Old dogs are great at new tricks: column stores for ir prototyping

By Hannes Mühleisen, Thaer Samar, Jimmy J. Lin, A. D. Vries. 2014

31 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Retrieval & Search, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jimmy J. Lin
2. Matt Crane
3. Joel M. Mackenzie
4. Hui Fang
5. Andrew Trotman
6. Peilin Yang
7. Grant Ingersoll
8. Rodrigo Nogueira

In Information Systems

1. Jimmy J. Lin
2. Rodrigo Nogueira
3. Andrew Yates
4. Wei Yang
5. Jamie Callan
6. Kyunghyun Cho
7. Sebastian Hofstätter
8. Joel M. Mackenzie

In Information Retrieval & Search

1. Jimmy J. Lin
2. Rodrigo Nogueira
3. Andrew Yates
4. Jamie Callan
5. Wei Yang
6. Zeynep Akkalyoncu Yilmaz
7. Kyunghyun Cho
8. Sebastian Hofstätter

In Signal Processing

1. Jimmy J. Lin
2. Matt Crane
3. Joel M. Mackenzie
4. Peilin Yang
5. Andrew Trotman
6. Hui Fang
7. Grant Ingersoll
8. Rodrigo Nogueira

Citations in the corpus

• Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “• doc2query–base: the original proposal of Nogueira et al. [2019b] used a transformer model trained from scratch (i.”
• Multi-Stage Document Ranking with BERT (2019, background): “It is a well-known fact in IR that different systems implementing the same scoring function might report very different results (Mühleisen et al., 2014; Lin et al., 2016), owing to details such as tokenization, stopword selection, stemming, and parameter tuning.”
• Anserini: Enabling the Use of Lucene for Information Retrieval Research (2017, background): “[13] reported large di‚erences in e‚ectiveness across four systems that all purport to implement BM25.”
• Toward Reproducible Baselines: The Open-Source IR Reproducibility Challenge (2016, background): “For example, M¨uhleisen et al. [13] reported large differences in effectiveness across four systems that all purport to implement BM25.”
• Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format (2020, methodology): “echniques. PISA primarily uses document-ordered indexes and Document-at-aTime query evaluation. •OldDog [5] is an IR engine built using a relational database, named after the work of Mühleisen et al. [11]. Its design supports rapid prototyping through formulation of different SQL queries. •Terrier [”
• Which BM25 Do You Mean? A Large-Scale Reproducibility Study of Scoring Variants (2020, methodology): “[6] .”