Abstract
Modern mobile devices such as smartphones concentrate information from various sources that provide textual contents, mainly in the form of e-mails, short and instant messages, web documents and social network posts. While the respective apps make it especially easy to intuitively consume and create such contents, the analysis of large amounts of natural language text on mobile devices is still uncommon, although their hardware is mostly powerful enough to carry out this task. This paper presents with Android IR a first solution for effective and power-saving full-text search on Android devices. Its features and working principles are described in detail. Furthermore, the app’s performance is evaluated using real-world text documents.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Interested readers can download Android IR (16.8 MB; installation of apps from unknown sources must be allowed in security settings) from: http://www.docanalyser.de/androidir.apk.
References
Statista: Daten und Statistiken zu WhatsApp (2016). https://de.statista.com/themen/1995/whatsapp/
Novet, J.: Facebook says people sent 63 billion WhatsApp messages on New Year’s Eve (2017). http://venturebeat.com/2017/01/06/facebook-says-people-sent-63-billion-whatsapp-messages-on-new-years-eve/
Statista: Monatliches Datenvolumen des privaten Internet-Traffics in den Jahren 2014 und 2015 sowie eine Prognose bis 2020 nach Segmenten (in Petabyte) (2016). https://de.statista.com/statistik/daten/studie/152551/umfrage/prognose-zum-internet-traffic-nach-segment/
Wachsmuth, H.: Text Analysis Pipelines: Towards Ad-Hoc Large-Scale Text Mining. Springer, Cham (2006)
Tsai, F.S., et al.: Introduction to mobile information retrieval. IEEE Intell. Syst. 25(1), 11–15 (2010)
Sateli, B., Cook, G., Witte, R.: Smarter mobile apps through integrated natural language processing services. In: Mobile Web and Information Systems: 10th International Conference, MobiWIS 2013, pp. 187–202. Springer (2013)
Gaber, M.M., Stahl, F., Gomes, J.B.: Pocket Data Mining: Big Data on Small Devices. Springer, Cham (2014)
International Data Corporation: Smartphone OS Market Share, 2016 Q3 (2016). http://www.idc.com/promo/smartphone-market-share/os
Ramakrishnan, C., et al.: Layout-aware text extraction from full-text PDF of scientific articles. Sour. Code Biol. Med. 7(1), 7 (2012)
Schweda, R.: Automatische Sprachverarbeitung und Information Retrieval unter Android. Master’s thesis, FernUniversität in Hagen (2015)
Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the HLT-NAACL-06 Workshop on Textgraphs 2006, pp. 73–80. ACL, New York City (2006)
Kubek, M., Unger, H., Loauschasai, T.: A quality- and security-improved web search using local agents. Intl. J. Res. Eng. Technol. (IJRET) 1(6) (2012)
Efer, T.: Text mining with graph databases: traversal of persisted token-level representations for flexible on-demand processing. In: Autonomous Systems 2015, Fortschritt-Berichte VDI, vol. 10, no. 842, pp. 157–167. VDI-Verlag, Düsseldorf (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Kubek, M., Schweda, R., Unger, H. (2018). Android IR - Full-Text Search for Android. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-60663-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60662-0
Online ISBN: 978-3-319-60663-7
eBook Packages: EngineeringEngineering (R0)