Welcome to Aspects of Advanced Analytics

**Description: **Big Data is being collected in ever larger amounts, e.g., from the (geo-social) web, sensors/IoT devices in cyber-physical systems, or scientific experiments. However, it is necessary to go beyond merely storing and querying data to get the full benefit. Instead, advanced analytics (data mining, prediction, forecasting,..) is applied to the huge data volumes to extract trends and patterns, and use historical data to predict future events, so-called predictive analytics. Recently, optimization has been added on top, resulting in so-called prescriptive analytics that prescribes the best course of action given the data and associated predictions and optimization goals.

Traditional data analytics systems do not scale, and/or support only some of the tasks, or do not support the deep requirements for specific types of data, resulting in poor scalability, poor developer productivity and/or lack of functionality.

This course will cover selected aspects of advanced analytics including concepts, algorithms, and systems. The course will feature a mix of theoretical concepts and algorithms with practical hands-on exercises using specific advanced analytics systems on a number of realistic case datasets, focusing on the application area of energy analytics.

**Prerequisites:** A general background in computer science, and general familiarity with database management and analytics is expected. In specific, students are required to possess following knowledge:

**Mandatory:**- Linear algebra: linear equations, vector and matrix operations
- Database systems: relational data model, SQL, DBMS internals/query processing
- Algorithms and data structures: basic algorithms (e.g., sort, search) and data structures (e.g., list, tree, table), and basic knowledge of designing and analyzing algorithms
- Programming languages: know at least one of the following: Python/Java/C-C++

**Preferable:**- Distributed computing: concepts of distributed systems, Cloud Computing, MapReduce and big data tools (e.g., Hadoop, Hive, Pig, Spark)
- Machine learning: basic algorithms of classification (e.g., regression, SVM, neural networks), clustering (e.g., k means, kNN)
- Optimization: linear/integer programming
- Time series forecasting: forecasting models for time series such as ETS models, ARIMA models
- Other scripting languages and tools: R/Matlab

**Learning objectives: **The objectives of the course are to provide students with a working understanding of concepts, algorithms and systems for selected aspects of advanced analytics, with an application focus on energy analytics.

**Organizer: **Professor Torben Bach Pedersen, email: tbp@cs.aau.dk

**Lecturers: **Associate Professor Lukasz Golab, University of Waterloo and post docs Thi Thao Nguyen Ho and Laurynas Siksnys, AAU.

**ECTS:** 2

**Time:** December 5-7, 2017

**Place:**

**Zip code: **

**City: **Aalborg

**Number of seats: **20

**Deadline: **20 October 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Nguyen Ho
- Teacher: Torben Bach Pedersen
- Teacher: Laurynas Siksnys

Welcome to Graph Analytics: Concepts, Algorithms, and Systems

**Description: **Many types of data can be described naturally using graphs, e.g., road networks, social media friendships, semantic web data, and molecule structures. Such data is being collected at unprecedented scale and holds valuable information. However, in order to extract the value from the graph data, one has to perform various kinds of graph analytics to find specific subgraphs, e.g., connected cliques in friendship graphs.

Traditional data storage and analytics systems, including mainstream Big Data Systems, do not handle graph data natively, and thus perform poorly both in terms of functionality and performance. Instead, graph-specific systems and algorithms are emerging. This course will cover the basics of graph analytics, including concepts, algorithms, and systems. The course will feature a mix of theoretical concepts and algorithms with practical hands-on exercises using specific graph analytics systems on a number of realistic case datasets.

**Prerequisites**: A general background in computer science, including standard graph concepts and algorithms, and general familiarity with database management, as can be achieved through an undergraduate database course, is expected. Participants who have taken graduate data management courses will benefit from this additional background.

**Learning objectives**: The objectives of the course are to provide students with a working understanding of concepts, algorithms and systems for graph analytics.

**Organizer: **Professor Torben Bach Pedersen, email: tbp@cs.aau.dk

**Lecturers: **International and recognised within the field

**ECTS:** 2

**Time:** September-August 2017

**Place:** Aalborg University

**Zip code: **

**City: **Aalborg

**Number of seats: **20

**Deadline: **30. June 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Torben Bach Pedersen

Welcome to Big Data Management on Modern Hardware

**Description: **The roots of many productive database systems today date back thirty years or more. Prominent systems such as IBM's System R or the then-research prototype Ingres were first developed in the 1970s and were designed to address the hardware landscape of the time: disks or even tapes were the only medium to hold reasonable amounts of data; main memory could be considered as truly random access; and the major cost factor in database processing was I/O.

Since that time, computer architectures have changed significantly. RAM chips have become cheap enough to make in-memory processing feasible; caches and other architectural details lead to non-uniform memory access cost (an increasingly relevant performance factor); and the omnipresence of multi-core systems adds a whole new class of complexity to the problem.

In this course we look at how architectural changes affect database systems. Rather than suffering from the increasing latency gap for accesses to main memory, for instance, we can use available CPU caches to our advantage. A cache-aware design can improve the performance of a database operation by orders of magnitude. Likewise, modern CPU features (such as vector instructions) or specialized CPUs (like IBM's Cell processor or the nVidia CUDA architecture) can accelerate database tasks if the respective implementation has been designed carefully.

**Prerequisites**: A general background in computer science and general familiarity with database management, as can be achieved through an undergraduate database course, is expected. Participants who have taken a graduate database course will benefit from this additional background.

**Learning objectives**: The goal of this course is that students can understand ongoing trends in hardware development and link them to the behaviour of algorithms, in particular data-intensive algorithms, when ran on modern hardware. This understanding will help in the design of new algorithms, tailor-made for the characteristics of modern hardware.

**Format**: Readings, lectures, and exercises

**Lecturer bio**: Jens Teubner is leading the Databases and Information Systems Group at TU Dortmund in Germany. His main research interest is data processing on modern hardware platforms, including FPGAs, multiKcore processors, and hardware-accelerated networks. Previously, Jens Teubner was a postdoctoral researcher at ETH Zurich (2008-2013) and IBM Research (2007-2008). He holds a PhD in Computer Science from TU München (Munich, Germany) and an M.S. degree in Physics from the University of Konstanz in Germany.

**Organizer: **Associate Professor Katja Hose, AAU, email: khose@cs.aau.dk

**Lecturers: **Jens Teubner, TU Dortmund, Germany, email: jens.teubner@cs.tu-dortmund.de

**ECTS:** 2

**Time:** 1 and 2 March 2017. From 8:00-16:00

**Place:** Selma Lagerlöfs Vej 300, room 02.90, Aalborg University

**Zip code: **DK-9220 Aalborg East

**City: **Aalborg

**Number of seats: **20

**Deadline: **8 February 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Katja Hose

Welcome to Route Planning in Transportation Networks

**Description: **Driven by digitization, transportation is under a rapid development. Route planning and related algorithms are at the core of such technologies as traffic-aware navigation, electric vehicles, and autonomous vehicles.

This course gives an overview of recent algorithmic results in the route planning context. In the first part, participants will learn how to compute optimal routes in road networks within milliseconds or less even at a continental scale, also touching upon harder problem variants like constrained or multi-criteria shortest paths and scenarios with dynamically changing edge weights. The second part will deal with journey planning in public transit networks which is, although seemingly very similar, considerably more challenging and requires new ideas and concepts to achieve interactive query times. In the last part, the course considers more high-level problems like facility location which are closely related to shortest path structures in the road network. The optimized placement of battery loading stations for electric vehicles is an example of such problems.

While the main focus of the course is on the algorithms, the course also includes one or two theoretical results that provide a better understanding why the proposed methods are so efficient in practice.

The course includes practical exercises where the students have to implement some of the algorithms and data structures covered in the lectures using OpenStreetMap data.

**Prerequisites**: Good knowledge of standard data structures and algorithms (e.g., search trees, Dijkstra's and other basic graph algorithms); good knowledge of a programming language such as C/C++ or Java.

**Learning objectives**: The objectives of the course are to provide students with a working understanding of concepts and algorithms for route planning in transportation networks.

**Organizer: **Associate Professor Simonas Saltenis, email: simas@cs.aau.dk

**Lecturer: **Professor Dr. Stefan Funke, University of Stuttgart

**ECTS:** 2

**Time:** 19-21 September 2017

**Place:** Aalborg University

**Zip code: **

**City: **Aalborg

**Number of seats: **20

**Deadline: **30. June 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Simonas Saltenis

Welcome to Probabilistic Network Analysis

**Description: **Social networks, biological networks, sensor networks, the WWW, ... all are examples of networks that provide increasingly rich sources of data, and that become an increasingly important subject of study. The common underlying structure, and the similarity of analysis tasks in very different domains concerned with networks has given rise to the emergent discipline of network science. An important element of network science consists of Machine Learning on graph data for network structure analysis and predictive modeling. In this course core Machine Learning techniques for network data are presented.

The focus lies on techniques that build on probabilistic network models, and that apply statistical learning principles (however, some classical non-probabilistic approaches will also be discussed).

The scope of the course is (approximately) described by the following topics:

- Random graph models; power laws;
- Graph clustering/community detection
- Node classification and link prediction
- Random walk models: pagerank, hitting times, commute times
- Multi-relational graphs and statistical relational learning

**Prerequisites:** Basic knowledge of probability theory and statistical inference. Ability to install and use a network analysis toolbox in the form of R or Matlab libraries.

**Learning objectives**: To understand basic principles and techniques of probabilistic network analysis. Ability to read current research papers applying probabilistic analysis techniques.

**Organizer and l****ecturer: **Associate Professor** **Manfred Jaeger, AAU, email: jaeger@cs.aau.dk

**ECTS:** 2.25

**Time: **29, 30 May and 1 June 2017

**Place: **Selma Lagerløfs Vej 300, room 02.90

**Zip code: **9220

**City: **Aalborg

**Number of seats: **25

**Deadline:** 15 May 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Manfred Jaeger

Welcome to Linear & Integer Linear Programming

**Description: **The course will consist of two parts. Part 1 will address linear programming (LP) (also called linear optimization) which is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (mathematical optimization). Whereas linear programming is solvable in polynomial time the exponential Simplex algorithm is the most widely used algorithm for solving LP problems. This part will illustrate how several problems may be formulated and solved using LP.

The second part considers integer programming problems being optimization problems, where some or all of the variables are restricted to be integers. In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear. In contrast to (ordinary) linear programming, which is solvable in polynomial time, ILP is NP-hard. However, there are several problems which may be naturally formulated as ILP, e.g. task-graph scheduling, travelling sales-person problem, worst-case execution time analysis of code. This part will provide heuristics for how to solve ILP efficiently for several practical instances.

**Prerequisites: **Understanding of basic complexity theory, manipulation of matrices and basic linear algebra.

**Learning objectives: **To be able to formulate and solve optimization problems using LP and ILP

**Organizer and l****ecturers: **Krishna-Murthy Subramani, email: sub@cs.aau.dk

**ECTS:** 2

** Time: **May 8th, 9th, 11th, 15th, 17th and 19th. Each time from 10:00-12:00 in the room 0.2.90

**Place: **AAU, Selma Lagerlöfsvej 300

**Zip code: **9220

**City: **Aalborg

**Number of seats: **20

**Deadline: **24. April 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Helene Ulrich Pedersen
- Teacher: Jiri Srba

Welcome to Logics for Computation

**Description: **This course aims at introducing the basic concepts, results and tools from Logic and Model Theory. The main focus will be on Modal Logics. We will formally define various metamathematical concepts such as syntax, semantics, truth, provability, completeness and complete axiomatizations, compactness, decidability and their relation to the concepts of theoretical computer science such as transition systems, bisimulation, operational semantics, concurrency. We will also discuss about process algebras and bisimulation, about probabilistic and stochastic systems with their measure theoretical and logical foundations. With some of these concepts the students are already familiar from more specific courses and they are already using some of them in their research. The role of this course is to present these concepts in a general framework and to clarify the spectrum of their use and applicability.

**Prerequisites:** The students attending the course are expected to have basic working knowledge of discrete mathematics and theoretical computer science.

**Learning objectives:** The aim of the course is to provide the students with the mathematical and logical prerequisite for approaching research topics in theoretical computer science.

**Organizer and l****ecturers: **Associate Professor Radu Mardare, email: mardare@cs.aau.dk & Professor Kim G Larsen, email: kgl@cs.aau.dk

**ECTS:** 2.25

**Time:** Spring 2017

**Place:** AAU Department of Computer Science, Selma Lagerløfs Vej 300

**Zip code: **9220

**City: **Aalborg

**Number of seats: **TBA

**Deadline: **TBA

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Kim Guldstrand Larsen
- Teacher: Radu Mardare

Welcome to Advances in Digital Design Theory

**Description: **Socio-interactive design is a particular perspective that applies to IT and software applications, how they must be view and examined in a social context, and the influence this has on design of features and interactions. Users are both individual and independent actors as well as social and dependent actors. In this course we will work design as a product and as a process and we will draw implications for the evaluation of applications. The perspective applies to both applications for leisure/entertainment and for supporting complex work settings and processes.

The course will cover the following topics:

- What is socio-interactive design and evaluation?
- Foundations in philosophy of science and in the disciplines of human-computer interaction and systems development
- Exemplars of socio-interactive design and evaluation
- Design science and action research
- Research design and experimental set-up
- Empirical evaluation

**Format**: Readings, lectures, and exercises.

**Learning objectives**: The goal of this course is to give an introduction the socio-interactive perspective on design and evaluation of IT/software applications. In particular, the course will enable the participants to design their own research, teach fundamental techniques that cover design and evaluation with a particular focus on empirical evaluation.

**Prerequisites**: Participants are expected to have general familiarity with human-computer interaction, interaction design and systems/software development.

**O****rganizer: **Professor Peter Axel Nielsen, email: pan@cs.aau.dk

**Lecturers: **International and recognised within the field

**ECTS:** 2

**Time:** September 2017

**Place:** AAU

**Zip code: **

**City: **

**Number of seats: **20

**Deadline: **11. August 2017

**Important information concerning PhD courses**** **We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Peter Axel Nielsen