Abstract
The benefits of big-data have been proven to ensure more control over the data, adding improvements in security and complex query capabilities across many datasets. However, a problem faced by many companies, especially by small and medium-sized companies (SMEs), is to define when it is necessary to apply big-data tools. Log management becomes a relevant challenge when the volume starts to grow. This paper aims to define the benefits of applying big-data tools to dealing with log-management. In addition, it provides implementation of log-centralisation based on a cluster made up of commodity nodes for medium-volume data environments using big-data technologies. The proposed system is tested on a real study case, in particular on a medium-sized telecommunication company. The results show that the implemented system brings efficiency in storing and analysing medium-volume datasets. Furthermore, the proposed solution scales the performance based on the data size and number of nodes, providing improvements in data security, data analysis and data storage.
This work is supported by projects MTM2017-83271-R, TIN2017-84553-C2-2-R and 2016DI090.
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References
Miranskyy, A., Hamou-Lhadj, A., Cialini, E., Larsson, A.: Operational-log analysis for big-data systems: challenges and solutions. IEEE Softw. 33(2), 52–59 (2015)
Chen, M., Mao, S., Liu, Y.: Big-data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)
Xia, X.G.: Small data, mid data, and big-data versus algebra, analysis, and topology. IEEE Signal Process. Mag. 34(1), 48–51 (2017)
Ardagna, C.A., Ceravolo, P., Damiani, E.: Big-data analytics as-a-service: issues and challenges. In: IEEE International Conference on Big-Data (Big-Data), pp. 3638–3644 (2016)
Kalan, R.S., Ünalir, M.O.: Leveraging big-data technology for small and medium-sized enterprises (SMES). In: 6th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 1–6 (2016)
Chuvakin, A., Peterson, G.: How to do application logging right. IEEE Secur. Priv. 8(4), 82–85 (2010)
Anastopoulos, V., Katsikas, S.K.: A methodology for building a log-management infrastructure. In IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 301–306 (2014)
Nagappan, M., Vouk, M.A.: Abstracting log lines to log event types for mining software system logs. In: 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 114–117 (2010)
Bazhenova, E., Buelow, S., Weske, M.: Discovering decision models from event logs. In: Business Information Systems (BIS 2016), Lecture Notes in Business Information Processing, vol. 255 (2016)
Calvanese, D., Kalayci, T.E., Montali, M., Tinella, S.: Ontology-based data access for extracting event logs from legacy data: the onprom tool and methodology. In: Business Information Systems (BIS 2017), Lecture Notes in Business Information Processing, vol. 288 (2017)
Gartner Inc.: Apply IT Operations Analytics to Broader Datasets for Greater Business Insight, June (2014)
Shokri, R., Osman, M.: Leveraging big-data technology for small and medium-sized enterprises (SMEs). In: 6th International Conference on Computer and Knowledge Engineering (ICCKE 2016) (2016)
Amar, M., Lemoudden, M., El Ouahidi, B.: Log file’s centralisation to improve cloud security. In: 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech), pp. 178–183 (2016)
Sharma, S., Mangat, V.: Technology and trends to handle big-data: survey. In: Fifth International Conference on Advanced Computing & Communication Technologies, pp. 266–271 (2015)
United States Small Business Profile: Office of Advocacy, United States Small (2016). Business Administration
Muller, P., Julius, J., Herr, D., Koch, L., Peycheva, V., McKiernan, S.: Annual Report On European SMEs 2016/2017. Entrepreneurship and SMEs. European Commission, Internal Market, Industry (2017)
Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort-Martorelle, X., Reisf, M.S.: How can SMEs benefit from big-data? Challenges and a path forward. Qual. Reliab. Eng. Int. 32(6), 2151–2164 (2016)
Sena, D., Ozturkb, M., Vayvayc, O.: An overview of big-data for Growth in SMEs. In: 12th International Strategic Management Conference, ISMC 2016, 28–30 October 2016, Antalya, Turkey. Procedia - Social and Behavioral Sciences, vol. 235, pp. 159–167 (2016)
Laney, D.: 3D Data Management: Controlling Data Volume, Velocity and Variety. Technical report, META Group (2001)
Demchenko, Y., Membrey, P., Grosso, P., de Laat, C.: Addressing big-data issues in scientific data infrastructure. In: First International Symposium on Big-Data and Data Analytics in Collaboration (BDDAC 2013). Part of The 2013 International Conference on Collaboration Technologies and Systems (CTS 2013), 20–24 May, San Diego, California, USA (2013)
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da Silva, V., Giné, F., Valls, M., Tapia, D., Sarret, M. (2019). Benefits of Applying Big-Data Tools for Log-Centralisation in SMEs. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_56
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