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- W2189404217 abstract "Meta-data hierarchies are playing central roles in the development and deployment of many data applications, especially in the Web. They can be defined as structured information that describes, explains, locates, and makes it easier to retrieve, use and manage information resources. In fact, they embody formalized knowledge and define aggregations between concepts/categories (in a given domain) facilitating the organization of the data and making the contents easily accessible to the users of these applications. Since hierarchical meta-data have significant roles in the data annotation, search, and navigation, they are often carefully engineered; however, especially in dynamically evolving domains, they do not necessarily reflect the content knowledge. In fact, the stagnant nature of meta-data may fail to timely capture the dynamic change of the relevant data contents. Moreover, when the users interests are highly focused, available meta-data, which are usually designed from domain experts for broad coverage of concepts in a given application domain, may fail to reflect details within the users foci of interest. Thus, in this thesis, we ask and answer, in the positive, the following question: “is there a feasible approach to efficiently and effectively adapt a given meta-data hierarchy to changing usage contexts?”. Based on these considerations, we propose a set of novel adaptation approaches for re-structuring existing meta-data hierarchies to varying application contexts and different data formats, and we evaluate the proposed schemes relying on different data collections. Moreover, we leverage these adapted meta-data structures for innovative data exploration methods that use domain-specific" @default.
- W2189404217 created "2016-06-24" @default.
- W2189404217 creator A5043892415 @default.
- W2189404217 creator A5050180423 @default.
- W2189404217 date "2010-01-01" @default.
- W2189404217 modified "2023-09-26" @default.
- W2189404217 title "Adaptation of Hierarchical Meta-Data for Efficient Large Data Set Exploration" @default.
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