AdMap: A Framework for Advertising using MapReduce Pipeline

Abhay Chaudhary, K R Batwada Batwada, Namita Mittal, Emmanuel S. Pilli


There is a vast collection of data for consumers due to tremendous development in digital marketing. Regular notification data allows different choices on ads and advertisement, as it applies to the operators. It involves a consumer and service data upgrade which is essential. For their ads or for consumers to validate nearby services which already are upgraded to the dataset systems, consumers are more concerned with the amount of data. Hence there is a void formed between the producer and the client. To fill that void, there is the need for a framework which can facilitate all the needs for query updating of the data. There has been work on MapReduce Informal Risk Allocation Review and Secondary Uncertainty System as well as Advertisement and selling Big Data Management services system. When data and user ads are increased in significant numbers, this leads to an improvement in service time, a significant advertisement network.


MapReduce is a practical programming model for large scale text and data analysis. The conventional MapReduce seems to have a drawback that the whole source sample size should be mounted further into the database even before evaluation might occur. Sizeable latency can be implemented when the data collection is immense. The present systems have some shortcomings, such as the construction of an application is made more difficult by a vast number of information that each time lead to decision tree-based approach. The decision tree includes several layers so that it can be dynamic, overlapping, and the estimation complexity of the decision tree can be increased with more multiple classes. Grouping the same matched data in a different node or device by clustering with a large amount of information takes time and often raises costs. A systematic solution to the automated incorporation of data into an HDFS warehouse (Hadoop File System) includes a data hub server, a generic data charging mechanism and a metadata model that together tackle the reliability of data loading, data source heterogeneity and evolution of the data warehouse design. In our model framework, the database would be able to govern the data processing schema. In the future, as a variety of data is archived, the datalake will play a critical role in managing that data. To order to carry out a planned loading function, the setup files immense catalogue move the datahub server together to attach the miscellaneous details dynamically to its schemas.


Map Reduce, Advertising, HDFS, Data Warehouse, Data Lake, Advertising and Publishing


Nichifor, B., 2014. The theoretical framework of advertising-some insights. Studies and Scientific Researches. Economics Edition, (19).

Kenyon, A.T. and Liberman, J., 2006. Controlling Cross-Border Tobacco: Advertising, Promotion and Sponsorship-Implementing the Fctc. U of Melbourne Legal Studies Research Paper, (161).

Kotler, P. and Armstrong, G., 2010. Principles of marketing. Pearson education.

Barry, T.E., 1987. The development of the hierarchy of effects: An historical perspective. Current issues and Research in Advertising, 10(1-2), pp.251-295.

Gardner, M.P., 1985. Mood states and consumer behavior: A critical review. Journal of Consumer research, 12(3), pp.281-300.

Belch, G.E. and Belch, M.A., 2003. Advertising and promotion: An integrated marketing communications perspective. The McGraw− Hill.

Holbrook, M.B. and O'Shaughnessy, J., 1984. The role of emotion in advertising. Psychology & Marketing, 1(2), pp.45-64.

Lee, E.J. and Schumann, D.W., 2004. Explaining the particular

case of incongruity in advertising: Combining classic theoretical approaches. Marketing Theory, 4(1-2), pp.59-90.

Nan, X. and Faber, R.J., 2004. Advertising theory: Reconceptualising the building blocks. Marketing Theory, 4(1-2), pp.7-30.

Petty, R.E., Cacioppo, J.T. and Schumann, D., 1983. Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of consumer research, 10(2), pp.135-146.

Popescu, I.C., 2003. „Comunicarea în marketing”, Ed. a II-a, Ed. Uranus, Bucureşti.

Smith, R.E. and Yang, X., 2004. Toward a general theory of creativity in advertising: Examining the role of divergence. Marketing theory, 4(1-2), pp.31-58.

Thorson, E. and Moore, J. eds., 2013. Integrated communication: Synergy of persuasive voices. Psychology Press.

Vakratsas, D. and Ambler, T., 1999. How advertising works: what do we really know?. Journal of marketing, 63(1), pp.26-43.

Vaughan, R., 2000. How Advertising Works: A Planning Model... putting it all together. Advertising & Society Review, 1(1).

Weilbacher, W.M., 2001. Point of view: Does advertising cause a 'hierarchy of effects'?. Journal of Advertising Research, 41(6), pp.19-26.

Dean, J. and Ghemawat, S., 2008. MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), pp.107-113.

Borthakur, D., Gray, J., Sarma, J.S., Muthukkaruppan, K., Spiegelberg, N., Kuang, H., Ranganathan, K., Molkov, D., Menon, A., Rash, S. and Schmidt, R., 2011, June. Apache hadoop goes realtime at facebook. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (pp. 1071-1080).

Jiang, D., Ooi, B.C., Shi, L. and Wu, S., 2010. The performance of mapreduce: An in-depth study. Proceedings of the VLDB Endowment, 3(1-2), pp.472-483.

Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S. and Stonebraker, M., 2009, June. A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp. 165-178).

Ghemawat, S., Gobioff, H. and Leung, S.T., 2003, October. The Google file system. In Proceedings of the nineteenth ACM symposium on Operating systems principles (pp. 29-43).

Li, B., Mazur, E., Diao, Y., McGregor, A. and Shenoy, P., 2011, June. A platform for scalable one-pass analytics using mapreduce. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (pp. 985-996).

Logothetis, D., Trezzo, C., Webb, K.C. and Yocum, K., 2011, June. In-situ MapReduce for log processing. In USENIX ATC (Vol. 11, p. 115).

Zaharia, M., Borthakur, D., Sen Sarma, J., Elmeleegy, K., Shenker, S. and Stoica, I., 2010, April. Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. In Proceedings of the 5th European conference on Computer systems (pp. 265-278).

Backman, N., Pattabiraman, K., Fonseca, R. and Cetintemel, U., 2012, June. C-MR: continuously executing MapReduce workflows on multi-core processors. In Proceedings of third international workshop on MapReduce and its Applications Date (pp. 1-8).

Kienzler, R., Bruggmann, R., Ranganathan, A. and Tatbul, N., 2012, April. Stream as you go: The case for incremental data access and processing in the cloud. In 2012 IEEE 28th International Conference on Data Engineering Workshops (pp. 159-166). IEEE.

Condie, T., Conway, N., Alvaro, P., Hellerstein, J.M., Elmeleegy, K. and Sears, R., 2010, April. sMapReduce online. In Nsdi (Vol. 10, No. 4, p. 20).

Verma, A., Cho, B., Zea, N., Gupta, I. and Campbell, R.H., 2013. Breaking the MapReduce stage barrier. Cluster computing, 16(1), pp.191-206.

Elteir, M., Lin, H. and Feng, W.C., 2010, December. Enhancing mapreduce via asynchronous data processing. In 2010 IEEE 16th International Conference on Parallel and Distributed Systems (pp. 397-405). IEEE.

Burrows, M., 2006, November. The Chubby lock service for loosely-coupled distributed systems. In Proceedings of the 7th symposium on Operating systems design and implementation (pp. 335-350).

Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A. and Gruber, R.E., 2008. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS), 26(2), pp.1-26.

Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A. and Gruber, R.E., 2008. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS), 26(2), pp.1-26.

Vernica, R., Balmin, A., Beyer, K.S. and Ercegovac, V., 2012, March. Adaptive MapReduce using situation-aware mappers. In Proceedings of the 15th International Conference on Extending Database Technology (pp. 420-431).

Guo, Z., Fox, G. and Zhou, M., 2012, June. Investigation of data locality and fairness in mapreduce. In Proceedings of third international workshop on MapReduce and its Applications Date (pp. 25-32).

Hammoud, M. and Sakr, M.F., 2011, November. Locality-aware reduce task scheduling for MapReduce. In 2011 IEEE Third International Conference on Cloud Computing Technology and Science (pp. 570-576). IEEE.

Shvachko, K., Kuang, H., Radia, S. and Chansler, R., 2010, May. The hadoop distributed file system. In 2010 IEEE 26th symposium on mass storage systems and technologies (MSST) (pp. 1-10). Ieee.



  • There are currently no refbacks.

Computer Science and Information Technologies
ISSN: 2722-323X, e-ISSN: 2722-3221

CSIT Stats

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.