Hospitality Data Mining Myths

Electronic database handling of buisness information has gradually gained its popularity in the hospitality industry. This article provides an overview on the fundamental concepts of a hotel database and investigates the feasibility of incorporating computer-assisted data mining techniques into hospitality database applications. The author also exposes some potential myths associated with data mining in hospitaltiy database applications

Applying Classification Techniques in E-Learning System: An Overview

The aim of this paper is to provide an overview of application of data mining methods in e-learning process. E-learning is concerned with web-based learning which is totally depending upon internet. Use of data mining algorithms can help to discover the relevant information from database obtained from web based education system. This paper focused on e-learning problems to which data mining techniques have been applied, including: student’s classification based on their learning performance, detection of irregular learning behavior of students. This paper shows types of various modeling techniques used which includes: neural network, fuzzy logic, graph and trees, association rules and multi agent systems

Using Ontologies for Semantic Data Integration

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed

A Frame Work for Text Mining using Learned Information Extraction System

Text mining is a very exciting research area as it tries to discover knowledge from unstructured texts These texts can be found on a computer desktop intranets and the internet The aim of this paper is to give an overview of text mining in the contexts of its techniques application domains and the most challenging issue The Learned Information Extraction LIE is about locating specific items in natural-language documents This paper presents a framework for text mining called DTEX Discovery Text Extraction using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships The initial version of DTEX integrates an LIE module acquired by an LIE learning system and a standard rule induction module In addition rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents thereby improving the recall of the underlying extraction system Applying these techniques best results are presented to a corpus of computer job announcement postings from an Internet newsgrou

Transparent Forecasting Strategies in Database Management Systems

Whereas traditional data warehouse systems assume that data is complete or has been carefully preprocessed, increasingly more data is imprecise, incomplete, and inconsistent. This is especially true in the context of big data, where massive amount of data arrives continuously in real-time from vast data sources. Nevertheless, modern data analysis involves sophisticated statistical algorithm that go well beyond traditional BI and, additionally, is increasingly performed by non-expert users. Both trends require transparent data mining techniques that efficiently handle missing data and present a complete view of the database to the user. Time series forecasting estimates future, not yet available, data of a time series and represents one way of dealing with missing data. Moreover, it enables queries that retrieve a view of the database at any point in time - past, present, and future. This article presents an overview of forecasting techniques in database management systems. After discussing possible application areas for time series forecasting, we give a short mathematical background of the main forecasting concepts. We then outline various general strategies of integrating time series forecasting inside a database and discuss some individual techniques from the database community. We conclude this article by introducing a novel forecasting-enabled database management architecture that natively and transparently integrates forecast models

Mining protein structure data

The principal topic of this work is the application of data mining techniques, in particular of machine learning, to the discovery of knowledge in a protein database. In the first chapter a general background is presented. Namely, in section 1.1 we overview the methodology of a Data Mining project and its main algorithms. In section 1.2 an introduction to the proteins and its supporting file formats is outlined. This chapter is concluded with section 1.3 which defines that main problem we pretend to address with this work: determine if an amino acid is exposed or buried in a protein, in a discrete way (i.e.: not continuous), for five exposition levels: 2%, 10%, 20%, 25% and 30%. In the second chapter, following closely the CRISP-DM methodology, whole the process of construction the database that supported this work is presented. Namely, it is described the process of loading data from the Protein Data Bank, DSSP and SCOP. Then an initial data exploration is performed and a simple prediction model (baseline) of the relative solvent accessibility of an amino acid is introduced. It is also introduced the Data Mining Table Creator, a program developed to produce the data mining tables required for this problem. In the third chapter the results obtained are analyzed with statistical significance tests. Initially the several used classifiers (Neural Networks, C5.0, CART and Chaid) are compared and it is concluded that C5.0 is the most suitable for the problem at stake. It is also compared the influence of parameters like the amino acid information level, the amino acid window size and the SCOP class type in the accuracy of the predictive models. The fourth chapter starts with a brief revision of the literature about amino acid relative solvent accessibility. Then, we overview the main results achieved and finally discuss about possible future work. The fifth and last chapter consists of appendices. Appendix A has the schema of the database that supported this thesis. Appendix B has a set of tables with additional information. Appendix C describes the software provided in the DVD accompanying this thesis that allows the reconstruction of the present work

Smart Health Predicting System Using Data Mining

An overview of the data mining techniques with its applications, medical, and educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. Such a large amount of data cannot be processed by humans in a short time to make diagnosis, and treatment schedules. A major objective is to evaluate datamining techniques in clinical and health care applications to develop accurate decisions. It also gives a detailed discussion of medical data mining techniques which can improve various aspects of Clinical Predictions. It is a new powerful technology which is of high interest incomputer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of machine learning and database management to extract new patterns from large datasets and the knowledge associated with these patterns. The actual task is to extract data by automatic orsemi- automatic means. The different parameters included in data mining include clustering, forecasting, path analysis and predictive analysis. It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Health Prediction system is an end user support and online consultation project. Here we propose a system that allows users to get instant guidance on their health issues through an intelligent health care system online. The system is fed with various symptoms and the disease/illness associated with those systems. The system allows user to share their symptoms and issues. It then processes userssymptoms to check for various illness that could be associated with it. Here we use some intelligent data mining techniques to guess the most accurate illness that could be associated with patient’s symptoms. If the system is not able to provide suitable results, it informs the user about the type of disease or disorder it feels user’s symptoms are associated with. If users symptoms do not exactly match any disease in our database