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Kursusnavn (dansk):Big Data Management 
Kursusnavn (engelsk):Big Data Management 
Semester:Efterår 2014 
Udbydes under:cand.it., softwareudvikling og -teknologi (sdt) 
Omfang i ECTS:7,50 
Kursussprog:Engelsk 
Kursushjemmeside:https://learnit.itu.dk 
Min. antal deltagere:12 
Forventet antal deltagere:50 
Maks. antal deltagere:100 
Formelle forudsætninger:This course assumes basic computer science and programming background. It requires that the participants have taken the introductory programming courses and a data modeling course (Introduction to Database Design) for the Software Development and Technology study programme.
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Information about study structure

This course is part of the SDT specialization Databases. 
Læringsmål:After the course students should be able to:

  • Analyze and discuss the characteristics of data exploration and analysis with large, fast-growing and diverse data sets.

  • Analyze and discuss the complexity of the database system landscape

  • Reflect upon the relative merits of SQL and NoSQL systems

  • Reflect upon basic big data analysis techniques

  • Reflect upon the societal issues linked to big data management

  • Design, conduct and report result of experiments related to data exploration and analysis in the context of two specific data sets.
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Fagligt indhold:“Big data” has become a term for a class of problems where the volume, complexity and velocity of the data challenge traditional approaches to data modeling, data management and data analysis. A few years back, production data was stored in relational forms and manipulated in the context of well-defined applications. This data could be extracted, transferred and loaded into data ware-houses where it could be analyzed off-line. Relational approaches to data management could fit all forms of problems.

Today, large volumes of data are being captured online, through sensors, outside the scope of traditional database systems. Making sense, possibly in real-time, of this data is a key challenge for many organi-zations so that they can understand and adapt quickly to changing conditions. For example, an hospital should incorporate GPS data about the actual location of its ambulances and helicopters with da-ta about the mission these vehicles are involved in as well as emer-gency calls and current status in various emergency rooms to take decisions in real-time when faced with an emergency call (also in the face of large-scale disasters). There is no longer one approach that can fit all problems. For each problem, IT specialists have to decide on appropriate models and systems to handle the relevant data.

In this course, we will introduce modern approaches to organizing and making sense of large, fast growing and diverse data sets. We will cover the principles of big data analysis, and propose a hands-on approach to big data modeling and management. The course will be based on lectures and practical weekly exercises with a range of different datasets and data management systems.
Two datasets will be selected as the outset for data exploration and analysis with relational, graph and document database systems. 
Læringsaktiviteter:14 ugers undervisning bestående af forelæsninger og øvelser

We will rely on exercises throughout the lectures to favor student involvements and to prepare for the exam.

We will rely on labs.
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See the schedule here:
link to the time table
The schedule will be available shortly before the beginning of the term. 

Obligatoriske aktivititer:There is a mandatory practical assignment extending the exercises done in class. 
Eksamensform og -beskrivelse:X. experimental examination form (7-scale; external exam), 7-trins-skala, Ekstern prøve

Duration of exam: 4 hours  

Litteratur udover forskningsartikler:The textbook we will use to cover the principles of big data analysis is:
"Principles of Big Data: Preparing, sharing and analysing complex information", Jules Berman, Morgan Kaufmann, 2013.

Optional/supplementary: "Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases", Joe Celko, Morgan Kaufmann, 2013.