Distributed Information Retrieval

1. Distributed Information Retrieval Server Ranking for Distributed Text Retrieval Systems on the Internet B. Yuwono and D. Lee Siemens TREC-4 Report: Further Experiments with Database Merging E. Vorhees Brian Shaw CS 5604

2. Issue: Merging for Effective Results • multiple brokers (take search queries), multiple collection servers • broker must select appropriate collection servers and merge results

3. Server Ranking: overview… • Problem: “cost” (including user’s time) of broadcasting to all servers and processing power • Solution: broker ranks collection servers (“goodness score”); broadcasts query to at most σ (sigma) collection servers (preset number or scoring threshold); merges results 1- Server Ranking for Distributed Text Retrieval on the Internet

4. Server Ranking: Server Selection • Relies solely on Document Frequency data (DF); all collection servers must report changes to broker • Cue Validity Variance (CVV) goodness score is based on estimate that term j distinguishes one collection server from another; not an indication of quantity or quality of relevance 1- Server Ranking for Distributed Text Retrieval on the Internet

5. Server Ranking: Merging • Assumption 1: the best document in collection i is equally relevant to the best document in collection k • A collection server containing a few but highly relevant documents will contribute to the final list. • Assumption 2: the distance between two consecutive document ranks is inversely proportional to the goodness score • Relative goodness scores are roughly proportional to the number of documents contributed to the final list. • Final ranking is a combination of goodness score and local rankings. 1- Server Ranking for Distributed Text Retrieval on the Internet

6. Experiments: (overview)… • Problem: broker has no access to meta-data from isolated collection servers • Solution: choose collection server(s) based on results from previous training queries 2- Further Experiments with Database Merging

7. Experiments: Server Selection, two approaches • Query Clustering (QC): cluster training queries (based on # of same documents retrieved) and calculate cluster “centroid vector”; compare query vector to centroid vector and assign weight to collection • Modeling Relevant Document Distributions (MRDD): find M most similar training queries and assign weights to collections based on the training run’s relevant document distribution 2- Further Experiments with Database Merging

8. Experiments: Merging • N documents retrieved from each server as determined by weights • Final ranking is a random process: roll a C-faced die that is biased by the number of documents still to be picked from each of the C collections 2- Further Experiments with Database Merging

9. Comparison

10. Conclusions • The server ranking method proposed by Yuwono and Lee is an effective way to minimize operating costs (such as time) in an environment where brokers and collection servers can share document frequency data. • The “isolated merging strategies” proposed by Vorhees is an effective way to choose a collection server where no meta-information is shared between the broker and collection server.