Abstract. Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, offer an incomplete solution for information retrieval. In this paper we describe our Beagle ++ desktop search prototype, which enhances conventional fulltext search with semantics and ranking modules. This prototype extracts and stores activity-based metadata explicitly as RDF annotations. Our main contributions are extensions we integrate into the Beagle desktop search infrastructure to exploit this additional contextual information for searching and ranking the resources on the desktop. Contextual information plus ranking brings desktop search much closer to the performance of web search engines. Initially disconnected sets of resources on the desktop are connected by our contextual metadata, PageRank derived algorithms allow us to rank these resources appropriately. First experiments investigating precision and recall quality of our search prototype show encouraging improvements over standard search.
The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.ACM 978-1-59593-654-7/07/0005. 1 Lowercase semantic web refers to an evolutionary approach for the Semantic Web by adding simple meaning gradually into the documents and thus lowering the barriers for re-using information. 2
Abstract-Data-intensive scientific workflows exhibit inter-task dependencies that generate file-based communication schemes. In such scenarios, traditional disk-based storage systems often limit overall application performance and scalability. To overcome the storage bottleneck, in-memory runtime distributed file systems speed up application I/O. Such systems are deployed statically onto a fixed number of compute nodes and act as a distributed, fast I/O cache for the runtime generated data. Such static deployment schemes have two major drawbacks. First, the user is faced with the sometimes difficult task of estimating the size of the generated data, as the application would fail otherwise. Second, because applications exhibit significant variability of the data footprint and of the achieved parallelism during their runtime, this deployment scheme also leads to severe resource underutilization. To address these limitations, we present MemEFS, an elastic in-memory runtime distributed file system. MemEFS is able to scale elastically, based on application storage demands, by acquiring or releasing resources when needed. Our evaluation shows that, while generating modest runtime overheads, MemEFS is able to increase the resource utilization efficiency by up to 65%.
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