Archive for the 'SIKS' Category

Dutch-Belgian IR workshop in Twente

Friday, August 15th, 2008, posted by Djoerd Hiemstra

Vrijhof with famous sunken tower We organize the 9th Dutch-Belgian Information Retrieval Workshop (DIR) in Twente. The event will take place on February 2-3, 2009 in our lovely “Vrijhof” building. The primary aim of Dutch-Belgian Information Retrieval (DIR) workshop is to provide an international meeting place where researchers from the domain of information retrieval and related disciplines, can exchange information and present innovative research developments. The first call for papers is just out. Papers may range from theoretical work to system descriptions. We encourage Ph.D. students to submit their research. We also welcome contributions from industry when they focus on novel research directions. Have a look at the DIR website at:

Seminar on Searching and Ranking

Wednesday, May 28th, 2008, posted by Djoerd Hiemstra

Nelly Litvak and I organize a small but really interesting seminar before the PhD defense of Henning Rode on 27 June 2008: The first SIKS/Yahoo Seminar on Searching and Ranking in Structured Text Repositories. The goal of the one day seminar is to bring together researchers from companies and academia working in the area of computer science and applied mathematics on ranking and searching in highly dynamic, structured text environments. Keynote speakers are:

  • Ricardo Baeza-Yates (Yahoo! Research, Barcelona, Spain)
  • Debora Donato (Yahoo! Research, Barcelona, Spain)

The seminar is sponsored by: WGI, CTIT, NWO, SIKS, and Yahoo. Please send your name and affiliation to ssr (at) if you plan to participate in the seminar.

SIKS focus area: Data management, storage and retrieval

Friday, January 25th, 2008, posted by Djoerd Hiemstra

The Dutch Research School for Information and Knowledge Systems (SIKS) organizes its research in 7 so-called research foci. The scope of the SIKS research focus Data Management, Storage and Retrieval is the theory and the application of computers to the management of information, including the aspects of data acquisition, organization, storage, querying and retrieval, security and privacy, ranging from highly structured databases to unstructured natural language texts.

The research focus Data Management, Storage and Retrieval is shaped by two major success stories in Computer Science: 1) the development of relational database systems in the 1970’s and 1980’s mainly influenced by office automation and enterprise information systems, and 2) the development of large scale information retrieval systems at the end of the 1990’s, influenced by the development of the world wide web. The storage and retrieval component of today’s information system is formed by database management systems (DBMSs), which abstract the peculiarities of storage media and processing components into a data model, integrity rules, and query facilities. Although strong relational DBMSs have become a commodity product for administrative information system applications, they have been proven inadequate for storing and searching semi-structured data such as web data. The storage and retrieval component of today’s web search engines is formed by information retrieval (IR) systems, that provide effective ranking strategies, efficient indexes, and data compression. They focus on user satisfaction rather than on integrity of the data. Research themes that SIKS PhD students address are:

  • Integration of Text, Data, and Streams: Create ways to integrate data retrieval and information retrieval, for instance for XML databases and XML streams
  • Multimedia Retrieval: Create easy ways to analyze, summarize, search, and view multimedia information such as video databases.
  • Sensor Data and Sensor Networks: Create ways to manage and query networks of very large numbers of low-cost devices.
  • Reasoning about uncertain data: Create ways to analyze, query and reason over imprecise and uncertain data (Related to SIKS focus Knowledge representation & reasoning)
  • Contextual Retrieval and User Interaction: Use knowledge about the user’s context to provide more effective results (related to SIKS focus Human computer interaction).
  • Learning Ranking Algorithms: Create new models of information retrieval and machine learning of complex ranking functions (related to SIKS focus Computational intelligence)
  • Enterprise search and Data Spaces: Integrate enterprise information to support business processes, for example expertise finding (related to SIKS focus Enterprise information systems)
  • Distributed and peer-to-peer data management: Create ways to distribute data over many loosely coupled autonomous systems (related to SIKS focus Agent technology and Web-based information systems)

SIKS research methodology course team photo

Friday, November 16th, 2007, posted by Djoerd Hiemstra

See below the brave SIKS students that successfully finished the course Research methods and methodology. The primary goal of this hands-on course is to enable Ph.D. students to make a good research design for their own research project. To this end, it provides an interactive training in various elements of research design, such as the conceptual design and the research planning. But the course also contains a general introduction to the philosophy of science (and particularly to the philosophy of mathematics, computer science and AI). And, it addresses such divergent topics as “the case-study method”, “elementary research methodology for the empirical sciences” and “empirical methods for computer science”.

SIKS team photo

XML: where databases and information retrieval meet

Monday, April 11th, 2005, posted by Djoerd Hiemstra

Advanced SIKS-course

This course focuses on the use of database and information retrieval techniques for managing large amounts of XML data. XML is the web standard for exchanging data on the world wide web. The standard comes with a number of tools that are available in database systems as well, like schema’s (DTDs and XML schema) and query languages (XPath, XQuery), but some things are still missing like efficient storage, query processing and indexing of XML data. As XML is often used to markup textual data, XML data management systems need to support techniques from search engines as well, for instance full-text search and ranking of search results.

See: course programme