'Query q' presentation slideshows

Traditional IR systems

Traditional IR systems. Traditonal IR systems Worth of a document w.r.t. a query is intrinsic to the document. Documents Self-contained units Generally descriptive and truthful. Web : A shifting universe. Web indefinitely growing Non-textual content Invisible keywords

1.1k views • 81 slides

Learning to Rank (part 1)

Learning to Rank (part 1). NESCAI 2008 Tutorial Yisong Yue Cornell University. Booming Search Industry. Goals for this Tutorial. Basics of information retrieval What machine learning contributes New challenges to address New insights on developing ML algorithms. (Soft) Prerequisites.

1.59k views • 66 slides

880 views • 58 slides

Perk: Personalized Keyword Search in Relational Databases through Preferences

Perk: Personalized Keyword Search in Relational Databases through Preferences. Marina Drosou Computer Science Department University of Ioannina , Greece Joint work with Kostas Stefanidis * and Evaggelia Pitoura http://dmod.cs.uoi.gr * Now at the Chinese University of Hong Kong.

688 views • 52 slides

I/O-Algorithms

I/O-Algorithms. Lars Arge Spring 2012 March 6, 2012. I/O-Model. Parameters N = # elements in problem instance B = # elements that fits in disk block M = # elements that fits in main memory T = # output size in searching problem We often assume that M>B 2

521 views • 39 slides

On Triple Dissemination, Forward-Chaining and Load Balancing in DHT Based RDF Stores

Computer Networks and Telematics Prof. Christian Schindelhaue. Albert-Ludwigs-University Freiburg SS 2009 Department of Computer Science. On Triple Dissemination, Forward-Chaining and Load Balancing in DHT Based RDF Stores.

663 views • 24 slides

Conjunctive Filter: Breaking the Entropy Barrier

2010 Jan. 16@ ALENEX2010 Austin Texas. Conjunctive Filter: Breaking the Entropy Barrier. Daisuke Okanohara *1, *2 Yuichi Yoshida *1*3 *1 Preferred Infrastructure Inc. *2 Dept. of Computer Science, Univ. of Tokyo *3 School of Informatics, Kyoto University.

435 views • 23 slides

Contextual IR

Contextual IR. Naama Kraus. Slides are based on the papers: Searching with Context, Kraft, Chang, Maghoul , Kumar Context-Sensitive Query Auto-Completion, Bar- Yossef and Kraus. The Problem (recap). User queries are an imperfect description of their information needs Examples:.

427 views • 22 slides

The Lucene Search Engine

418 views • 15 slides

Models for Information Integration: Tuning Local-as-view into Global-as-view

Models for Information Integration: Tuning Local-as-view into Global-as-view. Andrea Cali Giuseppe De Giacomo Maurizio Lenzerini. Local-as-view (LAV) vs. Global-as-view (GAV). Specifying the mapping between the global schema and the sources

275 views • 10 slides