# Real-Time Stream Processing in Social Networks with RAM\({}^{3}\)S
## 2 Motivation  
In this section, we provide a number of examples of use of social network services demanding processing of multimedia stream data with low latency requirements. **Example 1** (Job Suggestion). : _ConnectedTo is a service for business and employment that operates through a website and a mobile app. Its main use is professional networking, allowing employers to publish job proposals and job seekers to post their CVs._  
_[PERSON] recently joined ConnectedTo to find a new job: She posts her CV in order to find a selections of job proposals fitting her working experience, training, and skills. The system should match [PERSON]'s CV with the available jobs to present her with only the ones she could be interested in: The filtering (and possibly sorting) of job proposals should be performed in near-real-time, in order to allow [PERSON] to review the requested profile and, for example, discover which of her professional contacts can introduce her to the hiring company._  
_On the other side, [PERSON] is an HR manager at JCN, an IT company based in NY, and a long-term user of ConnectedTo: He wants to publish the proposal for a new job at the JCN R&D department. The system should efficiently filter out candidates who do not fit the profile requested by the company and point out the best candidates for the position with very low latency._  
**Example 2** (Dangerous Events Detection). : _The Christchurch Call to Action ([[https://www.christchurchcall.com/](https://www.christchurchcall.com/)]([https://www.christchurchcall.com/](https://www.christchurchcall.com/))) is a political agreement among 48 countries, initiated by New Zealand Prime Minister [PERSON], fostered by the Christchurch mosque shootings of 15 March 2019. World leaders and technology companies pledged to \"eliminate terrorist and violent extremist content online\"._  
_Being one of the Internet companies that signed the original pledge, the MePipe video sharing service includes, in its Terms of Service Agreement, the following statement:_\"Our service includes automated systems to detect and remove abusive and dangerous activity that could hurt the community at large.\" This clearly requires analysis every single video posted by any MePipe user to discover its appropriateness. For this, [PERSON] (an IT engineer at MePipe with specialization in machine learning) has developed a deep-learning-based software that can classify any video in real time on a single machine. However, every minute there are over 500 h of video uploaded to MePipe, which makes the centralized approach followed by [PERSON] clearly impracticable._  
**Example 3** (Clothing Recommendation). : _The app 19-Fasteners allows users to share images of clothes, shoes, garments, etc., and to receive buying suggestions related to the posted images. [PERSON], a young girl who recently subscribed to 19-Fasteners, is strolling through the streets of Milan, when she spots a wonderful pair of shoes in a shop in the Via Montenapoleone fashion district; however, the price largely exceeds her budget._  
_Luckily, the 19-Fasteners service allows users to upload a photo of garments and to receive offers of similar items from clothing sellers. [PERSON] immediately takes a picture of the shoes and posts the image on the app, specifying her requested price range; in only a few moments, 19-Fasteners returns to [PERSON] a list of items satisfying her budget, in decreasing order of similarity with respect to the uploaded photo._  
_Each server is able to process [PERSON]'s request in 500 milliseconds, however managers at 19-Fasteners foresee that, in the near future, the site activity will be about 1K requests/second. [PERSON], responsible for IT services at 19-Fasteners is now requested to estimate the number of new computers that should be installed at the 19-Fasteners server factory._  
In all the above examples, the input data can be represented as a stream of MM objects: Text (CVs/job proposals), videos (the MePipe casts), and images (clothing pictures).