Oracle International Corporation

Zahra Khatami

Avneesh Pant

Namrata Jampani

Described is an improved approach to implement an offline learning approach for machine learning that employs a window-based technique for predicting values within the window, and where outliers are identified and discarded from consideration. This approach efficiently permits offline learning to be employed in a manner that minimizes false positives, while also improving the quality of the data should retaining be required.

Method and system for implementing system monitoring and performance prediction

According to one or more embodiments, lookup, insertion, and deletion operations are allowed to continue during actions required for collision remediation. When relocation operations are used to resolve a collision, information encoded in header portions of the hash table entries that store the key-value pairs indicates when the associated key-value pairs are undergoing relocation. This information facilitates continued access to the RKVS during the relocation process by allowing other processes that access the RKVS to handle relocations without failure. Furthermore, when hash table expansion is needed in order to resolve a collision, a second, larger, hash table is allocated, and lookup operations continue on both the old hash table and the new hash table. One or more embodiments further prevent insertion, lookup, and deletion failures in the RKVS using flags, encoded in header information in hash table entries, that reflect the state of the respective key-value pairs in the store.

Growing community of inventors

Redwood Shores, CA, United States of America

Zahra Khatami