The Survey On: Data Mining Data Warehousing & OLAP

This paper gives a review of the Data mining handle. After the investigation of the way of data mining and its significance in information warehousing is included. It depicts the CRISP-DM standard now being utilized as a part of industry as the standard for an innovation impartial data mining prepare display. The paper finishes up with a noteworthy delineation of the data mining handle system and the unsolved issues that offer open doors for research. The approach is both reasonable and theoretically stable to be valuable to both scholastics and experts

Managing Information System Integration Technologies--A Study of Text Mined Industry White Papers

Industry white papers are increasingly being used to explain the philosophy and operation of a product in marketplace or technology context. This explanation is used by senior managers for strategic planning in an organization. This research explores the effectiveness of white papers and strategies for managers to learn about technologies using white papers. The research is conducted by collecting industry white papers in the area of Information System Integration and gleaned relevant information through text-mining tool, Vantage Point. The text mined information is analyzed to provide solutions for practical problems in systems integration market. The indirect findings of the research are New System Integration Business Models, Methods for Calculating ROI of System Integration Project, and Managing Implementation Failures

Analysis of Heart Disease using in Data Mining Tools Orange and Weka

Health care is an inevitable task to be done in human life Health concern business has become a notable field in the wide spread area of medical science Health care industry contains large amount of data and hidden information Effective decisions are made with this hidden information by applying patient however with data mining these tests could be reduced But there is a lack of analyzing tool according to provide effective test outcomes together with the hidden information so and such system is developed using data mining algorithms for classifying the data and to detect the heart diseases Data mining acts so a solution by many healthcare problems Na ve Bayes SVM Random Forest KNN algorithm is one such data mining method which serves with the diagnosis regarding heart diseases patient This paper analyzes few parameters and predicts heart diseases thereby suggests a heart diseases prediction system HDPS based total on the data mining approache

Case Study: A Mobile ERP to Handle Multiple Sand Mining Sites (Welithota App)

Construction industry has been growing rapidly from 2002 due to massive constructions done for rehabilitation. Further post-tsunami construction also contributed to the exponential growth of the industry from 2004. Recent mega projects including Sothern, Central and Airport highways, lotus-tower, condominium housing projects, and tourist hotels are few examples which contributed directly to the growth in the economy. However, the construction industry directly depends on supply sand and other raw materials. Hence the mining industry governs productivity in the construction industry. The main concern when it comes to the mining industry is the environmental concerns due to excessive consumption of earthy resources. The natural resources are non-renewable and require thousands of years to recreate the extracted minerals. The sustainability within the mining industry attracts major concerns as ill-management in extraction jeopardizes the nature, industry and also the economy. The Sri Lankan government has imposed Laws and By-laws by act number 33 of 1992 and established Geological Survey and Mines Bureau (GSMB) to ensure that construction raw material extraction industry functions with sustainability without compromising nature. This case study has been conducted to acknowledge how the Information Communication Technology had incorporated within the industry stakeholder; raw material mining contractors for sustainable sand mining. Miners with valid licenses were integrated with mobile based technologies to manage the day-today operations and the systematic adherence to imposed Laws. Furthermore, the paper discusses how technology has improved governance and management in technologically unattained industry. This case study resulted in the mobile based Enterprise Resource Planning (ERP) mobile application ''Welithota'', titled to be the first mobile only ERP system in Sri Lanka which works standalone without an internet access or other technological infrastructure

underground coal mine delay data analysis system

Due to the high levels of uncertainty in underground coal mining operations, delays occur regularly which inadvertently reduce the utilisation of mining equipment. In most coal mines delay data is primarily sourced from shift reports, machine monitoring and production systems. The recording process is initiated commonly at the end of a shift to ensure the correct information is recorded for the managerial decision process. An issue that the Australian coal industry faces is the lack of a standardised delay recording process and delay classification system. Tools used within the industry to analyse delay data are mostly mine specific and offer no means of comparing the mine performance to the performance at other mine sites. This paper describes a VBA based delay data analysis tool UCDelay for underground coal mines. UCDelay is an add-in Excel module for classification of delay data into a standardised form

Near Real Time Business Intelligence Framework using R Shiny

Modern industry deals with large amounts of data, which is often difficult for humans to process and use for decision making. Industry 4.0 proposes the automation of different procedures in enterprises, aiming to reduce human errors, operation time and costs. That includes analysis of different operation parameters in near real time, in order to facilitate management to make the right decisions at the right time. That requires the use of tools that are simple and fast to use and provide the necessary information. The present paper describes an architecture of a Business Intelligence system proposed for a Telecommunications software company. The system draws information from a proprietary ERP and is all developed using free open source software. The architecture proposed uses the power of R for statistical computing, data mining and artificial intelligence. Financial information is shown in a dashboard in near real time

Risk-based evaluation for underground mine planning

As underground mine planning tools become more sophisticated, mine planners have the capacity to investigate numerous mine sequencing options to identify the best strategy for a given project, creating higher value for shareholders. The information required for mine planning decisions goes beyond the external sources of uncertainty recognised by typical evaluation techniques used in the mining industry, to include technical factors (e.g. mine development layout) and the ability of a mineral extraction project to achieve planned production levels. Due to the individual characteristics that define underground mining projects, each will exhibit its individual risk profile, and thus advanced evaluation techniques must capture this information.This paper describes a Risk‐based Evaluation Methodology that accounts for financial and technical scheduling risk in the evaluation of underground mining projects. It provides decision‐makers with more information early in the mine planning cycle by combining planning and design methodologies with evaluation techniques to identify, optimise and evaluate strategies for mining extraction sequences. Standard evaluation practices used in the mining industry (Discounted Cash Flow, Real Options and Monte Carlo Simulation) are combined with the concepts of Modern Portfolio Theory to establish an evaluation methodology that recognises financial uncertainty in the context of technical scheduling factors. This paper will show that the Risk‐based Evaluation Methodology can be used at the tactical level, as it is applied in combination with the Schedule Optimisation Tool (SOT), for the purpose of recommending a materials handling system to be implemented in a mining project. For the case study, the inclusion of more information in the decision‐making process not only provides a more accurate valuation and allows for the recognition of risk, but it also alters the ultimate decision

An agent-based service oriented architecture for risk mining

University of Technology, Sydney. Faculty of Engineering and Information Technology.Risk Mining (RM) is the process of analyzing data including risk information by data mining methods, with the mining results for risk prevention. In the last few years, some researchers have proposed the combination of data mining and agent technology (agent mining) to improve the performance of data mining methodology in the heterogeneous business environments. However, problems exist for further research with the application of risk mining systems in real industry environments to enhance the robustness of system architect, dynamic business process and model accuracy etc. Therefore, in this thesis we present an Agent-based Service-oriented Risk Mining Architecture (ABSORM), which has been designed to facilitate the development of agent mining systems to address the above issues. This thesis focuses on developing the following strategies: • The integration of agent technology with web service. In this framework, we propose a new and easier method, by which the system functions are not integrated into the structure of the agents, rather modeled as distributed services and applications which are invoked by the agents acting as controllers and coordinators. Therefore, techniques developed in this framework can improve the interoperability between different modules, distribution of resources, and the lack of dependency of programming languages. • The integration of agent technology with business process management. In this work, we develop the autonomous agents that can collaborate in a business flow, which not only increases the reusability of the system, but also eases the system development in terms of re-usability of the computational resources. A group of agents solves problems in the following way: each individual agent solves the problem individually, and then interacts with each other to finalize a business process. • The integration of agent technology with ensemble learning methods. In this thesis, we are interested in developing agent-based ensemble learning strategies for risk mining: each ensemble agent individually gathers the evidence about model evaluation, and then ensembles learning methods like bagging and boosting is used to obtain prediction from the individually gathered evidence. Agent based ensemble learning can provide a critical boost to risk mining where predictive accuracy is more vital than model interpretability. The proposed architecture has been evaluated for building an online banking fraud detection system and a student risk management system. These two applications have been proved to be a sophisticated, yet user friendly, risk analysis and management tool. They are modular, interactive, dynamic and globally oriented