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P180 - BTW2011 - Datenbanksysteme für Business, Technologie und Web

https://dl.gi.de/handle/20.500.12116/21081
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Auflistung P180 - BTW2011 - Datenbanksysteme für Business, Technologie und Web nach Autor:in "Aberer, Karl"

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  • Konferenzbeitrag
    Efficient interest group discovery in social networks using an integrated structure/quality index
    (Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Budura, Adriana; Michel , Sebastian; Aberer, Karl
    We consider the problems of interest group discovery in a social network graph using term-based topic descriptions. For a given query consisting of a set of terms, we efficiently compute a connected subset of users that jointly cover the query terms, based on the annotation vocabulary utilized by users in the past. The presented approach is twofold; first we identify so-called seed users, centers of interest groups, that act as starting points of the group exploration. Subsequently, we inspect the seed users' neighborhoods and build up the tree connecting the most promising neighbors. We demonstrate the applicability and efficiency of our method by conducting a series of experiments on data extracted from a Web portal showing that our method does not only provide accurate answers but calculates these also in an efficient way.
  • Konferenzbeitrag
    Tracking hot-k items over web 2.0 streams
    (Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Haghani, Parisa; Michel, Sebastian; Aberer, Karl
    The rise of the Web 2.0 has made content publishing easier than ever. Yesterday's passive consumers are now active users who generate and contribute new data to the web at an immense rate. We consider evaluating data driven aggregation queries which arise in Web 2.0 applications. In this context, each user action is interpreted as an event in a corresponding stream e.g., a particular weblog feed, or a photo stream. The presented approach continuously tracks the most popular tags attached to the incoming items and based on this, constructs a dynamic top-k query. By continuous evaluation of this query on the incoming stream, we are able to retrieve the currently hottest items. To limit the query processing cost, we propose to pre-aggregate index lists for parts of the query which are later on used to construct the full query result. As it is prohibitively expensive to materialize lists for all possible combinations, we select those tag sets that are most beneficial for the expected performance gain, based on predictions leveraging traditional FM sketches. To demonstrate the suitability of our approach, we perform a performance evaluation using a real-world dataset obtained from a weblog crawl.