Abstract: The present system pertains to event tracking, involving the acquisition of event information, generation of compound hashes, and formation of a search data structure. Event information includes time-and-geolocation data and topic data for each event. Compound hashes, comprising a spacetime hash and a topic hash for each event, are generated based on this information. A search data structure is formed using these compound hashes, enabling the grouping of spacetime hashes and structuring of topic hashes. Upon receiving a query, the system executes a search based on the query's compound hash or parameters, returning matching event content. The system can also generate semantic descriptions of relevant events using a narrative generation model and predict event relevance using a preferential learning model.

Abstract: The present system pertains to event tracking, involving the acquisition of event information, generation of compound hashes, and formation of a search data structure. Event information includes time-and-geolocation data and topic data for each event. Compound hashes, comprising a spacetime hash and a topic hash for each event, are generated based on this information. A search data structure is formed using these compound hashes, enabling the grouping of spacetime hashes and structuring of topic hashes. Upon receiving a query, the system executes a search based on the query's compound hash or parameters, returning matching event content. The system can also generate semantic descriptions of relevant events using a narrative generation model and predict event relevance using a preferential learning model.

Abstract: The present system pertains to event tracking, involving the acquisition of event information, generation of compound hashes, and formation of a search data structure. Event information includes time-and-geolocation data and topic data for each event. Compound hashes, comprising a spacetime hash and a topic hash for each event, are generated based on this information. A search data structure is formed using these compound hashes, enabling the grouping of spacetime hashes and structuring of topic hashes. Upon receiving a query, the system executes a search based on the query's compound hash or parameters, returning matching event content. The system can also generate semantic descriptions of relevant events using a narrative generation model and predict event relevance using a preferential learning model.

Abstract: In an embodiment, a method comprises, by a networking data analysis system: receiving, from a client device, computer files, each computer file comprising log data; transforming each of the computer files, the transforming comprising: converting the log data associated with the computer files into a structured tabular representation of the log data; parsing the structured tabular representation of the log data to extract data elements associated with the log data and to identify data types associated with the data elements; creating a columnar representation of the log data based on the identified data types; storing the columnar representation of the log data in a columnar database; executing computer-implemented functions on the columnar representation of the log data to identify key networking security metrics; sending, to the client device, instructions for displaying the identified key networking security metrics associated with the log data of the one or more computer files.

Abstract: In an embodiment, a method comprises, by a networking data analysis system: receiving, from a client device, computer files, each computer file comprising log data; transforming each of the computer files, the transforming comprising: converting the log data associated with the computer files into a structured tabular representation of the log data; parsing the structured tabular representation of the log data to extract data elements associated with the log data and to identify data types associated with the data elements; creating a columnar representation of the log data based on the identified data types; storing the columnar representation of the log data in a columnar database; executing computer-implemented functions on the columnar representation of the log data to identify key networking security metrics; sending, to the client device, instructions for displaying the identified key networking security metrics associated with the log data of the one or more computer files.