associated with a first type of workload or a second type of workload. In response to determining that the request is associated with the first type of workload, the target data object replaces a least recently used data object in a cache. In response to determining that the request is associated with the second type of workload, the target data object is cached based on an associated access-level value.

Caching large objects in a computer system with mixed data warehousing and online transaction processing workload

A system optimizes a number of shared server processes executing on a processor. The system creates, in a memory, a data array for storing a plurality of performance metric values, each associated with a number of shared server processes. The system selects a value for an optimized number of shared server processes according to a first procedure based on the performance metric, observes a performance metric associated with the selected optimized number, and stores, in the data array, the observed performance metric. The system repeats the selecting, observing and storing until at least a predetermined number of contiguous data values are stored in the data array. The system selects the value for the optimized number according to a second procedure based on a slope of the performance metric. The system observes the performance metric associated with the selected optimized number, and stores, in the data array, the observed performance metric.

Optimizing the number of shared processes executing in a computer system

A system for optimizing shelf space placement for a product receives decision variables and constraints, and executes a Randomized Search ('RS') using the decision variables and constraints until an RS solution is below a pre-determined improvement threshold. The system then solves a Mixed-Integer Linear Program ('MILP') problem using the decision variables and constraints, and using the RS solution as a starting point, to generate a MILP solution. The system repeats the RS executing and MILP solving as long as the MILP solution is not within a predetermined accuracy or does not exceed a predetermined time duration. The system then, based on the final MILP solution, outputs a shelf position and a number of facings for the product.

Shelf space product placement optimizer

A method of inserting cache blocks into a cache queue includes detecting a first cache miss for the cache queue, identifying a storage block receiving an access in response to the cache miss, calculating a first estimated cache miss cost for a first storage container that includes the storage block, calculating an insertion probability for the first storage container based on a mathematical formula of the first estimated cache miss cost, randomly selecting an insertion probability number from a uniform distribution, and inserting, in response to the insertion probability exceeding the insertion probability number, a new cache block corresponding to the storage block into the cache queue.

Method and system for inserting cache blocks

A method for removing cache blocks from a cache queue includes detecting a first cache miss for the cache queue, identifying, within the cache queue, a new cache block storing a value of a storage block, calculating an estimated cache miss cost for a storage container having the storage block, calculating a removal probability for the storage container based on a mathematical formula of the estimated cache miss cost, randomly selecting a probability number from a uniform distribution, where the removal probability exceeds the probability number, and evicting, in response to the removal probability exceeding the probability number, the new cache block from the cache queue.

Method and system for removing cache blocks

The disclosed embodiments provide a system that facilitates scheduling threads in a multi-threaded processor with multiple processor cores. During operation, the system executes a first thread in a processor core that is associated with a shared cache. During this execution, the system measures one or more metrics to characterize the first thread. Then, the system uses the characterization of the first thread and a characterization for a second, second thread to predict a performance impact that would occur if the second thread were to simultaneously execute in a second processor core that is also associated with the cache. If the predicted performance impact indicates that executing the second thread on the second processor core will improve performance for the multi-threaded processor, the system executes the second thread on the second processor core.

Cache-aware thread scheduling in multi-threaded systems

A method for maximizing revenue generated from a plurality of service level agreements (SLAs) that includes receiving a first subset of the plurality of SLAs for executing a first plurality of jobs, wherein each SLA in the first subset specifies a first maximum requested delay that is greater than an initial minimum offered delay, and wherein a price of each SLA in the first subset is defined by the maximum requested delay and a price/delay function, calculating a first expected revenue from executing the first subset, and optimizing a second subset of the plurality of SLAs by replacing the initial minimum offered delay on the initial price/delay function with a new minimum offered delay based on the expected revenue, wherein each SLA in the second subset specifies a second maximum requested delay that is greater than the new minimum offered delay.

Method and system for maximizing revenue generated from service level agreements

One embodiment of the present invention provides a system that allocates resources to projects in a computer system. During operation, the system determines a current demand by a project for a resource, and a current allocation of the resource to the project. The system also uses a computational model to compute an expected long-term utility of the project for the resource. Next, the system trades the resource between the project and other projects in the computer system to optimize expected long-term utilities. During this process, the system uses a reinforcement learning technique to update parameters of the computational model for the expected long-term utility of the project based on performance feedback.

Using reinforcement learning to facilitate dynamic resource allocation

Some embodiments of the present invention provide a system that executes a garbage collector in a computing system. During operation, the system obtains a throughput model for the garbage collector and estimates a set of characteristics associated with the garbage collector. Next, the system applies the characteristics to the throughput model to estimate a throughput of the garbage collector. The system then determines a level of performance for the garbage collector based on the estimated throughput. Finally, the system adjusts a tunable parameter for the garbage collector based on the level of performance to increase the throughput of the garbage collector.

Growing community of inventors

Sunnyvale, CA, United States of America

David Vengerov