Demo Data from VLDB 2026 / reviewers in the wild / expert
Stefan Christoph
dblp:82/4207
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
text analysis |
0.1 | 1 | 2009 | Detecting the origin of text segments efficiently · WWW 2009 |
Information retrieval › document processing › document analysis
text reuse detection |
0.1 | 1 | 2009 | Detecting the origin of text segments efficiently · WWW 2009 |
Algorithms and data structures › sequence algorithms
string algorithms |
0.1 | 1 | 2009 | Detecting the origin of text segments efficiently · WWW 2009 |
Information retrieval › similarity measure
document similarity |
0.0 | 1 | 2009 | Detecting the origin of text segments efficiently · WWW 2009 |
Information retrieval
search engines |
0.0 | 1 | 2009 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Detecting the origin of text segments efficientlyabstractIn 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 |
WWW | 3 |