Yesterday, Today and Tomorrow of Big Data

Abstract Owing to the self-improvement desire, the human being always tries to reach to the current information and generate new ones from the data on hand. The practices are realized by processing and transforming the data, whose existence is broadly accepted, into information. Generating information from data is vitally important in terms of regulating the life. Especially firms need to store and transform data quickly and properly into information in order to achieve the objectives such as having a competitive edge, producing new products, moving the firm ahead and stabilizing the internal dynamics. The increase in the amount of data sources also increases the amount of the data acquired. Therefore storing and processing data become difficult and classical approaches remain incapable to do such transactions. By means of Big Data large amount of data with a wide range can be stored, managed and processed. Besides Big Data ensures proper information quickly and offers advantage and convenience to the firms, researchers and consumers by taking the properties of Volume, Value, Variety, Veracity and Velocity into consideration. This study consists of 5 parts. In the Introduction part the features, classification, the process, the areas of usage and the techniques of Big Data are explained. In the second part the appearance process and the advantages of the concept of Big Data are illustrated with examples. A detailed literature review is produced in the third part. The actual studies and the most interested areas of Big Data are told in this part. In the fourth part the future of the Big Data is evaluated. Besides the situation and distribution of the studies on Big Data in Turkey and all over the world is presented. In the Conclusion part, an overall assessment is included and probable troubles are mentioned.

[1]  Klaus Ackermann,et al.  A Resource Efficient Big Data Analysis Method for the Social Sciences: The Case of Global IP Activity , 2014, ICCS.

[2]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[3]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[4]  Muazzam Ahmed Siddiqui,et al.  Building an Arabic Sentiment Lexicon Using Semi-supervised Learning , 2014, J. King Saud Univ. Comput. Inf. Sci..

[5]  Dong-Hee Shin,et al.  Ecological views of big data: Perspectives and issues , 2015, Telematics Informatics.

[6]  Andy Blackburn,et al.  Google Flu trends , 2008 .

[7]  Samson Oluwaseun Fadiya,et al.  Advancing Big Data for Humanitarian Needs , 2014 .

[8]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[9]  Gu Jifa,et al.  Data, DIKW, Big Data and Data Science , 2014 .

[10]  Jian Pei,et al.  A spatiotemporal compression based approach for efficient big data processing on Cloud , 2014, J. Comput. Syst. Sci..

[11]  GandomiAmir,et al.  Beyond the hype , 2015 .

[12]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[13]  Caihui Liu,et al.  Hierarchical attribute reduction algorithms for big data using MapReduce , 2015, Knowl. Based Syst..

[14]  Z. Schwartz,et al.  What can big data and text analytics tell us about hotel guest experience and satisfaction , 2015 .

[15]  GaniAbdullah,et al.  The rise of "big data" on cloud computing , 2015 .

[16]  Rami Puzis,et al.  Link Prediction in Social Networks Using Computationally Efficient Topological Features , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[17]  Matthew Mohebbi,et al.  Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic , 2011, PloS one.

[18]  Aihua Li,et al.  Survey on the Applications of Big Data in Chinese Real Estate Enterprise , 2014 .

[19]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[20]  Ahmed Elragal,et al.  ERP and Big Data: The Inept Couple☆ , 2014, CENTERIS 2014.

[21]  Francisco Herrera,et al.  Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data , 2015, Fuzzy Sets Syst..

[22]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[23]  Stuart C. Gilson,et al.  Houghton Mifflin Harcourt , 2011 .

[24]  ShinDong-Hee,et al.  Ecological views of big data , 2015 .

[25]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[26]  Sean D. Young,et al.  A "big data" approach to HIV epidemiology and prevention. , 2015, Preventive medicine.

[27]  Arno Scharl,et al.  Enriching semantic knowledge bases for opinion mining in big data applications , 2014, Knowl. Based Syst..

[28]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[29]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..