Paper Info
Title
Information Retrieval with Time Series Query
Abstract
We study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant documents in a text collection of the same time period, which contain topics that are correlated with the query time series. This retrieval problem arises in many text mining applications where there is a need to analyze text data in order to discover potentially causal topics. To solve this problem, we propose and study multiple retrieval algorithms that use the general idea of ranking text documents based on how well their terms are correlated with the query time series. Experiment results show that the proposed retrieval algorithm can effectively help users find documents that are relevant to the time series queries, which can help users analyze the variation patterns of the time series.
Year
DOI
Venue
2013
10.1145/2499178.2499195
ICTIR
Keywords
Field
DocType
ranking text document,retrieval task,multiple retrieval algorithm,time series,novel information retrieval problem,time period,query time series,time series query,retrieval problem,proposed retrieval algorithm,information retrieval
Query language,Human–computer information retrieval,Information retrieval,Query expansion,Computer science,Web query classification,Ranking (information retrieval),Document retrieval,Concept search,Visual Word
Conference
Citations 
PageRank 
References 
2
0.37
24
Authors
5
Name
Order
Citations
PageRank
Hyun Duk Kim11578.05
Danila Nikitin220.37
ChengXiang Zhai311908649.74
Malú Castellanos485754.71
Meichun Hsu53437778.34