We propose to use MapReduce to quickly test new retrieval approaches on a cluster of machines by sequentially scanning all documents. We present a small case study in which we use a cluster of 15 low cost machines to search a web crawl of 0.5 billion pages showing that sequential scanning is a viable approach to running large-scale information retrieval experiments with little effort. The code is available to other researchers at:

Abstract. We propose to use MapReduce to quickly test new retrieval approaches on a cluster of machines by sequentially scanning all documents. We present a small case study in which we use a cluster of 15 low cost machines to search a web crawl of 0.5 billion pages showing that sequential scanning is a viable approach to running large-scale information retrieval experiments with little effort. The code is available to other researchers at:

This paper describes our entry into the TREC 2010 Web track. We extracted and ranked results for both last year’s and this year’s topics from the ClueWeb09 corpus using a parallel processing pipeline that avoids the generation of an inverted file. We describe the components of the parallel architecture and the pipeline and how we ran the TREC experiments, and we present effectiveness results. 1

– draft – This draft report presents preliminary results for the TREC 2010 adhoc web search task. We ran our MIREX system on 0.5 billion web documents from the ClueWeb09 crawl. On average, the system retrieves at least 3 relevant documents on the first result page containing 10 results, using a simple index consisting of anchor texts, page titles, and spam removal. 1

This draft report presents preliminary results for the TREC 2010 adhoc web search task. We ran our MIREX system on 0.5 billion web documents from the ClueWeb09 crawl. On average, the system retrieves at least 3 relevant documents on the first result page containing 10 results, using a simple index consisting of anchor texts, page titles, and spam removal. 1

This paper provides an overview of the experiments we carried out at the TREC 2010 Session Track. We propose an approach for interpreting reformulated queries by using query expansions derived from anchor logs which we envisage to be a potential alternative to query logs. We show that expansion with terms or phrases extracted from anchor logs improves the retrieval performance over a search session. We provide a detailed discussions of our runs which were among the top performing systems of the track. 1