For bachelor students we offer German lectures on database systems in addition with paper- or project-oriented seminars. Within a one-year bachelor project students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, search engines and information retrieval enhanced by specialized seminars, master projects and advised master theses.
The Web Science group focuses on various topics related to the Web, such as Information Retrieval, Natural Language Processing, Data Mining, Knowledge Discovery, Social Network Analysis, Entity Linking, and Recommender Systems. The group is particularly interested in Text Mining to deal with the vast amount of unstructured and semi-structured information available on the Web.
Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our data sets and source code.
We publish our research mostly at DB and IR-related journals and conferences. Many of our publications are available as downloads. If you cannot find one, please contact one of the authors.
Our group includes PostDocs, PhD students, and student assistants, and is headed by Prof. Felix Naumann. If you are interested in joining our team, please contact Felix Naumann.
There are numerous sources for statistical data sets: companies, government agencies, international organizations, etc. Statistical data is usually
of numerical type
collected in fixed intervals over a certain time period, and
reveals certain short-term and long-term trends.
Event Data
source: wikipedia.org
Event data can be gathered from various (mostly unstructured) sources, such as Wikipedia, Freebase, News Archives, and so on. In the context of this seminar, an event is described by its
type,
location, and
(starting) point in time.
Augmenting Statistical Data
Our goal is to detect certain trends in statistical data and automatically relate them to the historical events that triggered these events based on specific rules previously learned by our system.
Proposed Architecture
The following diagram depicts a possible architecture for the system. The green boxes each represents a specific component a team of students will be working on.
Team structure and tasks
Event Extractor
Time Series Analyzer
Rule System
Visualizer
Tasks
gather data sources
text extraction
information integration
choose data format
gather data sources
information integration
regression / time series analysis
statistics
association rule mining
machine learning
time shift
evaluation
find visualization framework
implement interactive GUI
data export
Important Dates and Material
Date
Event
Slides
18.10.10
first meeting
22.10.10, 23:59 CEST
deadline for sending participation request mail to Johannes
include your preferred focus (if any): extraction or analysis
include your preferred amount of "Leistungspunkte": 3 or 6
