Towards an automated query modification assistant

Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.Comment: 1st International Workshop on Usage Analysis and the Web of Data (USEWOD2011) in the 20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28th, 201

The Mirror DBMS at TREC-8

The database group at University of Twente participates in TREC8 using the Mirror DBMS, a prototype database system especially designed for multimedia and web retrieval. From a database perspective, the purpose has been to check whether we can get sufficient performance, and to prepare for the very large corpus track in which we plan to participate next year. From an IR perspective, the experiments have been designed to learn more about the effect of the global statistics on the ranking

The SIKS/BiGGrid Big Data Tutorial

The School for Information and Knowledge Systems SIKS and the Dutch e-science grid BiG Grid organized a new two-day tutorial on Big Data at the University of Twente on 30 November and 1 December 2011, just preceding the Dutch-Belgian Database Day. The tutorial is on top of some exciting new developments in large-scale data processing and data centers, initiated by Google, and followed by many others such as Yahoo, Amazon, Microsoft, and Facebook. The course teaches how to process terabytes of data on large clusters, and discusses several core computer science topics adapted for big data, such as new file systems (Google File System and Hadoop FS), new programming paradigms (MapReduce), new programming languages and query languages (Sawzall, Pig Latin), and new 'noSQL' databases (BigTable, Cassandra and Dynamo)

Runtime Optimizations for Prediction with Tree-Based Models

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms

The Lowlands team at TRECVID 2008

In this paper we describe our experiments performed for TRECVID 2008. We participated in the High Level Feature extraction and the Search task. For the High Level Feature extraction task we mainly installed our detection environment. In the Search task we applied our new PRFUBE ranking model together with an estimation method which estimates a vital parameter of the model, the probability of a concept occurring in relevant shots. The PRFUBE model has similarities to the well known Probabilistic Text Information Retrieval methodology and follows the Probability Ranking Principle