Facebook, Inc.

Proofpoint, Inc.

Martin Traverso

Samuel Rash

David Eric Hagar

Timothy Williamson

Charles Wade Chambers

David Andrew Phillips

Dain Sidney Sundstrom

Mark Erol Kent

Dynamically detecting abnormalities in otherwise legitimate emails containing Uniform Resource Locators (URLs) is provided. An example method includes determining one or more rules defining normal patterns in a number of sending Top-Level Domains of previously received emails received via a computer network to a user or group of users; generating a trusted trends criteria for a received email, associated with the user or the group of users, by evaluating the received email against the one or more rules; determining whether the trusted trends criteria exceeds a predetermined threshold; in response to exceeding the predetermined threshold, generating a second URL and applying it to the received email by replacing a first URL of the received email with the second URL; and redetermining the one or more rules defining normal patterns in the number of sending Top-Level Domains based on the previously received emails and the received email.

Dynamically detecting abnormalities in otherwise legitimate emails containing uniform resource locators (URLs)

A method for filtering unsolicited emails may comprise dynamically aggregating historical email data associated with a user or a group of users and dynamically determining one or more trusted trends criteria associated with the historical email data. The method may further comprise receiving a new email addressed to the user or the group of users, calculating a score associated with the new email based on the one or more trusted trends criteria, determining that the score is above a predetermined threshold score, and, based on the determination, selectively filtering the new email.

Email security architecture

Disclosed here are methods, systems, paradigms and structures for predicting queries, creating tables to store data for the predicted queries, and selecting a particular table to obtain the data from in response to a query. The methods include determining various combinations of a finite set of columns users may query on, based on (i) a list of columns users are interested in obtaining data for, and (ii) cardinality information of a column or combinations of columns in the list of columns. The methods further includes creating various tables based on the determined combinations of the columns using a meta query language. A query is responded to by selecting a table that has least number of rows, among the tables that satisfy query parameters. The methods include selecting a table that has a longest sequence of columns matching with a portion of the query parameters.

Query prediction

Disclosed are methods, systems, paradigms and structures for caching data associated with a sliding window in computer systems. A sliding window can include a time window that progresses with time, and the data can include time series data. As time progresses, the sliding window changes bringing in new data. The cache is updated with new data as and when the sliding window moves. The sliding window data is cached at various granularity levels. The method includes storing a first portion of the data at a first granularity level and a second portion at a second granularity level. The data is cached at various granularity levels in order to effectively use the cache considering at least cache updating criteria such as (i) number of times a storage unit is queried to retrieve the data for updating the cache, (ii) the day/date/time at which the storage unit is queried.

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Menlo Park, CA, United States of America

Martin Traverso