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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Journal ArticleDOI
TL;DR: Six predictors of query performance are studied, which can be generated prior to the retrieval process without the use of relevance scores, showing that these predictors can be useful to infer query performance in practical applications.

159 citations

Proceedings ArticleDOI
Johann-Christoph Freytag1
01 Dec 1987
TL;DR: This paper describes its operations by transformation rules which generate different QEPs from initial query specifications and hopes that the approach taken will contribute to the more general goal of a modular query optimizer as part of an extensible database management system.
Abstract: The query optimizer is an important system component of a relational database management system (DBMS). It is the responsibility of this component to translate the user-submitted query - usually written in a non-procedural language - into an efficient query evaluation plan (QEP) which is then executed against the database. The research literature describes a wide variety of optimization strategies for different query languages and implementation environments. However, very little is known about how to design and structure the query optimization component to implement these strategies.This paper proposes a first step towards the design of a modular query optimizer. We describe its operations by transformation rules which generate different QEPs from initial query specifications. As we distinguish different aspects of the query optimization process, our hope is that the approach taken in this paper will contribute to the more general goal of a modular query optimizer as part of an extensible database management system.

159 citations

Proceedings ArticleDOI
16 Apr 2018
TL;DR: DeepView as mentioned in this paper is a system for automatic data visualization that tackles three problems: (1) visualization recognition: given a visualization, is it "good or "bad"? (2) visualization ranking: given two visualizations, which one is "better"? and (3) visualization selection: how to find top-k visualizations?
Abstract: Data visualization is invaluable for explaining the significance of data to people who are visually oriented. The central task of automatic data visualization is, given a dataset, to visualize its compelling stories by transforming the data (e.g., selecting attributes, grouping and binning values) and deciding the right type of visualization (e.g., bar or line charts). We present DEEPEYE, a novel system for automatic data visualization that tackles three problems: (1) Visualization recognition: given a visualization, is it "good or "bad"? (2) Visualization ranking: given two visualizations, which one is "better"? And (3) Visualization selection: given a dataset, how to find top-k visualizations? DEEPEYE addresses (1) by training a binary classifier to decide whether a particular visualization is good or bad. It solves (2) from two perspectives: (i) Machine learning: it uses a supervised learning-to-rank model to rank visualizations; and (ii) Expert rules: it relies on experts' knowledge to specify partial orders as rules. Moreover, a "boring" dataset may become interesting after data transformations (e.g., binning and grouping), which forms a large search space. We also discuss optimizations to efficiently compute top-k visualizations, for approaching (3). Extensive experiments verify the effectiveness of DEEPEYE."

159 citations

Patent
23 Apr 2004
TL;DR: In this article, a method and computer program product for determining a document relevance function for estimating a relevance score of a document in a database with respect to a query is presented. But the method is not suitable for the task of document classification.
Abstract: Provided is a method and computer program product for determining a document relevance function for estimating a relevance score of a document in a database with respect to a query. For each of a plurality of test queries, a respective set of result documents is collected. For each test query, a subset of the documents in the respective result set is selected, and a set of training relevance scores is assigned to documents in the subset. In one embodiment, at least some of the training relevance scores are assigned by human subjects who determine individual relevance scores for submitted documents with respect to the corresponding queries. Finally, a relevance function is determined based on the plurality of test queries, the subsets of documents, and the sets of training relevance scores.

159 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168