Solr Integration in the Anserini Information Retrieval Toolkit
Pages 1285 - 1288
Abstract
Anserini is an open-source information retrieval toolkit built around Lucene to facilitate replicable research. In this demonstration, we examine different architectures for Solr integration in order to address two current limitations of the system: the lack of an interactive search interface and support for distributed retrieval. Two architectures are explored: In the first approach, Anserini is used as a frontend to index directly into a running Solr instance. In the second approach, Lucene indexes built directly with Anserini can be copied into a Solr installation and placed under its management. We discuss the tradeoffs associated with each architecture and report the results of a performance evaluation comparing indexing throughput. To illustrate the additional capabilities enabled by Anserini/Solr integration, we present a search interface built using the open-source Blacklight discovery interface.
References
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L. Azzopardi, M. Crane, H. Fang, G. Ingersoll, J. Lin, Y. Moshfeghi, H. Scells, P. Yang, and G. Zuccon. 2017. The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017. In SIGIR. 1429--1430.
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Index Terms
- Solr Integration in the Anserini Information Retrieval Toolkit
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Information & Contributors
Information
Published In
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
- General Chairs:
- Benjamin Piwowarski,
- Max Chevalier,
- Eric Gaussier,
- Program Chairs:
- Yoelle Maarek,
- Jian-Yun Nie,
- Falk Scholer
Copyright © 2019 ACM.
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Association for Computing Machinery
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Publication History
Published: 18 July 2019
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- Research-article
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- Natural Sciences and Engineering Research Council of Canada
- Ontario Research Fund
- Canada Foundation for Innovation Leaders Fund
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SIGIR '19
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SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 21 - 25, 2019
Paris, France
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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%
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