An Application of a Multi-Tier Data Warehouse in Oil and Gas Drilling Information Management

Expenditure on wells constitute a significant part of the operational costs for a petroleum enterprise, where most of the cost results from drilling. This has prompted drilling departments to continuously look for ways to reduce their drilling costs and be as efficient as possible. A system called the Drilling Comprehensive Information Management and Application System (DCIMAS) is developed and presented here, with an aim at collecting, storing and making full use of the valuable well data and information relating to all drilling activities and operations. The DCIMAS comprises three main parts, including a data collection and transmission system, a data warehouse (DW) management system, and an integrated platform of core applications. With the support of the application platform, the DW management system is introduced, whereby the operation data are captured at well sites and transmitted electronically to a data warehouse via transmission equipment and ETL (extract, transformation and load) tools. With the high quality of the data guaranteed, our central task is to make the best use of the operation data and information for drilling analysis and to provide further information to guide later production stages. Applications have been developed and integrated on a uniform platform to interface directly with different layers of the multi-tier DW. Now, engineers in every department spend less time on data handling and more time on applying technology in their real work with the system.

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Protecting the privacy of data in the multi-cloud is a crucial task.Data mining is a technique that protects the privacy of individual data while mining those data.The most significant task entails obtaining data from numerous remote databases.Mining algorithms can obtain sensitive information once the data is in the data warehouse.Many traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud platform.These strategies are primarily concerned with protecting the privacy of user data.This study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve infinitefields.This approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing techniques.Also,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the data.Average approximation error,computational cost,anonymizing time,and data loss are considered performance measures.The suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

Data mining of hospital characteristics in online publication of medical quality information

Information disclosure can reduce information asymmetry between health care providers and patients, thus improving both patient safety and medical quality. The National Bureau of Health Insurance (NBHI) inTaiwancurrently publishes health-related information online in order to enhance service efficiency and enable the public to monitor the country’s medical system. A data mining technique, classification and regression tree (CART), is used in this work to investigate online public quality information to compare the characteristics of hospital. The hospital quality indicators and characteristics data are available on the websites of the NBHI (http://www.nhi.gov.tw/AmountInfoWeb/Index.aspx) and the Department of Health (http://www.doh.gov.tw/). The full classification and regression tree presented in this work, grown using the hospitals’ quality medical indicators and characteristic values, classifies all hospitals into seven groups. The rate of stays longer than 30 days, which is the dependent variable in this study, is most influenced by the number of medical staff. This reflects the fact that the fewer medical staffs that are employed, the smaller the hospital is, and patients who are likely to have longer stays tend to go to the medium or large hospitals. Policy makers should work to decrease or eliminate persistent healthcare disparities among different socioeconomic groups and offer more online healthrelated services to reduce information asymmetry between health care providers and patients.

How to Apply Data Mining Technology to the Study of Agricultural Information Data Resources?

This paper makes a brief description of the definition and methods of data mining.It describes the characteristics of agricultural data(value delivery,specialization,spatio-temporal bidimensionality)and the status of application of data mining technology in agriculture.

Web-based: A data warehouse on osteoporosis data warehouse in the osteoporosis community health information management system

Objective: To establish an interactive management model for community-oriented high-risk osteoporosis in conjunction with a rural community health service center. Materials and Methods: Toward multidimensional analysis of data, the system we developed combines basic principles of data warehouse technology oriented to the needs of community health services. This paper introduces the steps we took in constructing the data warehouse;the case presented here is that of a district community health management information system in Changshu, Jiangsu Province, China. For our data warehouse, we chose the MySQL 4.5 relational database, the Browser/Server, (B/S) model, and hypertext preprocessor as the development tools. Results: The system allowed online analysis processing and next-stage work preparation, and provided a platform for data management, data query, online analysis, etc., in community health service center, specialist outpatient for osteoporosis, and health administration sectors. Conclusion: The users of remote management system and data warehouse can include community health service centers, osteoporosis departments of hospitals, and health administration departments;provide reference for policymaking of health administrators, residents’ health information, and intervention suggestions for general practitioners in community health service centers, patients’ follow-up information for osteoporosis specialists in general hospitals.

The Designing of CRM Based on Data Warehouse

Along with the rapid development of internet, CRM has become one of the most important facts leading the enterprises to be competent. At the same time, the analytical CRM based on Date Warehouse is the kernel of CRM system. This paper mainly explains the idea of CRM and the DW model of analytical CRM system.

Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

抢先赢得商机的Data Mining──基于数据仓库的数据挖掘技术

首先介绍了数据仓库以及在此技术上产生的数据挖掘技术,其次阐述了实现数据挖掘应用的几种工具以及选用工具时应遵循的原则,最后说明了数据挖掘技术现存的问题及他现在重要的商业地位。

Research on Rolling Load Distribution Method based on Data Mining

A new method of establishing rolling load distribution model was developed by online intelligent information-processing technology for plate rolling. The model combines knowledge model and mathematical model with using knowledge discovery in database （KDD） and data mining （DM） as the start. The online maintenance and optimization of the load model are realized. The effectiveness of this new method was testified by offline simulation and online application.

On Application of Big Data Mining in Earthquake Precursor Observation

Research and application of big data mining,at present,is a hot issue. This paper briefly introduces the basic ideas of big data research, analyses the necessity of big data application in earthquake precursor observation,and probes certain issues and solutions when applying this technology to work in the seismic-related domain. By doing so,we hope it can promote the innovative use of big data in earthquake precursor observation data analysis.

Integrating Data Mining Into Managerial Accounting System： Challenges and Opportunities

Data mining involves extracting information from large data sets, discovering the hidden relationships and unknown dependencies, and supporting strategic decision-making tasks. The alignment of data mining and business would bring benefits to the organization＇s management. The study investigated the adoption of data mining technologies in managerial accounting system, concentrating on the challenges and opportunities. The research showed that with the technology adoption, managerial functions could be improved and current information system could be upgraded. Since the technical progresses are reshaping the world of business and accountancy, it is significant for accountants and finance professionals to exploit information technologies.

Study on Material ManagementSystem of Coal Enterprise Basedon Data Warehouse

On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).

Data Mining Techniques and its Uses in Different Fields: A Review Paper

The paper introduced the data mining and issues related to it.Data mining is a technique by which we can extract useful knowledge from urge set of data.Data mining tasks used to perform various operations and used to solve various problems related to data mining.Data warehouse is the collection of different method and techniques used to extract useful information from raw data.Genetic algorithm is based on Darwin’s theory in which low standard chromosomes are removed from the population due to their inability to survive the process of selection.The high standard chromosomes survive and are mixed by recombination to form more appropriate individuals.In this urge amount of data is used to predict future result by following several steps.

A study on building data warehouse of hospital information system

Background Existing hospital information systems with simple statistical functions cannot meet current management needs. It is well known that hospital resources are distributed with private property rights among hospitals, such as in the case of the regional coordination of medical services. In this study, to integrate and make full use of medical data effectively, we propose a data warehouse modeling method for the hospital information system. The method can also be employed for a distributed-hospital medical service system. Methods To ensure that hospital information supports the diverse needs of health care, the framework of the hospital information system has three layers： datacenter layer, system-function layer, and user-interface layer. This paper discusses the role of a data warehouse management system in handling hospital information from the establishment of the data theme to the design of a data model to the establishment of a data warehouse. Online analytical processing tools assist user-friendly multidimensional analysis from a number of different angles to extract the required data and information. Results Use of the data warehouse improves online analytical processing and mitigates deficiencies in the decision support system. The hospital information system based on a data warehouse effectively employs statistical analysis and data mining technology to handle massive quantities of historical data, and summarizes from clinical and hospital information for decision making. Conclusions This paper proposes the use of a data warehouse for a hospital information system, specifically a data warehouse for the theme of hospital information to determine latitude, modeling and so on. The processing of patient information is given as an example that demonstrates the usefulness of this method in the case of hospital information management. Data warehouse technology is an evolving technology, and more and more decision support information extracted by data mining and with decision-making technology is required for further research.

Information mining and similarity computation for semi-/un-structured sentences from the social data

In recent years,with the development of the social Internet of Things(IoT),all kinds of data accumulated on the network.These data,which contain a lot of social information and opinions.However,these data are rarely fully analyzed,which is a major obstacle to the intelligent development of the social IoT.In this paper,we propose a sentence similarity analysis model to analyze the similarity in people’s opinions on hot topics in social media and news pages.Most of these data are unstructured or semi-structured sentences,so the accuracy of sentence similarity analysis largely determines the model’s performance.For the purpose of improving accuracy,we propose a novel method of sentence similarity computation to extract the syntactic and semantic information of the semi-structured and unstructured sentences.We mainly consider the subjects,predicates and objects of sentence pairs and use Stanford Parser to classify the dependency relation triples to calculate the syntactic and semantic similarity between two sentences.Finally,we verify the performance of the model with the Microsoft Research Paraphrase Corpus(MRPC),which consists of 4076 pairs of training sentences and 1725 pairs of test sentences,and most of the data came from the news of social data.Extensive simulations demonstrate that our method outperforms other state-of-the-art methods regarding the correlation coefficient and the mean deviation.

Alteration Information Extraction by Applying Synthesis Processing Techniques to Landsat ETM+Data: Case Study of Zhaoyuan Gold Mines, Shandong Province, China

Satellite remote sensing data are usually used to analyze the spatial distribution pattern of geological structures and generally serve as a significant means for the identification of alteration zones. Based on the Landsat Enhanced Thematic Mapper （ETM＋） data, which have better spectral resolution （8 bands） and spatial resolution （15 m in PAN band）, the synthesis processing techniques were presented to fulfill alteration information extraction： data preparation, vegetation indices and band ratios, and expert classifier-based classification. These techniques have been implemented in the MapGIS-RSP software （version 1.0）, developed by the Wuhan Zondy Cyber Technology Co., Ltd, China. In the study area application of extracting alteration information in the Zhaoyuan （招远） gold mines, Shandong （山东） Province, China, several hydorthermally altered zones （included two new sites） were found after satellite imagery interpretation coupled with field surveys. It is concluded that these synthesis processing techniques are useful approaches and are applicable to a wide range of gold-mineralized alteration information extraction.

Application and research of global grid database design based on geographic information

Energy crisis and climate change have become two seriously concerned issues universally. As a feasible solution, Global Energy Interconnection(GEI) has been highly praised and positively responded by the international community once proposed by China. From strategic conception to implementation, GEI development has entered a new phase of joint action now. Gathering and building a global grid database is a prerequisite for conducting research on GEI. Based on the requirement of global grid data management and application, combining with big data and geographic information technology, this paper studies the global grid data acquisition and analysis process, sorts out and designs the global grid database structure supporting GEI research, and builds a global grid database system.

Ubiquitous Mining with Interactive Data Mining Agents

Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed computing are becoming the important trends of modern information systems. Many applications such as security informatics and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform. Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive data mining agent - OIDM （online interactive data mining）, which provides three categories （classification, association analysis, and clustering） of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control. OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining techniques to broader areas of data intelligence and knowledge informaties.