[Παλιό]19/4/2005-Σεμινάριο Διαχείρ/Επεξεργασίας Πληροφοριών

[Ethel]

Τμήμα Πληροφορικής, Οικονομικό Πανεπιστήμιο Αθηνών

Ομιλητής: M. Theobald, Max Planck Institute for Informatics
Τίτλος ομιλίας: "Efficient Top-k Query Processing and Self-Tuning
Incremental Query Expansions"
Ημέρα: Τρίτη 19 Απριλίου 2005
Ώρα: 16:00 - 17:30 *** Οι ομιλίες ξεκινούν στις 16:00 ακριβώς. ***
Αίθουσα: Α41 (πτέρυγα Αντωνιάδου, κεντρικά κτίρια Πατησίων)

Περίληψη της ομιλίας:

Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algorithm for evaluating top-k queries is Fagin's threshold algorithm (TA). Since the user's goal behind top-k queries is to identify one or a few relevant and novel data items, it is intriguing to use approximate variants of TA to reduce run-time costs. Our work introduces a family of approximate top-k algorithms based on probabilistic arguments. When scanning index lists of the underlying multidimensional data space in descending order of local scores, various forms of convolutions and derived bounds are employed to predict when it is safe, with high probability, to drop candidate items and to prune the index scans. Furthermore, we present a novel approach for efficient and
self-tuning query expansions that is natively embedded into the top-k engine. The precision and the efficiency of the developed methods are experimentally evaluated based on different TREC text corpa and a structured data collection.
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