XSACT

Autor: Ziyang Liu, Tim Meehan, Peng Sun, Stephen Booher, Yi Chen, Sivaramakrishnan Natarajan, Robert Winkler
Rok vydání: 2010
Předmět:
Zdroj: Proceedings of the VLDB Endowment. 3:1581-1584
ISSN: 2150-8097
Popis: Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results. In this demo we present a system XSACT for differentiating search results on structured data. XSACT takes as input a set of structured results, and outputs a Differentiation Feature Set (DFS) for each result to highlight their differences within a size bound. The problem of generating DFSs with maximal differences is proved to be NP-hard. XSACT adopts efficient algorithms for DFS generation, and features a user-friendly interface that effectively interacts with the users to help them compare search results.
Databáze: OpenAIRE