The size of theWeb as well as user bases of search systems continue to grow exponentially. Consequently, providing subsecond query response times and high query throughput become quite challenging for large-scale information retrieval systems. Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval systems. The 8th Workshop on Large-Scale Distributed Systems for Information Retrieval (LSDS-IR'10) has provided a venue to discuss the current research challenges and identify new directions for distributed information retrieval. The workshop contained two industry talks as well as six research paper presentations. The hot topics in this year's workshop were collection selection architectures, application of MapReduce to information retrieval problems, similarity search, geographically distributed web search, and optimization techniques for search efficiency.
[1]
James P. Callan,et al.
Topic-based Index Partitions for Efficient and Effective Selective Search
,
2010,
LSDS-IR@SIGIR.
[2]
Victor Carneiro,et al.
Performance Evaluation of Large-scale Information Retrieval Systems Scaling Down
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2010,
LSDS-IR@SIGIR.
[3]
Djoerd Hiemstra,et al.
Query-Based Sampling using Snippets
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2010,
LSDS-IR@SIGIR.
[4]
Sebastian Michel,et al.
RankReduce - Processing K-Nearest Neighbor Queries on Top of MapReduce
,
2010,
LSDS-IR@SIGIR.
[5]
Craig MacDonald,et al.
Efficient Dynamic Pruning with Proximity Support
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2010,
LSDS-IR@SIGIR.
[6]
Ranieri Baraglia,et al.
Scaling Out All Pairs Similarity Search with MapReduce
,
2010,
LSDS-IR@SIGIR.