Abstract: Technologies for monitoring input feature health for machine learning models and detecting anomalies are described. Embodiments include receiving, by a trained machine learning model and from an online system, a set of historical values and a current feature value of a time series feature, the time series feature represents one or more measured values of the online system. Embodiments include predicting an expected feature value and an expected range of values using the set of historical values. Embodiments include receiving the expected feature value and the expected range of values. Embodiments include receiving the current feature value. Embodiments include determining that an anomaly condition is present based on the current feature value, the expected feature value, and the expected range of values. Embodiments include generating an alert for the anomaly condition that includes the severity metric and the duration metric.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a graph containing nodes, edges between the nodes, and attributes of the nodes and the edges. Next, the system stores an in-memory representation of the graph in a set of columns. The system then receives a request for performing one or more computations for traversing the graph, wherein the computation(s) include iterating through subsets of the nodes and additional subsets of the edges. To process the request, the system executes the computation(s) on the stored representation of the graph to generate a near-real-time ranking of candidates for recommending to a member of an online network. Finally, the system transmits, in a response to the request, at least a portion of the near-real-time ranking as connection recommendations in the online network.

Abstract: System and method for suffix tree creation for large input data/text streams. The methodology leverages the structure of suffix trees to build a suffix tree by simultaneously tiling accesses to both the input string as well as the partially constructed suffix tree. The end result enables the indexing of very large input strings and at the same time maintain a bounded working set size and a fixed memory footprint. The method is employed for serial processing. Further, a scalable parallel suffix tree construction is realized that is suitable for implementation on parallel distributed memory systems that use effective collective communication and in-network caching. The methodology is also applied for suffix link recovery in both serial and parallel implementations.

Abstract: System and method for suffix tree creation for large input data/text streams. The methodology leverages the structure of suffix trees to build a suffix tree by simultaneously tiling accesses to both the input string as well as the partially constructed suffix tree. The end result enables the indexing of very large input strings and at the same time maintain a bounded working set size and a fixed memory footprint. The method is employed for serial processing. Further, a scalable parallel suffix tree construction is realized that is suitable for implementation on parallel distributed memory systems that use effective collective communication and in-network caching. The methodology is also applied for suffix link recovery in both serial and parallel implementations.