Abstract: Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Abstract: Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Abstract: An online system receives data and processes the data in a data processing pipeline. To data loss in the data processing pipeline, the online system determines a time interval during which each item of data is received and associates a set of counters with each time interval. For each time interval, an input counter is incremented for each data item received during the time interval and an output counter is incremented for each data item received during the time interval that was processed by the data processing pipeline. The online system compares an input number from the input counter and an output number from the output counter for each time interval. Based on a difference between the input number and output number for a time interval, the online system determines if a loss of data received during the time interval occurred. Lost Data are identified and processed.

Abstract: An online system processes data in a distributed processing system. To detect loss and corruption of data, the online system periodically stores information describing states of data processed during various time intervals and updates the information to include changes occurring within a predetermined period. Based on states of data described by information stored at a time, the online system performs a modified process on data received or processed during a time interval. For each item of data on which the modified process was performed, the online system compares a modified state of the data item to a state described by information stored at an additional time to determine if data was lost or corrupted. Lost or corrupted data is identified and processed based on the state of data described by the information stored at the time.

Abstract: An online system receives data and processes the data in a data processing pipeline. To data loss in the data processing pipeline, the online system determines a time interval during which each item of data is received and associates a set of counters with each time interval. For each time interval, an input counter is incremented for each data item received during the time interval and an output counter is incremented for each data item received during the time interval that was processed by the data processing pipeline. The online system compares an input number from the input counter and an output number from the output counter for each time interval. Based on a difference between the input number and output number for a time interval, the online system determines if a loss of data received during the time interval occurred. Lost Data are identified and processed.

Abstract: An online system processes data in a distributed processing system. To detect loss and corruption of data, the online system periodically stores information describing states of data processed during various time intervals and updates the information to include changes occurring within a predetermined period. Based on states of data described by information stored at a time, the online system performs a modified process on data received or processed during a time interval. For each item of data on which the modified process was performed, the online system compares a modified state of the data item to a state described by information stored at an additional time to determine if data was lost or corrupted. Lost or corrupted data is identified and processed based on the state of data described by the information stored at the time.

Abstract: Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Abstract: Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Abstract: Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Abstract: To allow for tracking events and classifying assets within a social networking system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset is classified based on comparison of the first signal value and the second signal value. In an embodiment, the time series is based on at least one time window including time intervals. In an embodiment, counters to determine a number of occurrences of an event type are associated with the time intervals. In an embodiment, each of the counters are incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Abstract: To allow for tracking events and classifying assets within a social networking system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset is classified based on comparison of the first signal value and the second signal value. In an embodiment, the time series is based on at least one time window including time intervals. In an embodiment, counters to determine a number of occurrences of an event type are associated with the time intervals. In an embodiment, each of the counters are incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.