doi: 10.17586/2226-1494-2017-17-5-850-858


M. M. Zaslavskiy, E. I. Blees, S. I. Balandin

Read the full article  ';
Article in Russian

For citation: Zaslavskiy M.M., Blees E.I., Balandin S.I. Method for real time processing of open data containing geocontext markup. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 5, pp. 850–858 (in Russian). doi: 10.17586/2226-1494-2017-17-5-850-858


The paper proposes solution for the problem of real time processing and analysis interface for Open Data containing geocontext markup inside location-based services (LBS) platform. Solution method is based on providing the ability to extend a platform by the addition that implements import and mapping of several Open Data sets. This addition also should perform calculation of additional data attributes and processing of set element by set statistical indicators and clustering methods. The plug-in for Geo2Tag LBS-platform was developed for the proposed method approval. The solution determines popularity of open WiFi hot spots with the use of VK social network data. The plug-in performs import and mapping of Saint Petersburg government Open Data set and VK posts archive related to particular city districts. The mapping is performed by calculation of additional data attribute related to posts distribution - median, arithmetical mean or cluster centers by k-means clustering method. For plug-in performance evaluation, a series of experiments was performed. Analysis of experiment results showed that plug-in work time firstly depends on Open Data downloading speed because the time of Open Data processing by plug-in is an order of magnitude less than the loading time. This fact demonstrates that plug-in can perform almost real-time analysis of remote Open Data source. Developed method can be applied not only for Geo2Tag LBS-platform but also for broad set of similar systems because the solution depends only on Open Data import subsystem implementation that can be implemented on any LBS-platform. The method also gives competitive advantage for LBS-platform because it gives the possibility to extend qualitative composition of platform data by imported Open Data analysis results wherein analysis methods can be defined not only by LBS-platform administrators but also by platform users who are also the developersdue to the fact that the method is based on user’s plug-in subsystems.  

Keywords: Location-Based Services, Geo2Tag, LBS-platform, Open Data

 1.     Marr B. Big Data: 20 mind-boggling facts everyone must read. Forbes Magazine, 2015.
2.     Dey A.K. Understanding and using context. Personal and Ubiquitous Computing, 2001, vol. 5, no. 1, pp. 4–7. doi: 10.1007/s007790170019
3.     Basiri A. et al. Challenges of location-based services market analysis: current market description. Lecture Notes in Geoinformation and Cartography,2014, pp. 273–282. doi: 10.1007/978-3-319-11879-6_19
4.     Peterson M.P. (ed.) Online Maps with APIs and WebServices. Springer, 2012, 318 p. doi: 10.1007/978-3-642-27485-5
5.     Dittrich J., Quiane-Ruiz J.A. Efficient big data processing in Hadoop MapReduce.Proc. VLDB Endowment, 2012, vol. 5, no. 12, pp. 2014–2015.
6.     Zaslavskiy M.M., Balandin S.I. Method of open data import and processing in LBS-platform. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 5, pp. 816–822. (In Russian) doi: 10.17586/2226-1494-2016-16-5-816-822
7.     Abiteboul S. et al. The Lorel query language for semistructured data. International Journal on Digital Libraries, 1997, vol. 1, no. 1, pp. 68–88.
8.     Dijcks J.P. Oracle: Big data for the enterprise. Oracle White Paper, 2012.
9.     Foote K.E., Lynch M. Geographic Information Systems as an Integrating Technology: Context, Concepts, and Definitions. 1996.
10.  Weinreich A.P., Levy J.E., Barron G. Location-Based Services Platform. Patent US8447332, 2013.
11.  Nandimath J. et al. Big data analysis using Apache Hadoop. Proc. IEEE 14th Int. Conf. on Information Reuse and Integration (IRI). San Francisco, USA, 2013, pp. 700–703. doi: 10.1109/iri.2013.6642536
12.  Boy J.D., Uitermark J. How to study the city on Instagram. PloS One, 2016, vol. 11, no. 6, p. e0158161. doi: 10.1371/journal.pone.0158161
13.  Lohan E.S., Kauppinen T., Chandra Debnath S.B. A survey of people movement analytics studies in the context of smart cities. Proc. 19th FRUCT Conference. Jyväskylä, Finland, 2016. doi: 10.23919/fruct.2016.7892195
14.  Import Plugin Repository. URL: (accessed: 17.04.2017).
15.  Inaba M., Katoh N., Imai H. Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. Proc. 10th ACM Symposium on Computational Geometry.NY, 1994, pp. 332–339. doi:10.1145/177424.178042

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright 2001-2021 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.