• Algorithmic Challenges of Big Data (ACBD 2015)

    Tackling the challenges of big data is one of the most active research areas in computer science, both theoretical and practical. The goal of the workshop is to consolidate the European research community in this area, discuss recent advances, understand current trends, identify understudied areas, and formulate new directions for further investigation.

    The 2nd European Meeting on Algorithmic Challenges of Big Data (ACBD 2015) will be held in Dortmund, Germany, September 28-30, 2015.

    Further information can be found here.

  • BwInf Workshop for students

    On March 5 and 6 a workshop with 32 students from NRW takes place. Participants of the Bundeswettbewerb Informatik who successfully competed in the first round of the contest and live in NRW were allowed to apply for the workshop. Over the course of two days the students will solve problems from the fields of online algorithms, distributed data streams, inference control and model checking in groups of eight students.
    Further information can be found here.

  • Christian Sohler recieves ERC Starting Grant "Sublinear Algorithms for the Analysis of very large Graphs"

    The structural analysis of large networks such as the web graph, friendship graphs in social networks and citation graphs is important to many research areas and a major challange in the field of computer science. Algorithms currently available for network analysis are often not suitable as the size of these networks and the difficulty of the proposed problems makes hardware requirements or running times infeasible. Therefore, a common approach is to draw small samples from the network. This poses the following questions:

    What is the appropriate method to optain a sample from the network?

    After drawing a random node, should the entire neighborhood be explored or should one of the neighboring nodes be chosen randomly (random surfer model)? Depeding on the underlying problem, one choice may be preferrable to the other.

    How can the sample be interpretated?

    What can be inferred about the global structure of the network using only a local sample? For instance, are there significant differences between a locally planar network (a network without edge crossings) and globally planar networks? At first glance this seems likely, but there exist locally planar network instances whose global structure are notably different from planar networks.

    Among others, these topics are within the scope of Christian Sohler's ERC Starting Grant "Sublinear Algorithms for the Analysis of very large Graphs" funded by the European Union with more than 1.4 million Euros.

  • Workshop on Algorithms for Data Streams

    From July 23 to July 27 the "Workshop on Algorithms for Data Streams 2012" will be held at TU Dortmund in cooperation with the collaborative research center 876. The workshop will offer the chance to present and discuss their most recent scientific results in the field of data stream algorithms to international scientists.

  • Melanie Schmidt awarded Google Anita Borg Memorial Scholarship

    Chair 2 is very proud to welcome the third winner of the Google Anita Borg Scholarship among its members. Christine Zarges und Christiane Lammersen were also awarded in 2011 and 2010, respectively. The scholarship is endowed with 7.000 and is awarded annually to young grad and undergrad students based on their previous academic accomplishments and their perceived leadership. Melanie will be given the opportunity to participate at the Google Scholar's Retreat in June with the other 39 scholars.

    Congratulations on your award, Melanie!
  • The German Research Foundation (DFG) has granted a new collaborative research center:
    SFB 876 - Providing Information by Ressource-Constrained Data Analysis

    Combining embedded systems and data analysis enables new solutions in computer science, bio medicine, physics and mechanical engineering. The restrictions in computing power, memory and energy demand new algorithms for known learning tasks. These resource bounded learning tasks may also be applied to large-scale and high-dimensional data on servers.

    Our chair will contribute with research on the following projects: