A computer-implemented method, computer program product, and systems for event detection. The computer system for event detection includes an interface component configured to receive data entries from a social media data storage wherein the data entries have associated time values and location values. The received data entries are stored in a data storage component. A cluster creator of a clustering component can create a cluster with cluster data entries wherein the cluster data entries are received data entries having time values within a range of a time interval and having location values within a range of a location interval. A cluster evaluator can then determine a cluster value for the cluster by computing an event-specific cluster feature vector as input to a machine learning algorithm wherein the machine learning algorithm calculates the cluster value. If the cluster value exceeds an event detection threshold value an event is detected.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system for social event detection, comprising: A computer system having: an interface component configured to receive data entries from a social media data storage wherein the data entries have associated time values and location values; a data storage component configured to store the received data entries; a clustering component having: a cluster creator configured to create a cluster with cluster data entries wherein the cluster data entries are received data entries having time values within a range of a time interval and having location values within a range of a location interval; a cluster evaluator configured to: determine a cluster value for the cluster by computing an event-specific cluster feature vector as input to a machine learning algorithm wherein the machine learning algorithm calculates the cluster value; and to detect an event if the cluster value exceeds an event detection threshold value. 2. The computer system of claim 1 wherein the cluster evaluator comprises: a decision data structure configured to store decision rules for the machine learning algorithm; and a machine learning classifier configured to apply the decision rules to the event-specific cluster feature vector. 3. The computer system of claim 1, wherein the clustering component further has a cluster updater configured to add a further cluster data entry to the cluster wherein the further cluster data entry corresponds to a data entry received from the social media data storage after the creation of the cluster and has a location value within the range of the location interval. 4. The computer system of claim 3, wherein the cluster updater is further configured to: merge the cluster with a further cluster if the further cluster has a temporal and/or a spatial overlap with the cluster and the overlap exceeds a predefined merging threshold. 5. The computer system of claim 1, wherein the clustering component further has a cluster finalizer configured to prepare the cluster for visualization if the cluster value exceeds the event detection threshold value. 6. The computer system of claim 1, wherein the event-specific cluster feature vector includes a textual feature related to content portions of the cluster data entries and/or a quantity feature related to a number associated with the cluster data entries. 7. The computer system of claim 6 wherein the quantity feature is associated with the number of different users associated with the cluster data entries. 8. The computer system of claim 1 further comprising: a queue data structure configured to buffer the received data entries in a memory portion of the computer system; and a data entry processor configured to adjust a format of the queued data entries in compliance with format constraints of a database which is stored in the data storage component. 9. The computer system of any one of the previous claims wherein the data storage component comprises a NoSQL database. 10. The computer system of claim 1 further comprising: a data visualization component configured to generate a visual output representing the detected event on an output device 11. A computer implemented method for event detection comprising: using a computer system configured to: receive data entries from a social media data storage wherein the data entries have associated time values, location values, and content portions; persist the received data entries during a persistence interval; create a cluster with cluster data entries wherein the cluster includes the received data entries having time values within a range of a time interval and having location values within a range of a location interval; determine a cluster value for the cluster by computing an event-specific cluster feature vector as input to a machine learning algorithm wherein the machine learning algorithm calculates the cluster value; and detect an event if the cluster value exceeds an event detection threshold value. 12. The computer implemented method of claim 11, wherein the computer system is further configured to: add an additional cluster data entry to the cluster wherein the additional cluster data entry corresponds to a data entry received from the social media data storage after the creation of the cluster and has a location value within the range of the location interval. 13. The computer implemented method of claim 12, wherein the computer system is further configured to: merge the cluster with a further cluster if the further cluster has a temporal or a spatial overlap with the cluster and the overlap exceeds a predefined merging threshold. 14. The computer implemented method of claim 13, wherein the computer system is further configured to: buffer the received data entries in a memory portion; and adjust a format of the queued data entries in compliance with format constraints of a database which is stored in the data storage component. 15. The computer implemented method of claim 14, wherein the computer system is further configured to generate for a user a visual output representing the detected event. 16. A computer program product that when loaded into a memory of a computing device and executed by at least one processor of the computing device causes the computing device to detect an event by performing a method comprising: receiving data entries from a social media data storage wherein the data entries have associated time values, location values, and content portions; persisting the received data entries during a persistence interval; creating a cluster with cluster data entries wherein the cluster includes the received data entries having time values within a range of a time interval and having location values within a range of a location interval; determining a cluster value for the cluster by computing an event-specific cluster feature vector as input to a machine learning algorithm wherein the machine learning algorithm calculates the cluster value; and detecting an event if the cluster value exceeds an event detection threshold value. 17. The computer program product of claim 16, further comprising adding an additional cluster data entry to the cluster wherein the additional cluster data entry corresponds to a data entry received from the social media data storage after the creation of the cluster and has a location value within the range of the location interval. 18. The computer program product of claim 17, further comprising merging the cluster with a further cluster if the further cluster has a temporal or a spatial overlap with the cluster and the overlap exceeds a predefined merging threshold. 19. The computer program product of claim 18, further comprising: buffering the received data entries in a memory portion; and adjusting a format of the queued data entries in compliance with format constraints of a database which is stored in the data storage component. 20. The computer program product of claim 19, further comprising generating for a user a visual output representing the detected event.