Boston University 2020 20 Location-based services Vasiliki Kalavri | Boston University 2020 21 Online recommendations Vasiliki Kalavri | Boston University 2020 Sensor measurements analysis • Monitoring 23 Vasiliki Kalavri | Boston University 2020 Financial transaction analysis • Fraud detection, online risk calculation Example: Someone steals your phone and sings in your banking app. The app allows data (e.g. user profile data) Examples • online A/B testing • trending topics • sentiment analysis, e.g., reaction to just published campaign • online recommendations of products, articles, people

University 2020 • What if you were in a plane and not on a train? • What if you never came back online? • How long do we have to wait before we decide that we have seen all events? How do we know

Failure handling: application needs to be aware of message loss, producers and consumers always online 5 Message queues • Asynchronous point-to-point communication • Lightweight buffer for temporary

update an underlying graph structure 5 Edge events: A purchase, a movie rating, a like on an online post, a bitcoin transaction, a packet routed from a source to destination Vertex events: A new

data cleaning and standardization 6 Vasiliki Kalavri | Boston University 2020 1. Process events online without storing them 2. Support a high-level language (e.g. StreamSQL) 3. Handle missing, out-of-order

maybe? 40 Vasiliki Kalavri | Boston University 2020 Example: Non-blocking AVG UDA AGGREGATE online_avg(Next Int): Real { TABLE state(tsum Int, cnt Int); INITIALIZE: { INSERT

accuracy for a specific query, then more tuples can be sampled to provide for more accuracy, in an online fashion. • It is a general-purpose synopsis and can be used to answer a wide variety of arbitrary