## 7 Conclusions
### Future Work  
As far as we know, this work is the first to focus solely on video event data and query processing without considering tasks such as pre-processing, convolutional neural network costs, and database operations. Therefore, the proposed solution and results show the real computational cost without the burden of third-party algorithms. Future work will consider testing the NOP Query on a real-world surveillance system with many video streams, using an entire computer vision pipeline, including GPU processing. NOPL support will be added, and the results will be compared with the C++ 4.0 NOP Framework results. An interesting extension of the current work includes the development of a new \"NOP-based (re)active database\", which avoids permanent triggers. It also includes the addition of other query operators, such as the one recently published in [4; 6; 32]:  
* The _selection_ operator allows the selection of particular objects or events of interest. For example, users may search for instances of people. * The _aggregation_ operator allows computing some statistics over the video frames. Common aggregations include count, sum, average, maximum, and minimum operators. For example, a city planner may be interested in computing the average number of cars per frame or counting the number of pedestrians that cross in front of a camera. * The _limit_ operator allows finding a cardinality-limited number of events occurring within some time interval. For example, a city planner may search for ten instances of buses at stop signs. * The _similarity_ operator allows searching for portions of the video similar to an input frame or video clip. For example, a football analyst may input a frame or video clip of a goal of a soccer player. Such queries often involve iterative, ad hoc analysis to arrive at the final query. * The _join_ operator allows performing a join and, subsequently, a _selection_, _aggregation_, or _limit_ query. For example, an amber alert application may join extracted license plates with an external data source. * The _projection_ operator extracts only a part of the information to compute video events. In addition, other possible use cases for NOP Query include the domain of Complex Event Processing (CEP) systems, also known as event stream processing. This technology is used to query data before storing them in a database or, in some cases, without storing them. The main objective of CEP is to identify significant events, such as opportunities or threats, in real-time situations and quickly respond to them. More details about CEP systems can be found at [68; 71]. Examples of CEP systems include:  
* Stock market trading systems: real-time analysis of market data;
* Mobile devices: handling incoming streams of events;
* Fraud detection: identifying suspicious patterns;
* Business activity monitoring: monitoring business processes;
* Security monitoring: detecting anomalies;
* Transportation industry: real-time tracking;
* Government intelligence gathering: analyzing diverse data sources. [0.5 cm]  
**Author Contributions:** Conceptualization, C.K., J.M.S. and H.S.L. ; methodology, C.K., J.M.S. and H.S.L. ; software and experiments, C.K. ; validation, C.K. and H.S.L. ; formal analysis, C.K. and H.S.L. ; investigation, C.K. ; resources, C.K. ; data curation, C.K. ; writing--original draft preparation, C.K. ; writing--review and editing, C.K., H.S.L. and J.M.S. ; visualization, C.K. ; supervision, H.S.L. ; project administration, C.K. ; funding acquisition, H.S.L. All authors have read and agreed to the published version of the manuscript. [0.5 cm]  
**Funding:** This research received no external funding. [0.5 cm]  
**Institutional Review Board Statement:** Not applicable. [0.5 cm]  
**Informed Consent Statement:** Not applicable.