Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Stefan Christoph

dblp:82/4207 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
text analysis
0.112009
Detecting the origin of text segments efficiently · WWW 2009
Information retrieval › document processing › document analysis
text reuse detection
0.112009
Detecting the origin of text segments efficiently · WWW 2009
Algorithms and data structures › sequence algorithms
string algorithms
0.112009
Detecting the origin of text segments efficiently · WWW 2009
Information retrieval › similarity measure
document similarity
0.012009
Detecting the origin of text segments efficiently · WWW 2009
Information retrieval
search engines
0.012009
Detecting the origin of text segments efficiently · WWW 2009

Methods — techniques the papers use, named apart from their topics

fixed-memory algorithms · 0.2estimation techniques · 0.2
YearPublicationVenuePosition
2009 Detecting the origin of text segments efficiently
abstract
In the origin detection problem an algorithm is given a set S of documents, ordered by creation time, and a query document D. It needs to output for every consecutive sequence of k alphanumeric terms in D the earliest document in $S$ in which the sequence appeared (if such a document exists). Algorithms for the origin detection problem can, for example, be used to detect the "origin" of text segments in D and thus to detect novel content in D. They can also find the document from which the author of D has copied the most (or show that D is mostly original.) We concentrate on solutions that use only a fixed amount of memory. We propose novel algorithms for this problem and evaluate them together with a large number of previously published algorithms. Our results show that (1) detecting the origin of text segments efficiently can be done with very high accuracy even when the space used is less than 1% of the size of the documents in $S$, (2) the precision degrades smoothly with the amount of available space, (3) various estimation techniques can be used to increase the performance of the algorithms.
Ossama Abdel Hamid, Behshad Behzadi, Stefan Christoph, Monika Henzinger
WWW3