## 4 Proposed Approach
### Priority Computing Unit
#### 4.2.1 Event Detector Module
Based on the _tweets_ location (latitude and longitude), Jasmine extracts the entity which represents where the event is happening (e.g., a concert at _House of Blues_). By restricting the event geographical information to one representative place, Jasmine does not consider the region impacted by the event. In EvenTweet [59] and GeoBurst [56], the location is represented by the spatial distribution of _tweets_ related to events. The EvenTweet strategy to detect events includes a continuous analysis of the most recent _tweets_ within a time-based sliding window [59], enabling the tracking of events over time. These time windows have a fixed length and the event detection is triggered only when the current window is saturated, compromising the real-time event detection [56]. Moreover, to detect a keyword burst, EvenTweet clusters the keywords only according to their spatial distribution, resulting in irrelevant \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Proposal** & **Structural Content** \\ \hline EvenTweet [59] & Local event detection & Location; Start time; Keywords \\ GeoBurst [56] & Local event detection & Representative _tweet_; Related _tweets_ \\ Jasmine [60] & Local event detection & Location, Start time, Keywords \\ TEDAS [55] & Global event detection & Event score; Location; Start time; Related _tweets_ \\ Twevent [67] & Global event detection & Keywords \\ TwitterNews [68] & Global Event detection & Representative _tweet_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some unspecified _Twitter_ on-line event detection applications. keywords in the cluster. Due to this strategy, that system is not capable to detect two distinct events happening simultaneously in the same location [56]. Finally, in a different way from EvenTweet, GeoBurst uses spatio-textual clusters to group incoming _tweets_, organizing them into geographically close and semantically coherent clusters. This strategy generates candidate events which are further ranked according to spatio-temporal burstiness, eliminating fake events (e.g., routine activities) [56]. In addition, GeoBurst has an update module which enables real-time and continuous event detection when the time window shifts [56]. Considering its effectiveness in front of other applications, this work exploits the same strategy of GeoBurst [56] to implement the Event Detector module. In a general form, GeoBurst is an effective method for performing a real-time local event detection in localized tweet streams. A local event is an unusual activity triggered in a delimited area and restricted number of participants in a specific location (e.g., protests, fires, and soccer matches) [56; 59]. An event naturally boosts the production of geo-tagged tweets around the event epicenter (e.g., fans at the stadium posting tweets about the soccer match), these tweets are geographically close (near to the stadium) and semantically coherent (they all give an information about the soccer match), forming a \"geo-topic cluster\" and characterizing a potential event [56]. A geo-topic cluster is not necessarily a local event because it may represent a routine activity (e.g., art-related tweets sent from a museum) or an occurrence of a global event (e.g., the premiere of a TV show may spread lots of geo-topic clusters throughout the city). Thus, in order to identify the local events and rank them, the spatiotemporal burstiness of each geo-topic cluster is measured [56]. The first step of GeoBurst is to gather all geo-topic clusters as candidate events in a given query window. To achieve this, the \"geo-topic authority\" of each tweet is obtained by combining the geographical and semantic contributions from similar tweets. In short, the geographical contribution is measured by using the Epanechnikov kernel function, while the semantic contribution is calculated using random walk on a keyword co-occurrence graph [56]. Afterwards, an authority ascent process is executed in order to find all high authority tweets (pivots).