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This is the schedule for INFO319 in the autumn of 2019. The dates should be fixed at this stage, but the themes may change a little.
== Tentative themes for each session ==


== Dates and Tentative Themes ==
* Thursday August 18th: Introduction meeting
*Monday 2020-08-18 1415: [[Information meeting via Zoom]]
* Thursday September 1st: Session 1 - Introduction to big data. Big-data processing. Spark
* Tuesday 2020-08-20 1015: [[Session 1 - Introduction to INFO319 and Emergency Management]]
* Thursday September 15th: Session 2 - Big data architecture. News Angler/News Hunter. Kafka
* Tuesday 2020-08-20 1400: [[Practical session, trying emergency response tool (Ushahidi)]]
* Thursday September 29th: Session 3 - Cloud. OpenStack
* Wednesday 2020-08-21 1015: [[Session 2 - Big Data for emergency management]]
* Thursday October 13th: Session 4 - Virtual instances. Docker
* Wednesday 2020-08-21 1400: [[Practical session, introduction to Spark]],
* Thursday October 27th:Session 5 - Big data storage. Cassandra
* Friday 2020-08-28 14:00 [[Essay | Essay selection deadline]] (email to [mailto:vimala.nunavath@usn.no vimala.nunavath@usn.no])
* Thursday November 10th: Session 6 - Privacy. GDPR
* Thursday 2020-10-01 1015: [[Session 3 - Emergency data sources]]
* Thursday November 24th: Session 7 - Essay presentations
* Thursday 2020-10-01 1400: [[Practical session, using Spark for emergency datasources]]
* Thursday December 8th: Session 8 - Project demonstrations
* Friday 2020-10-02 1015: [[Session 4 - Sensors/IoT for Emergency Management]]
* Friday 2020-10-09 1400:[[Programming project | Project selection deadline]] (email to [mailto:vimala.nunavath@usn.no vimala.nunavath@usn.no])
* Thursday 2020-10-29 1015: [[Session 5 - Social Media for Emergency Management]]
* Thursday 2020-10-29 1400: [[Practical session, Spark streaming for Twitter data analysis]]
* Friday 2020-10-30 1015: [[Session 6 - Machine learning and NLP for EM ]]
* Friday 2020-10-30 1400: [[Practical session, Spark streaming for sentiment analysis]]
* Friday 2020-11-20 1400: [[Essay | Essay submission deadline]] (submit PDF-file through Inspera)
* Thursday 2020-11-26 1015: [[Session 7 - Essay presentations (mandatory)]]
* Wednesday 2020-11-25 1400: [[Programming project | Project deadline]] (submit ZIP-file through Inspera)
* Friday 2020-11-27 1015: [[Session 8 - Student programming project presentations (mandatory)]]
* Tuesday 2020-12.01: Written exam


== Session 1 - Introduction to big data. Big-data processing. Spark ==
* Kitchin, chapter 4-5
* Section 1 in Opdahl, A. L., & Nunavath, V. (2020). Big Data. Big Data in Emergency Management: Exploitation Techniques for Social and Mobile Data, 15-29. [https://link.springer.com/chapter/10.1007/978-3-030-48099-8_2 Paper]
* [https://spark.apache.org/docs/latest/quick-start.html Spark Quick Start (with Python examples)]


[https://tp.uio.no/uib/timeplan/timeplan.php?id=INFO319&type=course&sort=week&sem=18h&lang=en.]
== Session 2 - Big data architecture. News Angler/News Hunter. Kafka ==
* Kitchin, chapter 1-3
* Opdahl, A. L., & Tessem, B. (2021). Ontologies for finding journalistic angles. Software and Systems Modeling, 20(1), 71-87. [https://link.springer.com/article/10.1007/s10270-020-00801-w Paper]
* Gallofré, M., Opdahl, A. L., Stoppel, S., Tessem, B., & Veres, C. (2021). The News Angler Project: Exploring the Next Generation of Journalistic Knowledge Platforms. In Proceedings of Norsk IKT-konferanse for forskning og utdanning. [https://ojs.bibsys.no/index.php/NIK/article/view/939/792 Short Paper] [Poster]
* [https://kafka.apache.org/intro Kafka Introduction]


== Locations ==
== Session 3 - Cloud. OpenStack ==
* '''Information meeting:''' via Zoom.
* NREC. OpenStack. TerraForm. Ansible
* '''Regular sessions:''' Lauritz Meltzers hus (the Social Science / SV building), seminar room 548 (6th floor)


== Format ==
== Session 4 - Virtual instances. Docker ==
The regular sessions will be a combination of lectures, student presentations and discussions. You will all be expected to present 3-4 papers/chapters as part of the course. I will try to balance workload evenly (so if some people get two papers and other three, the two papers will be longer and the three papers shorter).
* TDB


We will try to finish each session by 1600.
== Session 5 - Big data storage. Cassandra ==
* TDB


== Readings ==
== Session 6 - Privacy. GDPR ==
The detailed readings for each session will be made available on this page in due time.
* TDB


== Presenting a chapter from the book ==
== Session 7 - Essay presentations ==
*   Prepare presentation for about 20 minutes. We will set off 5-10 more minutes for discussion and comments.
* TDB
*    Each slide should contain at least one of the sections from the allocated chapter.
*    Prepare slides. For a 20 minute presentation, 10 slides is the maximum.
*    Rehearse a few times beforehand. Talk through the presentation out loud for yourself (not just "inside your head").
*    Share your slides by uploading them to the file section here in the portal.


== Presenting a research article ==
== Session 8 - Project demonstrations ==
Here are a few points about the paper presentations and preparations:
* TDB
*   Make sure you start reading at least a week before, not in the last 2-3 days. You need time to let the paper sink in a bit before you start preparing the presentation. That way it is easier to see and present the big picture.
*    This may be the first time you read a research paper. I have tried to choose papers that are rather short and simple but, nevertheless, some parts of almost every paper will be hard for you to understand. If you come across difficult details, try to focus on the purpose of what they are doing. When they mention, for example, statistical techniques, I do not expect that you read up on statistics. But explain why they need statistics and tell us the names of the techniques they use and on what data.
*    Plan each presentation for about 20 minutes. We will set off 5-10 more minutes for discussion and comments.
*    Prepare slides. For a 20 minute presentation, 10 slides is the maximum.
*    Your presentation should try to answer at least the following: What is the problem the paper addresses? Why is this an important problem? Are the authors targetting a particular usage domain? What solutions do they propose? How does the solution work? Have they evaluated the solution? If so how? If not yet, how are they planning to evaluate it - or how do you think they should evaluate it? What are the limitations of the proposal? Do you see problems with what they are doing?
*    These questions are not all suitable for all papers, so you must make a pick! Maybe there are other things you should say about the paper too. Some of the papers mostly describe a problem or a case study, for example, so the presentations will be quite different.
*    Rehearse a few times beforehand. Talk through the presentation out loud for yourself (not just "inside your head").
*    Share your slides by uploading them to the file section here in the portal.
*    Some papers are longer and some shorter, some easier and some harder. This is how it has to be, but I will try to balance it out so that the workload on each of you is as equal as possible.
 
== Uploading your presentation ==
Sharing your presentation slides is a mandatory part of the presentation. You can upload your slides through Inspera in this group
[https://mitt.uib.no/groups/? mitt.uib.no/groups/?]. If you are not already a member, you can register yourself (the group is open).
 
Please use file names like this: "Session2-Pathologies-ALO.pdf", so that "Session2" is the session, "Pathologies" is a central term in the paper title and "ALO" are your initials.

Revision as of 14:13, 5 July 2022

Tentative themes for each session

  • Thursday August 18th: Introduction meeting
  • Thursday September 1st: Session 1 - Introduction to big data. Big-data processing. Spark
  • Thursday September 15th: Session 2 - Big data architecture. News Angler/News Hunter. Kafka
  • Thursday September 29th: Session 3 - Cloud. OpenStack
  • Thursday October 13th: Session 4 - Virtual instances. Docker
  • Thursday October 27th:Session 5 - Big data storage. Cassandra
  • Thursday November 10th: Session 6 - Privacy. GDPR
  • Thursday November 24th: Session 7 - Essay presentations
  • Thursday December 8th: Session 8 - Project demonstrations

Session 1 - Introduction to big data. Big-data processing. Spark

Session 2 - Big data architecture. News Angler/News Hunter. Kafka

  • Kitchin, chapter 1-3
  • Opdahl, A. L., & Tessem, B. (2021). Ontologies for finding journalistic angles. Software and Systems Modeling, 20(1), 71-87. Paper
  • Gallofré, M., Opdahl, A. L., Stoppel, S., Tessem, B., & Veres, C. (2021). The News Angler Project: Exploring the Next Generation of Journalistic Knowledge Platforms. In Proceedings of Norsk IKT-konferanse for forskning og utdanning. Short Paper [Poster]
  • Kafka Introduction

Session 3 - Cloud. OpenStack

  • NREC. OpenStack. TerraForm. Ansible

Session 4 - Virtual instances. Docker

  • TDB

Session 5 - Big data storage. Cassandra

  • TDB

Session 6 - Privacy. GDPR

  • TDB

Session 7 - Essay presentations

  • TDB

Session 8 - Project demonstrations

  • TDB
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