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Summer School 2012

GEOCROWD Summer School on Data Management for Crowdsourced and Volunteered Geographic Information


30.6.-4.7.2012 (arrival 29.6.)


Porto Platanias Hotel (http://www.portoplatanias.gr/)
Chania, Crete


The program consists of a total of 8 full sessions each lasting 3 hours and including the following types of sessions:
  • 10min madness – students presenting their research results and agenda in a 10min talk
  • Tutorials – 3h talks given by invited speakers.
  • Mentored sessions – work in groups around certain themes assisted by present speakers/scientists.

Day/Session Morning (9-12) Afternoon (14-17)
30.6. (Sat) Welcome & Ailamaki 10 min madness
1.7. (Sun) Renz Excursion
2.7 (Mo) Wenk Torp
3.7 (Tue) Ligozat Mentored Session
4.7 (Wed) Tsiavos Departure


The following invited speakers have accepted giving a talk at the summer school. The talk titles are tentative.

Prof. Carola Wenk

University of Texas at San Antonio, USA

Shape analysis and reconciliation of geospatial trajectory data
This tutorial gives an overview of data processing techniques for sets of geospatial trajectories, with the goal of extracting and reconciling common shapes and patterns in the data. In the first part, we will introduce shape-based distance measures, such as the Frechet distance, to compare trajectories. We will study algorithms to compute these distance measures, and we will discuss map-matching algorithms that map a trajectory to a road network. The second part of the tutorial will cover algorithms for identifying patterns and clusters in sets of trajectories. We will discuss how to compute average or median curves that represent and reconcile a set of curves. We will also study algorithms for constructing networks from sets of trajectories. Throughout the tutorial we will discuss how to properly model the inherent noise and uncertainty in the trajectory data.

Prof. Kristian Torp

Aalborg University, Denmark

An ITS Platform
Due to the current economic downturn and the focus on reduction the green-house gas (GHG) emission the traffic industry is facing the challenge of having both to do the transportation cheaper and use less fuel. One way this challenge is tackled is by using so called Intelligent Transport System (ITS) technology, which covers equipment being added to both the road infrastructure and to the vehicles. This equipment produces a lot of data that is of interest to the database community both for storing and querying.
This talk will provide an overview of an ITS platform that stores various types of data produced from vehicles. This data is mainly GPS and CANBus data. This data is received from 3,000-5,000 vehicles and daily loaded into a data warehouse. The data is used to estimate driving time for the entire road network in Denmark and to estimate the GHG emission for individual trips.
The talk will cover the software architecture that consists only of open-source components. The talk will discuss the advantages and disadvantages of using open-source software and the creative-commons digital map from Open-Street Map.
The platform is quite flexible and can provide novel and detailed information that can be used by the transport industry to better utilize the resources used both in term of man power and fuel. A number of examples will be provided. This include turn-time estimation, green wave analysis of road stretches, and eco-driving.

Prodromos Tsiavos

London School of Ecomomics – IPR and licensing of (open) geospatial data


Prof. Gérard Ligozat

LIMSI-CNRS, University of Paris-Sud, France

Qualitative spatial and temporal reasoning: an introduction
I.   A bird’s-eye view of the domain
II.  Focussing on specific topicsI. A bird’s-eye view of QSTR
The first part of the tutorial aims at giving a bird’s-eye view of the domain of QSTR.
Its table of contents is the following:
1. A ground-breaking paper: Allen 1983
-Allen’s calculus
-The time point calculus
-Allen’s algebra
-Complexity results for Allen’s calculus
2. After Allen: a gallery of calculi
-Qualitative spatial and temporal binary calculi
-Ternary calculi
-Complexity results
3. The search for unifying principles
-Partition schemes
-Weak representations
-The category of weak representations
4. Complementary and alternative approaches
-Hybrid calculi
-Fuzzy reasoning
-Relational Structures
5. Conclusions and prospects

II.  Focussing on specific topics
The second part of the tutorial will consider more in depth some of the topics
presented in the first part. A tentative list of topics:
1. What is a qualitative calculus?
– Partition schemes
– Weak representations
– Consistency as a categorical notion
2. The geometric approach to complexity vs the syntactic approach
– Regions and lattices
– Convexity and stronger notions
– Application to some formalisms
– The case of INDU
3. Functors between calculi
4. Complexity of relational structures
5. Software tools for QSTR

Dr. Matthias Renz

LMU Munich, Germany

Principles of Similarity Search and Spatial Query Processing and Advanced Techniques for Searching in Uncertain Location Data
This tutorial provides a comprehensive and comparative overview of general techniques to efficiently support spatial query processing. In particular, it identifies the most generic query types and discusses general algorithmic methods to answer such queries efficiently. Here, the tutorial addresses the two prominent paradigms of efficient spatial query processing: indexing and multi-step query processing. In addition, the tutorial introduces the general concept of probabilistic query processing in uncertain databases which has achieved much attention in the database community recently due to new sensor technologies and new ways of collecting data. This tutorial provides a comprehensive overview of general techniques for the key topics in the field of querying uncertain data. In particular, it identifies the most generic types of probabilistic spatial query processing and discusses general algorithmic methods to answer such queries efficiently. Finally, novel concepts for managing and querying uncertain spatio-temporal data are presented.

Prof. Anatassia Ailamaki

EPFL, Switzerland

Managing Scientific Data
Today’s scientific processes heavily depend on fast and accurate analysis of experimental data. Scientists are routinely overwhelmed by the effort needed to manage the volumes of data produced either by observing phenomena or by sophisticated simulations. As database systems have proven inefficient, inadequate, or insufficient to meet the needs of scientific applications, the scientific community typically uses special-purpose legacy software. When compared to a general-purpose DBMS, however, application-specific systems require more resources to maintain, and in order to achieve acceptable performance they often sacrifice data independence and hinder the reuse of knowledge. Nowadays, scientific datasets are growing at unprecedented rates, a result of increasing complexity of the simulated models and ever-improving instrument precision; consequently, scientistsqueries become more sophisticated as they try to interpret the data correctly. Datasets and scientific query complexity are likely to continue to grow indefinitely, rendering legacy systems increasingly inadequate. To respond to the challenge, the data management community aspires to solve scientific data management problems by carefully examining the problems of scientific applications and by developing special- or general-purpose scientific data management techniques and systems. This talk discusses the work of teams around the world in an effort to surface the most critical requirements of such an undertaking, and the technological innovations needed to satisfy them.

Attending Scientists

  1. Timos Sellis, National Technical University of Athens, Greece
  2. Dieter Pfoser, National Technical University of Athens, Greece
  3. Marinos Kavouras, National Technical University of Athens, Greece
  4. Christian Freksa, Universitaet Bremen, Germany
  5. Thomas Barkowsky, Universitaet z, Germany
  6. A. Stewart Fotheringham, University of St. Andrews, UK
  7. Agnes Voisard, FU Berlin, Germany

PhD Students

  1. George Skoumas, National Technical University of Athens, Greece
  2. Christodoulos Efstathiades, National Technical University of Athens, Greece
  3. Alkyoni Baglatzi, National Technical University of Athens, Greece
  4. Kostas Patroumpas, National Technical University of Athens, Greece
  5. Sophia Karagiorgou, National Technical University of Athens, Greece
  6. Rami Alsalman, Universitaet Bremen, Germany
  7. Chunyuan Cai, Universitaet Bremen, Germany
  8. Paolo Fogliaroni, Universitaet Bremen, Germany
  9. Giorgio De Felice, Universitaet Bremen, Germany
  10. Laura Radaelli, Aarhus University, Denmark
  11. Anders Skovsgaard, Aarhus University, Denmark
  12. Vaida Ceikute, Aarhus University, Denmark
  13. Katarzyna Siła-Nowicka, University of St. Andrews, UK
  14. Felix Kling, National University of Ireland at Maynooth
  15. Cathal Coffey, National University of Ireland at Maynooth
  16. Paras Mehta, FU Berlin
  17. To Tu Cuong, FU Berlin