Query latency is an important performance measure of any search engines because it directly affects search users' satisfaction. The key challenge is how to efficiently retrieve top-K ranked results for a query. Current search engines process queries in either the conjunctive or disjunctive modes. However, there is still a large performance gap between these two modes since the conjunctive mode is more efficient with lower search accuracy while the disjunctive mode is more effective but requires more time to process the queries.

Top-K query processing is one of the most important problems in large-scale Information Retrieval systems. Since query processing time varies for different queries, an accurate run-time performance prediction is critical for online query scheduling and load balancing, which could eventually reduce the query waiting time and improve the throughput. Previous studies estimated the query processing time based on the combination of term-level features. Unfortunately, these features were often selected arbitrarily, and the linear combination of these features might not be able to accurately capture the complexity in the query processing.

In this paper, we describe VIRLab, a novel web-based virtual laboratory for Information Retrieval (IR). Unlike existing command line based IR toolkits, the VIRLab system provides a more interactive tool that enables easy implementation of retrieval functions with only a few lines of codes, simplified evaluation process over multiple data sets and parameter settings and straightforward result analysis interface through operational search engines and pair-wise comparisons. These features make VIRLab a unique and novel tool that can help teaching IR models, improving the productivity for doing IR model research, as well as promoting controlled experimental study of IR models.

Pseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a two-round retrieval process: the first round is to retrieve an initial set of documents relevant to an original query, and the second round is to retrieve final retrieval results using the original query expanded with terms selected from the previously retrieved documents. This two-round retrieval process is clearly time consuming, which could arguably be one of main reasons that hinder the wide adaptation of the pseudo-relevance feedback methods in real-world IR systems.