Above query partition events by the player. Here event partitions partition the events by some condition before processing it. We wrote a query for each speed range, found the time stamps of speed range changes, and calculated the final result.
You can find more information about Siddhi language from Siddhi Language Specification. Here “+” point to one or more occurrences similar to regular expressions. Siddhi invokes the callback when those conditions are met. For example, Players means velocity range in events received in player event stream. Insert into RunningStats partition by player We have implemented this usecase by using CEP event sequences to detect whenever a player crossed a threshold of event speeds using a query like following. For example, results will tell that the player “Martin” is running fast from the time 27 minutes and 01 second of the game to 27 minute and 35 second of the game. The first usecase measures each player’s running speeds and calculates how long he spent on different speed ranges. We worked with two streams: Players – events from players and BallStream – events from the ball.
That is the query matches when those conditions have matched in the given order by the incoming events. Here the comma separated conditions define an event sequence. To understand the queries, you need to know the Siddhi Event query language. I will explain each and tell bit about how we implemented them. Each event includes the location (x,y,z), time stamp, velocity and acceleration of the sensor. Both the player’s shoes and ball had sensors, and as the game continues, it generates about 15,000 events per second. This year grand challenges is to process events generated in a football game and answer four queries related to the game in streaming fashion. This year we are also competing, and following are some of our experiences while doing it.
Every year, they provide a grand challenge that challenges the participants to solve an event-based problem. ACM International Conference on Distributed Event-Based Systems (DEBS) is one of the primary venues for Complex Event processing related research.
0 kommentar(er)
