research-article

What, where, and when: keyword search with spatio-temporal ranges

Authors:

Sergey Nepomnyachiy,

Tehila MinkusAuthors Info & Claims

GIR '14: Proceedings of the 8th Workshop on Geographic Information Retrieval

Article No.: 2, Pages 1 - 8

https://doi.org/10.1145/2675354.2675358

Published: 04 November 2014 Publication History

Abstract

With the adoption of timestamps and geotags on Web data, search engines are increasingly being asked questions of "where" and "when" in addition to the classic "what." In the case of Twitter, many tweets are tagged with location information as well as timestamps, creating a demand for query processors that can search both of these dimensions along with text. We propose 3W, a search framework for geo-temporal stamped documents. It exploits the structure of time-stamped data to dramatically shrink the temporal search space and uses a shallow tree based on the spatial distribution of tweets to allow speedy search over the spatial and text dimensions. Our evaluation on 30 million tweets shows that the prototype system outperforms the baseline approach that uses a monolithic index.

References

[1]

R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd ed., Addison-Wesley Professional, 2011.

Digital Library

[2]

J. L. Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM, vol. 18, no. 9, pp. 509--517, 1975.

Digital Library

[3]

R. Blanco and A. Barreiro, Tsp and cluster-based solutions to the reassignment of document identifiers, Information Retrieval, vol. 9, no. 4, pp. 499--517, 2006.

Digital Library

[4]

D. Blandford and G. Blelloch, Index compression through document reordering, in Proc of DCC, 2002.

Digital Library

[5]

M. Busch, et al, Earlybird: Real-Time Search at Twitter, in Proc of ICDE, 2012.

Digital Library

[6]

X. Cao, G. Cong and C. S. Jensen, Retrieving top-k prestige-based relevant spatial web objects, in Proc of VLDB, 2010.

Digital Library

[7]

Y. Y. Chen, T. Suel and A. Markowetz, Efficient query processing in geographic web search engines, in Proc of SIGMOD, 2006.

Digital Library

[8]

L. Chen, et al. 2013. Spatial keyword query processing: an experimental evaluation. In Proc of VLDB, 2013.

Digital Library

[9]

M. Christoforaki, et al, Text vs. space: efficient geo-search query processing, in Proc of CIKM, 2011.

Digital Library

[10]

V. Cozza, et al. 2013.Spatio-Temporal Keyword Queries in Social Networks. In collection Advances in Databases and Information Systems. Springer, Berlin. 70--83.

[11]

L. R. A. Derczynski, B. Y., and C. S. Jensen. 2013. Towards context-aware search and analysis on social media data. In Proc of EDBT, 2013.

Digital Library

[12]

S. Ding, A. Attenberg and T. Suel, Scalable techniques for document identifier assignment in inverted indexes, in Proc of WWW, 2010.

Digital Library

[13]

S. Ding and T. Suel, Faster Top-k Document Retrieval Using Block-Max Indexes, in Proc of SIGIR, 2011.

Digital Library

[14]

J. Dean, Challenges in Building Large-Scale Information Retrieval Systems (Video), Talk at WSDM 2009 Conference, 2009.

Digital Library

[15]

I. D. Felipe, V. Hristidis and N. Rishe, Keyword search on spatial databases, in ICDE, 2008.

Digital Library

[16]

F. Gey, et al, NTCIR9-GeoTime Overview - Evaluating Geographic and Temporal Search: Round 2, in Proc of NTCIR-9, 2011.

[17]

A. Guttman, R-Trees: A Dynamic Index Structure for Spatial Searching, in Proc of SIGMOD, 1984.

Digital Library

[18]

B. Han and T. Baldwin, Lexical Normalisation of Short Text Messages: Makn Sens a #twitter, in ACL 2011, 2011.

Digital Library

[19]

A. Java, et al, Why we twitter: understanding microblogging usage and communities, in Proc. of SNA-KDD, 2007.

Digital Library

[20]

M. Hadjieleftheriou, G. Kollios and V. J. G. D. Tsotras, Indexing Spatio-temporal Archives, Encyclopedia of GIS, pp. 530--538, 2008.

[21]

Lins, Lauro and Klosowski, James T and Scheidegger, Carlos. Nanocubes for real-time exploration of spatiotemporal datasets. In IEEE Transactions on Visualization and Computer Graphics, 19:12, pages 2456--2465, 2013.

Digital Library

[22]

X. Long and T. Suel, Three-Level Caching for Efficient Query Processing in Large Web Search Engines, in Proc of WWW, 2005.

Digital Library

[23]

I. Mani, et al, Introduction to the special issue on temporal information processing, in TALIP, 2004.

Digital Library

[24]

Y. Manolopoulos, A. Nanopoulos and Y. Theodoridis, R-Trees: Theory and Application, Springer, 2006.

[25]

C. D. Manning, P. Raghavan and H. Schutze, An Introduction to Information Retrieval, Cambridge University Press, 2009.

[26]

Metzler, D, et al, Improving Search Relevance for Implicitly Temporal Queries, in Proc. of SIGIR, 2009.

Digital Library

[27]

W. Shieh, et al, Inverted file compression through document identifier reassignment, Information Processing and Management, vol. 39, no. 1, pp. 117--131, 2003.

Digital Library

[28]

F. Silvestri, S. Orlando and R. Perego, Assigning Identifiers to Documents to Enhance the Clustering Property of Fulltext Indexes, in Proc of SIGIR, 2004.

Digital Library

[29]

F. Scholer, et al, Compression of inverted indexes for fast query evaluation, in Proc of SIGIR, 2002.

Digital Library

[30]

Anders Skovsgaard, Darius Sidlauskas, Christian S. Jensen 2014. Scalable top-k spatio-temporal term querying. Data Engineering (ICDE), 2014

[31]

J. Teevan, D. Ramage and M. Ringel Morris, #TwitterSearch: a comparison of microblog search and web search, in Proc of WSDM, 2011.

Digital Library

[32]

D. Papadias, et al, Indexing Spatio-Temporal Data Warehouses, in Proc of ICDE, 2002.

Digital Library

[33]

S. Vaid, C. B. Jones, H. Joho and M. Sanderson. Spatio-Textual Indexing for Geographical Search on the Web. Proceedings of 9th International Symposium on Spatial and Temporal Databases, 2005.

Digital Library

[34]

Z. Xue, D. Yin and B. Davidson, Normalizing Microtext, in Proc of AAAI, 2011.

[35]

H. Yan, S. Ding and T. Suel, Inverted Index Compression and Query Processing with Optimized Document Reordering, in Proc of WWW, 2009.

Digital Library

[36]

J. Zobel and A. Moffat, Inverted Files for Text Search Engines, Computing Surveys, vol. 38, 2006.

Digital Library

[37]

D. Zhang, et al. 2013. Scalable top-k spatial keyword search. In Proc of EDBT, 2013.

Digital Library

[38]

Y. Zhou, et al. 2005. Hybrid index structures for location-based web search. In Proc of CIKM, 2005.

Digital Library

Cited By

Bai LZhu L(2023)Keyword Query of Uncertain Spatiotemporal XML DataUncertain Spatiotemporal Data Management for the Semantic Web10.4018/978-1-6684-9108-9.ch018(395-435)Online publication date: 15-Dec-2023
https://doi.org/10.4018/978-1-6684-9108-9.ch018
Faheem MAnees THussain MDitta AAlquhayz HKhan M(2023)Indexing in WoT to Locate Indoor ThingsIEEE Access10.1109/ACCESS.2023.327269111(53497-53517)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3272691
Tang JLu XXiang YShi CGu J(2023)Blockchain search engine: Its current research status and future prospect in Internet of Things networkFuture Generation Computer Systems10.1016/j.future.2022.08.008138(120-141)Online publication date: Jan-2023
https://doi.org/10.1016/j.future.2022.08.008
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Index Terms

What, where, and when: keyword search with spatio-temporal ranges
1. Information systems
  1. Information retrieval
  2. Information storage systems

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cover image ACM Conferences

GIR '14: Proceedings of the 8th Workshop on Geographic Information Retrieval

November 2014

94 pages

ISBN:9781450331357

DOI:10.1145/2675354

Program Chairs:
Ross Purves
University of Zurich
,
Chris Jones
Cardiff University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

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Published: 04 November 2014

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SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4 - 7, 2014

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GIR '14 Paper Acceptance Rate 11 of 15 submissions, 73%;

Overall Acceptance Rate 46 of 61 submissions, 75%

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Cited By

Bai LZhu L(2023)Keyword Query of Uncertain Spatiotemporal XML DataUncertain Spatiotemporal Data Management for the Semantic Web10.4018/978-1-6684-9108-9.ch018(395-435)Online publication date: 15-Dec-2023
https://doi.org/10.4018/978-1-6684-9108-9.ch018
Faheem MAnees THussain MDitta AAlquhayz HKhan M(2023)Indexing in WoT to Locate Indoor ThingsIEEE Access10.1109/ACCESS.2023.327269111(53497-53517)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3272691
Tang JLu XXiang YShi CGu J(2023)Blockchain search engine: Its current research status and future prospect in Internet of Things networkFuture Generation Computer Systems10.1016/j.future.2022.08.008138(120-141)Online publication date: Jan-2023
https://doi.org/10.1016/j.future.2022.08.008
Xu CZhu LBai LHe J(2023)Keywords Query of uncertain spatiotemporal data based on XMLEarth Science Informatics10.1007/s12145-023-00934-816:1(241-257)Online publication date: 13-Jan-2023
https://doi.org/10.1007/s12145-023-00934-8
Xu AZhang ZMa XZhang ZXu T(2022)Spatial Concept Query Based on Lattice-TreeISPRS International Journal of Geo-Information10.3390/ijgi1105031211:5(312)Online publication date: 15-May-2022
https://doi.org/10.3390/ijgi11050312
Gryllakis FPelekis NDoulkeridis CVarlamis ITheodoridis Y(2022)Social Spatio-temporal Keyword Pattern (S²KP) Queries in Multiple Aspect Trajectories DatabasesProceedings of the 34th International Conference on Scientific and Statistical Database Management10.1145/3538712.3538716(1-12)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3538712.3538716
Ríssola EAliannejadi MCrestani F(2022)Mental disorders on online social media through the lens of language and behaviour: Analysis and visualisationInformation Processing & Management10.1016/j.ipm.2022.10289059:3(102890)Online publication date: May-2022
https://doi.org/10.1016/j.ipm.2022.102890
Chen ZChen LCong GJensen C(2021)Location- and keyword-based querying of geo-textual data: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00661-w30:4(603-640)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1007/s00778-021-00661-w
Tang JZhou ZXue XWang G(2020)Using Collaborative Edge-Cloud Cache for Search in Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2019.29463897:2(922-936)Online publication date: Feb-2020
https://doi.org/10.1109/JIOT.2019.2946389
Tang JZhou ZShu LHancke G(2019)SMPKR: Search Engine for Internet of ThingsIEEE Access10.1109/ACCESS.2019.29523907(163615-163625)Online publication date: 2019
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