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Streamsets, Apache Apex, and When-To-Post on Social Networks

December 2, 2015 6:00 PM

Logging infrastructure for Microservices using StreamSets Data Collector

Virag Kothari Streamsets

Containerized services present a unique set of challenges for log shipping usecases; In this talk we’ll look at how you can use the opensource StreamSets Data Collector to optimize the log shipping usecase within such environments.

Introducting Apache Apex (Incubating)

Thomas Weise DataTorrent

DataTorrent lead architects- Pramod and Thomas will present and introduce you to Project Apex, the industry’s only enterprise grade, fault tolerant batch and stream processing engine.

In this talk you will learn:

  • Batch and Streaming in a unified architecture- SAY WHAT
  • Show you how DataTorrent and APEX drive ease of use, ease of operability and ease of management
  • Show you the benefits of using a truly enterprise grade platform and reduce time to business insight
  • You don’t have to rewrite or redo your existing code or operational processes and be up and running with new applications in hours and days. NOT weeks and months. YOU GOT TO BE KIDDING
  • See a quick hands on ‘how to build your first application using Apex’

When-To-Post on Social Networks

Zhisheng Li & Prantik Bhattacharyya Lithium

For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an underexplored topic of research.

In this talk, we will formulate a when-to-post problem, where the objective is to find the best times for a user to post on social networks in order to maximize the probability of audience responses. To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook, over a billion posted messages and observed reactions.