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Using Summaries to Search and Visualize Distributed Resources Addressing Spatial and Multimedia Features

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Abstract

Summarization is an important means to cope with the challenges of big data. Summaries can help to achieve a first overview, they can be used to characterize subsets, they allow for the targeted access to data, and they build the basis for visualization techniques. In the present article, we point out the role of summaries as well as potential application scenarios. As examples, summarization techniques for spatial data (as an example for specific low dimensional techniques) and for general metric spaces (as a generic example with a broad spectrum of applications) are described. Furthermore, their use for resource selection and resource visualization in large distributed scenarios is outlined.

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Notes

  1. Resource selection based on a single criterion, for example image content, is only a first step on the way to an effective retrieval system. When querying for multiple criteria, for example for an image with a particular content which was taken in a certain geographic region, criterion-specific resource rankings can be combined by applying a merging algorithm for ranked lists (cf. e.g. [2, 19]). Moreover, resource description and selection schemes can be designed which support content-based search and in addition preserve the geographic distribution of the images by integrating both content-based as well as geographic search criteria (cf. e.g. [14] which combines text and geographic information). However, such aspects are out of the scope here.

  2. Note that we may use the term (non-)relevant differently from traditional IR, where it is strongly related with the information need concept. We speak of (non-)relevant database objects to indicate that they are (not) part of the final query result. Similarly, (non-)relevant feature space regions or resources do (not) contain database objects from the final result. This interpretation corresponds with for example [27].

  3. An overview of different summarization techniques both using pivoting and/or aggregation is given in [3].

  4. In order to fit into a spreadsheet, the allocation has been sampled. The first 300,000 resources show the orginal values. Afterwards, three resources are sampled into one value, which is a very accurate depiction of the real distribution, since the 300,001st biggest resource only has 16 data points, degrading one-by-one to one for resource 2,491,785.

  5. We use the images of the MIRFLICKR-25000 collection [18]. CEDD [8] features are extracted and compared using the Hellinger distance. Images are assigned to resources by the Flickr user ID.

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  1. Media Informatics Group, University of Bamberg, Bamberg, Germany

    Daniel Blank, Andreas Henrich & Stefan Kufer

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  1. Daniel Blank
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  2. Andreas Henrich
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  3. Stefan Kufer
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Correspondence to Daniel Blank.

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Blank, D., Henrich, A. & Kufer, S. Using Summaries to Search and Visualize Distributed Resources Addressing Spatial and Multimedia Features. Datenbank Spektrum 16, 67–76 (2016). https://doi.org/10.1007/s13222-015-0210-5

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