Implementing Graph Pattern Mining for Big Data in the Cloud

With the increasing popularity of various social networking sites, there is an explosive growth in data associated with these, so mining big data has become an important problem in the graph pattern mining research area. Graph mining helps to explore the patterns from networks or databases. Till now various graph mining techniques exist for mining frequent patterns for a graph database which contains relatively small sized graphs. But with the rapid arrival of the era of big data, traditional graph mining approaches have been unable to meet large data analysis needs. In this context, this paper proposes an adaptation to the big graph data mining approach especially in the field of social networks. The proposed approach is based on Hadoop plateform, and improves the efficiency by processing big data in distributed fashion. Again the proposed approach can be adapted to cloud environment which has the merits – load balancing, scalability and efficiency. Experiments have been conducted with real Facebook data set. The approach can be also adapted to dataset larger than experimented data. DOI: 10.17762/ijritcc2321-8169.150514

A Study of Various Privacy Preserving Data Ming Algorithms for Datasets

Privacy, security and accuracy are the major issues to be concern in field of data mining data mining when data is shared. A number of data mining algorithms are already introduced for big data when we are talking about Privacy preserving data mining. These algorithms categories data into groups. Further these groups can be used for extract useful information. Such kind of data is used in surveys, calculations etc. An election data can be considered as an example for such kind of groups. The groups are made in a, b, ab, cd category. Each group is not aware that which group has which data. In the end using data mining algorithms the desired data can be extracted

Big Data analysis for drilling and blasting in a mine in the Central Andes

Big Data applied to mining, contemplates the combi- nation of algorithms located in advanced technological tools to process a quantity of data, Power BI allows the interaction of dif- ferent data formats, for integration it has the support of Python Script. In this article, Big Data was applied to essential activities such as drilling and blasting, analyzing the parameters, standards, quantities, advances, the objective was to develop an integration system of a quantity of data for its analysis and interpretation, it will contribute to decision making in the mining operation. The development of Dashboard for interactive reportability based on indicators, will allow to visualize more efficiently and in a virtual way among the interested parties. Finally, the application of Big Data in the field of mining mainly in the treatment of its data will be the trend of the future which will allow to optimize the time and the functionality of the reports

Review the challenges of using big data in the supply chain

The increasing growth of computer networks and Internet-based technologies, followed by the growth of data and information required by their users and consumers, has led to the emergence of new concepts in this field. Big data is one of these concepts that has been considered by researchers in various fields of business in recent years. When looking at it from the outside, it is fair to assume that the more data a company or organization has, the better, because the company in question will have a larger amount of data for mining, and as a result their data will be more accurate. However, this is not always the case, because learning how to effectively manage Big Data has become a very challenging task for many businesses around the world. Working with big data involves collecting data from information sources, exploring and analyzing them, modeling them based on the desired features, and providing data security measures. For this reason, this paper examines the challenges of working with big data and the big data revolution in general and big data mining in the business supply chain as fundamental business processes