Long queries frequently contain many extraneous terms that hinder retrieval of relevant documents. We present techniques to reduce long queries to more effective shorter ones that lack those extraneous terms. Our work is motivated by the observation that perfectly reducing long TREC description queries can lead to an average improvement of 30 % in mean average precision. Our approach involves transforming the reduction problem into a problem of learning to rank all sub-sets of the original query (sub-queries) based on their predicted quality, and select the top sub-query. We use various measures of query quality described in the literature as features to represent sub-queries, and train a classifier. Replacing the original long query with the top-ranked subquery chosen by the ranking classifier results in a statistically significant average improvement of 8 % on our test sets. Analysis of the results shows that query reduction is wellsuited for moderately-performing long queries, and a small set of query quality predictors are well-suited for the task of ranking sub-queries.

Wireless adhoc networks are excellent area for researchers with an algorithm background. In this paper we have given an algorithmic approach to the problem of routing with minimum energy consumption by the ad hoc network. we have proposed an energy optimal path algorithm used for routing in static adhoc networks using greedy approach of algorithm design. We have given the mathematical proof of correctness of proposed algorithm. We also performed simulation to show the effectiveness & correctness of our algorithm. Simulation results show that the proposed algorithm perform well in comparison to the GPSR algorithm in terms of energy, throughput & other factors.