CDIM 2013 - International Workshop on Crowd-enabled Data and Information Management (CDIM)

In the recent years algorithms for data and information management, organization, and access have grown quite powerful even over huge and largely unstructured information repositories like the Web. Applications are almost limitless ranging from basic information extraction over knowledge management to complex business intelligence. However, with more complex information processing, retrieval, or mining capabilities also the algorithms’ complexity, susceptibility for errors and danger of overspecialization increases. Since most failings can be traced back to limited cognitive abilities, missing contextual knowledge or heuristics gone wrong, the idea of direct human feedback, supervision and intervention at processing time is currently pursued in many domains.
In fact, the need for human assistance in bridging the final quality gap for today’s infor-mation processing has already given rise to information systems that rely on hybrid archi-tectures. Such hybrid architectures transparently combine the efficiency of current algo-rithms with the cognitive power and flexibility of humans. Here, generally two design directions are popular:
Using human input for improving the steps performed by information processing algorithms by providing training samples, answering questions about ambiguous results, or by providing relevance feedback.
Involving humans directly into the information processing process, explicitly out-sourcing some of the required tasks or operators within the process.
The CDIM workshop welcomes original research and experience papers on a broad range of topics related to crowd-based data and information management, bringing together experts from the data base, information retrieval and data mining communities. The themes and topics listed below are intended as a sample of familiar themes and topics. However, we encourage papers on all crowd-related topics:
Crowd-enabled databases and information systems
Query processing with human operators
Crowd-enabled semantic annotation and disambiguation
Crowd-based algorithm evaluation
Crowd-provided training for data mining and machine learning
Quality issues in crowd-based retrieval methods
Interfaces for crowd-based approaches
Hybrid crowd-based approaches
Ethical issues of crowdsourcing