Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data

Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the data correspond to data stream is data stream mining. In this paper, we propose the feature selection with online decision tree. At first, we construct online type decision tree to regard credit card transaction data as data stream on data stream mining. At second, we select attributes thought to be important for detection of illegal use. We apply VFDT (Very Fast Decision Tree learner) algorithm to online type decision tree construction

Designing Traceability into Big Data Systems

Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems need to be designed from the outset to support usage of such Items across the spectrum of business use rather than from any specific application view. The design philosophy advocated in this paper is to drive the design process using a so-called description-driven approach which enriches models with meta-data and description and focuses the design process on Item re-use, thereby promoting traceability. Details are given of the description-driven design of big data systems at CERN, in health informatics and in business process management. Evidence is presented that the approach leads to design simplicity and consequent ease of management thanks to loose typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore July 2015. arXiv admin note: text overlap with arXiv:1402.5764, arXiv:1402.575

Structural Analysis of Optimal Investment Strategy with Budget Constraints for Project Management : Real Option Approach

A Dynamic Programming approach is proposed for managing projects with uncertain risks. As in Huchzermeier and Loch [3], this real option approach does not require the underlying asset of the project to be traded in a market since alternative options of the project do not have to be replicated as financial options. In Huchzermeier and Loch [3], the necessary investments are treated as exogenous functions of time and the decision is limited to whether or not the option of terminating the projects should be exercised at each time stage. This paper extends their framework substantially by incorporating the optimal investment strategy with budget constraints explicitly. Structural properties of the optimal investment strategy are investigated in detail, establishing certain monotonicity properties of the optimal project value and the optimal investment amount, as well as convexity properties of the optimal project value. Some numerical results are also presented