## 4 Proposed Approach
### Priority Computing Unit
#### 4.2.1 Event Detector Module  
Event detection from _Twitter_ streaming data has been an important topic in academic research, resulting in different techniques with different purposes [47; 65]. Based on several contributions in the literature, the most appropriate approach for this work was selected. Event detectors can be divided into two major groups, as previously described: _unspecified_ and _specified_ event detection [47]. Referring to these groups, a technique is defined as _unspecified_ if no prior information about the event is available. Unspecified techniques monitor the streaming data, detecting bursts on a set of keywords and grouping documents (posts and _tweets_) with similar patterns. On the other hand, a _specified_ event detector technique is defined if a query is built from some known event information (e.g., time, location, and description). Generally, specified event detection is applied for retrospective event detection (e.g., [35; 38; 39]) and monitoring scheduled events (e.g., [66]). In a different way, unspecified event detection is applied for unplanned (also referred to as real-time or on-line) event detection (e.g., [37; 56; 59]). Generally speaking, both approaches have advantages and drawbacks. However, whatever are the characteristics of the employed approach, methods for event detection inevitably involve machine or statistical learning concepts [47]. Moreover, learning tasks are also divided into _supervised_ and _unsupervised_ learning approaches. While _supervised_ learning requires tries to predicts the results within a continuous output, _unsupervised_ learning produces an output by clustering the data based on relationships among its variables (words, in our case) [47]. In this context, supervised approaches are usually applied for specified event detection [35; 39], while unsupervised approaches are employed in unspecified event detection [56; 59]. However, hybrid approaches can be employed for detecting on-line events which responds to a pre-defined criteria [55]. A Smart City can be assumed as a universe of uncertain parallel events. In such way, due to the amount of variables involved on those events, the monitoring costs involved and the unspecified nature of the events, an unsupervised event detector is the most appropriate choice to the problem of this work. Moreover, the real-time nature of the proposed adaptive sensing relevance approach requires a real-time event detector and, in order to properly reach the sensors and deliver the priority configuration, the event detector should be capable to indicate the region in which the event is happening (e.g., a minimum bounding rectangle involving the affected region). This information is essential to associate sensors to the computed priority. In Table 1, unspecified real-time event detection approaches are compared, focusing on the previously discussed requirements: \"Proposal\" refers to the application main goal, while \"Structural Content\" details the provided information about a detected event. The applications in Table 1 are divided into two major groups: _global_ and _local_ event detection approaches. Global event detectors aims to recognize unusual behaviors from the entire _tweet_ stream (e.g., world premiere of a movie), while local event detectors intent to recognize events happening in small geographic regions (e.g., a city festival). Thus, a global detection method applied over a stream of geotagged _tweets_ tends to miss many local events [56]. As mentioned above, this article requires an high precision event detector, capable to detect events originated in small areas (e.g., city park or any neighborhood). Consequently, global event detectors do not meet our requirements. Analyzing the presented approaches in Table 1, there is some information that is relevant to this work. First, despite Jasmine's good ability to detect events, its main goal is to allocate geotags for non-geotagged _tweets_[60].