Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: Techniques for suggesting a filter field based on a user input are disclosed. A system trains a machine learning model by obtaining historical data including sets of user search input, including a first search term and a first value for a first filter field. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: Techniques for suggesting filters for query terms based on previously selected query results are disclosed. Common characteristics of previously selected query results are presented as a filter. A system trains a machine learning model by obtaining historical data including query characteristics and selected query results. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: Techniques for suggesting a filter field based on a user input are disclosed. A system trains a machine learning model by obtaining historical data including sets of user search input, including a first search term and a first value for a first filter field. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: Techniques for suggesting filters for query terms based on previously selected query results are disclosed. Common characteristics of previously selected query results are presented as a filter. A system trains a machine learning model by obtaining historical data including query characteristics and selected query results. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Abstract: A computer-implemented method for operating a fantasy sports system permits the drafting of non-active players. The non-active players can be draft alongside active players, or a team or even a league can be comprised of exclusively non-active players. Fantasy points are generated for each of the non-active players by randomly selecting a plurality of historical plays by each non-active player from a database of historical plays. A total fantasy score for each fantasy team is generated based upon the fantasy points for each of the non-active players.