Abstract: A computer-implemented method includes receiving one or more log files. Each of the one or more log files includes one or more logs. The computer-implemented method further includes extracting one or more event records from said one or more logs. The computer-implemented method further includes, for each event record of the one or more event records, determining one or more attributes and one or more dimensions based on the event record, respectively. The computer-implemented method further includes grouping the one or more event records into one or more attribute groups. The computer-implemented method further includes ordering the one or more event records of each of the one or more attribute groups by the one or more dimensions. The computer-implemented method further includes generating one or more graphical representations of the one or more attribute groups. A corresponding computer system and computer program product are also disclosed.

Abstract: A first plurality of traffic data transmitted by a first client device associated with a first vehicle may be received. The received first plurality of traffic data may be stored in a database according to a license plate of the first vehicle. A second plurality of traffic data transmitted by a second client device associated with a second vehicle may be received. The license plate of the first vehicle may be identified within the received second plurality of traffic data using at least one image recognition technique. The identified license plate may be determined to be stored in the database. The first plurality of traffic data may be transmitted to the second client device based on determining the identified license plate is stored in the database. The transmitted first plurality of traffic data may be displayed within a user interface associated with the second client device.

Abstract: A computer-implemented method includes receiving one or more log files. Each of the one or more log files includes one or more logs. The computer-implemented method further includes extracting one or more event records from said one or more logs. The computer-implemented method further includes, for each event record of the one or more event records, determining one or more attributes and one or more dimensions based on the event record, respectively. The computer-implemented method further includes grouping the one or more event records into one or more attribute groups. The computer-implemented method further includes ordering the one or more event records of each of the one or more attribute groups by the one or more dimensions. The computer-implemented method further includes generating one or more graphical representations of the one or more attribute groups. A corresponding computer system and computer program product are also disclosed.

Abstract: Embodiments of the present invention disclose populating a database by conducting a training phase to analyze training security log streams. A server extracts the words contained within the training security log and replaces each of the extracted words with a symbol to form a symbol string and an n-gram scan is conducted on the symbol string to generate statistical data. Tokens are extracted from symbol string, where the tokens are words, phrase or alphanumeric sequence represented by a symbol within training security log stream. The symbol string is replaced with the words they represent to allow for the identification of the actual token phrases. A scan is conducted on the token phrase to acquire scan data on the identified token phrases. Once the tokens, position information, and scan data are known they are stored within the database to allow security log streams to be analyzed in real time.

Abstract: A computer-implemented method includes receiving one or more log files. Each of the one or more log files includes one or more logs. The computer-implemented method further includes extracting one or more event records from said one or more logs. The computer-implemented method further includes, for each event record of the one or more event records, determining one or more attributes and one or more dimensions based on the event record, respectively. The computer-implemented method further includes grouping the one or more event records into one or more attribute groups. The computer-implemented method further includes ordering the one or more event records of each of the one or more attribute groups by the one or more dimensions. The computer-implemented method further includes generating one or more graphical representations of the one or more attribute groups. A corresponding computer system and computer program product are also disclosed.

Abstract: A computer-implemented method includes receiving one or more log files. Each of the one or more log files includes one or more logs. The computer-implemented method further includes extracting one or more event records from said one or more logs. The computer-implemented method further includes, for each event record of the one or more event records, determining one or more attributes and one or more dimensions based on the event record, respectively. The computer-implemented method further includes grouping the one or more event records into one or more attribute groups. The computer-implemented method further includes ordering the one or more event records of each of the one or more attribute groups by the one or more dimensions. The computer-implemented method further includes generating one or more graphical representations of the one or more attribute groups. A corresponding computer system and computer program product are also disclosed.

Abstract: The method includes identifying a log stream from a first device, wherein the first device is of an unknown device type. The method further includes identifying features in the log stream from the first device. The method further includes determining a probability that the log stream from the first device was created by a specific device type based on matching of features of the identified log stream from the first device and features in log streams from a plurality of known device types.

Abstract: The method includes identifying a log stream from a first device, wherein the first device is of an unknown device type. The method further includes identifying features in the log stream from the first device. The method further includes determining a probability that the log stream from the first device was created by a specific device type based on matching of features of the identified log stream from the first device and features in log streams from a plurality of known device types.