A federated database system (FDBS) is a collection of cooperating database systems that are autonomous and possibly heterogeneous. In this paper, we define a reference architecture for distributed database management systems from system and schema viewpoints and show how various FDBS architectures can be developed. We then define a methodology for developing one of the popular architectures of an FDBS. Finally, we discuss critical issues related to developing and operating an FDBS.

Federated database systems for managing distributed, heterogeneous, and autonomous databases

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

/pdf/educational-data-mining-a-review-of-the-state-of-the-art-51aglgywl8.pdf

Educational Data Mining: A Review of the State of the Art

Today's business enterprises must deal with global competition, reduce the cost of doing business, and rapidly develop new services and products. To address these requirements enterprises must constantly reconsider and optimize the way they do business and change their information systems and applications to support evolving business processes. Workflow technology facilitates these by providing methodologies and software to support (i) business process modeling to capture business processes as workflow specifications, (ii) business process reengineering to optimize specified processes, and (iii) workflow automation to generate workflow implementations from workflow specifications. This paper provides a high-level overview of the current workflow management methodologies and software products. In addition, we discuss the infrastructure technologies that can address the limitations of current commercial workflow technology and extend the scope and mission of workflow management systems to support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. In particular, we discuss how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area.

http://lsdis.cs.uga.edu/library/download/GHS95.pdf

An overview of workflow management: from process modeling to workflow automation infrastructure

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analysed are multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables. The comparison for each model is done in two view points: Mean Square Error (MSE) and Mean Absolute Deviate (MAD) using real exchange daily rate values of NASDAQ Stock Exchange index.

Using artificial neural network models in stock market index prediction

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1772_bol14_INS_FS_microarray.pdf

A review of microarray datasets and applied feature selection methods

The management of multidatabase transactions presents new and interesting challenges, due mainly to the requirement of the autonomy of local database systems. In this paper, we present an extended transaction model which provides the following features useful in a multidatabase environment: (1) It allows the composition of flexi.ble transactions which can tolerate failures of individual sub transactions by taking advantage of the fact that a given function can frequently be accomplished by more than one database system; (2) It supports the concept of mixed transactions allowing compensatable and non-compensatable subtransactions to coexist within a single global transaction; and (3) It incorporates the concept of time jn both the subtransaction and global transaction processing, thus allowing more flexibility in transaction scheduling. We formally define the extended transaction model and discuss its transaction scheduling mechanism.

/pdf/a-multidatabase-transaction-model-for-interbase-13odi8bhuc.pdf

A multidatabase transaction model for InterBase

In our daily life, people often use forecasting techniques to predict weather, economy, population growth, stock, etc. However, in the real world, an event can be affected by many factors. Therefore, if we consider more factors for prediction, then we can get better forecasting results. In recent years, many researchers used fuzzy time series to handle prediction problems. In this paper, we present a new method to predict temperature and the Taiwan Futures Exchange (TAIFEX), based on the two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationships based on the historical data to increase the forecasting accuracy rate. The proposed method gets a higher forecasting accuracy rate than the existing methods.

Handling forecasting problems based on two-factors high-order fuzzy time series

Recently, many methods have been proposed for microarray data analysis. One of the challenges for microarray applications is to select a proper number of the most relevant genes for data analysis. In this paper, we propose a novel hybrid method for feature selection in microarray data analysis. This method first uses a genetic algorithm with dynamic parameter setting (GADP) to generate a number of subsets of genes and to rank the genes according to their occurrence frequencies in the gene subsets. Then, this method uses the @g^2-test for homogeneity to select a proper number of the top-ranked genes for data analysis. We use the support vector machine (SVM) to verify the efficiency of the selected genes. Six different microarray datasets are used to compare the performance of the GADP method with the existing methods. The experimental results show that the GADP method is better than the existing methods in terms of the number of selected genes and the prediction accuracy.

A novel hybrid feature selection method for microarray data analysis

The concept map proposed by Novak is a good tool to portray knowledge structure and to diagnose students' misconception in education. However, most of the learning concept maps have to be constructed through the suggestions of experts or scholars in related realm. It is really a complicated and time-consuming knowledge acquisition process. The study proposed to apply the algorithm of Apriori for Concept Map to develop an intelligent concept diagnostic system (ICDS). It provides teachers with constructed concept maps of learners rapidly, and enables teachers to diagnose the learning barriers and misconception of learners instantly. The best Remedial-Instruction Path (RIP) can be reached through the algorithm of RIP suggested in this study. Furthermore, RIP can be designed to provide remedial learning to learners. Moreover, by using statistical method, the study analyzed 245 students' data to investigate whether the learning performance of learners can be significantly enhanced after they have been guided by the RIP.

Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning

Fuzzy time series model has been successfully employed in predicting stock prices and foreign exchange rates. In this paper, we propose a new fuzzy time series model termed as distance-based fuzzy time series (DBFTS) to predict the exchange rate. Unlike the existing fuzzy time series models which require exact match of the fuzzy logic relationships (FLRs), the distance-based fuzzy time series model uses the distance between two FLRs in selecting prediction rules. To predict the exchange rate, a two factors distance-based fuzzy time series model is constructed. The first factor of the model is the exchange rate itself and the second factor comprises many candidate variables affecting the fluctuation of exchange rates. Using the exchange rate data released by the Central Bank of Taiwan, we conducted several experiments on exchange rate forecasting. The experiment results showed that the distance-based fuzzy time series outperformed the random walk model and the artificial neural network model in terms of mean square error.

Yungho Leu

Papers

A multidatabase transaction model for InterBase

Handling forecasting problems based on two-factors high-order fuzzy time series

A novel hybrid feature selection method for microarray data analysis

Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning

A distance-based fuzzy time series model for exchange rates forecasting