The paper investigates a version of the resource allocation problem arising in the wireless networking, namely in the OVSF code reallocation process. In this setting a complete binary tree of a given height n is considered, together with a sequence of requests which have to be served in an online manner. The requests are of two types: an insertion request requires to allocate a complete subtree of a given height, and a deletion request frees a given allocated subtree. In order to serve an insertion request it might be necessary to move some already allocated subtrees to other locations in order to free a large enough subtree. We are interested in the worst case average number of such reallocations needed to serve a request.In [4] the authors delivered bounds on the competitive ratio of online algorithm solving this problem, and showed that the ratio is between 1.5 and O(n). We partially answer their question about the exact value by giving an O(1)-competitive online algorithm.In [3], authors use the same model in the context of memory management systems, and analyze the number of reallocations needed to serve a request in the worst case. In this setting, our result is a corresponding amortized analysis.Classification: Algorithms and data structures
There are already several tools available that are capable of visualizing RDF data. The problem with RDF data is that they tend to be very large. To handle the data, the visualizers cannot display the whole data but rather need to use some kind of navigation. We have designed and implemented such visualizer. This paper describes our approach to the navigation, which was designed specifically with the preservation of the user's mental map in mind. We also compare our approach to the other visualizers.
Clustered graph visualization techniques are an easy to understand way of hiding complex parts of a visualized graph when they are not needed by the user. When visualizing RDF, there are several situations where such clusters are defined in a very natural way. Using this techniques, we can give the user optional access to some detailed information without unnecessarily occupying space in the basic view of the data. This paper describes algorithms for clustered visualization used in the Trisolda RDF visualizer. Most notable is the newly added clustered navigation technique.
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