An auto-scoring mechanism for evaluating problem-solving ability in a web-based learning environment

The rapid development of computer and network technologies has attracted researchers to investigate strategies for and the effects of applying information technologies in learning activities; simultaneously, learning environments have been developed to record the learning portfolios of students seeking web information for problem-solving. Although previous research has demonstrated the benefits of applying information technologies to learning activities, the difficulties in doing so have also been revealed. One of the major difficulties is the lack of a mechanism to assist teachers in evaluating the problem-solving ability of the students, such that constructive suggestions can be given to the students, and tutoring strategies can be improved accordingly.     

Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

Data mining for adaptive learning sequence in English language instruction

The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).     

Effective temporal data classification by integrating sequential pattern mining and probabilistic induction

Data classification is an important topic in the field of data mining due to its wide applications. A number of related methods have been proposed based on the well-known learning models such as decision tree or neural network. Although data classification was widely discussed, relatively few studies explored the topic of temporal data classification. Most of the existing researches focused on improving the accuracy of classification by using statistical models, neural network, or distance-based methods. However, they cannot interpret the results of classification to users. In many research cases, such as gene expression of microarray, users prefer the classification information above a classifier only with a high accuracy. In this paper, we propose a novel pattern-based data mining method, namely classify-by-sequence (CBS), for classifying large temporal datasets. The main methodology behind the CBS is integrating sequential pattern mining with probabilistic induction. The CBS has the merit of simplicity in implementation and its pattern-based architecture can supply clear classification information to users. Through experimental evaluation, the CBS was shown to deliver classification results with high accuracy under two real time series datasets. In addition, we designed a simulator to evaluate the performance of CBS under datasets with different characteristics. The experimental results show that CBS can discover the hidden patterns and classify data effectively by utilizing the mined sequential patterns.     

BASE: an assistant tool to precisely simulate energy consumption and reliability of energy‐efficient storage systems

The concept of green storage in cluster computing has recently attracted enormous interest among researchers. Consequently, several energy‐efficient solutions, such as multi‐speed disks and disk spin down methods, have been proposed to conserve power in storage systems and improve disk access. Some researchers have assessed their proposed solutions via simulations, while others have used real‐world experiments. Both methods have advantages and disadvantages. Simulations can more swiftly assess the benefits of energy‐efficient solutions, but various measurement errors can arise from procedural shortcomings. For instance, many power simulation tools fail to consider how heat increases the power overhead of disk operations. Some researchers claim that their modeling methods reduce the measurement error to 5% in the single disk model. However, the demand for large‐scale storage systems is growing rapidly. Traditional power measurement using a single disk model is unsuited to such systems because of their complex storage architecture and the unpredictability of numerous disks. Consequently, a number of studies have conducted real machine experiments to assess the performance of their solutions in terms of power conservation, but such experiments are time consuming. To address this problem, this study proposes an efficient simulation tool called Benchmark Analysis Software for Energy‐efficient Solution (BASE), which can accurately estimate disks' power consumption in large‐scale storage systems. We evaluate the performance of BASE on real‐world traces of Academia Sinica (Taiwan) and Florida International University. BASE incorporates an analytical method for assessing the reliability of energy‐efficient solutions. The analytical results demonstrate that the measurement error of BASE is 2.5% lower than that achieved in real‐world experiments involving energy‐estimation experiments. Moreover, the results of simulations to assess solution reliability are identical to those obtained through real‐world experiments. Copyright © 2015 Copyright © 2015 John Wiley & Sons, Ltd.     

The indexing for one-dimensional proportionally-scaled strings

Related problems of scaled matching and indexing, which aim to determine all positions in a text T that a pattern P occurs in its scaled form, are considered important because of their applications to computer vision. However, previous results only focus on enlarged patterns, and do not allow shrunk patterns since they may disappear. In this paper, we give the definition and an efficient indexing algorithm for proportionally-scaled patterns that can be visually enlarged or shrunk. The proposed indexing algorithm takes O(|T|) time in its preprocessing phase, and achieves time in its answering phase, where |T|, |P|, Up, and m denote the length of T, the length of P, the number of reported positions, and the length of T under run-length representation, respectively.     

Flow analysis of a ribbed helix lip seal with consideration of fluid–structure interaction

Ribbed helix lip seals for rotating shafts have been widely used to retain oil and exclude contaminants in many applications throughout the industry. The objective of this study is to better understand the basic flow behavior associated with the pumping process of a ribbed helix lip seal. The theoretical model consists of a flow analysis of the lubricating film of the hydraulic fluid in conjunction with a stress analysis of the lip seal distortion. The complicated mechanical interaction between the oil flow and rubber deformation was simulated using a coupled fluid–structure approach implemented in a commercial computational fluid dynamics (CFD) code ESI-CFD, ACE+®. The flow characteristics and rubber deformation around a ribbed helix lip seal were fully resolved in a pumping-rate test environment, where both air and oil sides were filled with oil initially. The three-dimensional pressure field solved by the model via the coupled flow-stress analysis was compared with the predictions obtained from the model via the nondeformable rubber assumption to elucidate the significant effect of the fluid–structure interaction on accurate simulation of the oil pumping behavior. In the rotating speed ranging from 1000 to 6000 rpm, both measured and calculated pumping rates increase with the shaft speed for a ribbed helix lip seal. As compared to the baseline case, calculations with considering the fluid–structure interaction at higher rotary speeds can result in thicker oil films, and in turn produce greater pumping rates.     

Entropic component analysis and its application in geological data

We present an entropic component analysis for identifying key parameters or variables and the joint effects of various parameters that characterize complex systems. This approach identifies key parameters through solving the variable selection problem. It consists of two steps. First, a Bayesian approach is utilized to convert the variable selection problem into the model selection problem. Second, the model selection is achieved uniquely by evaluating the information difference of models by relative entropies of these models and a reference model. We study a geological sample classification problem, where a brine sample from Texas and Oklahoma oil field is considered, to illustrate and examine the proposed approach. The results are consistent with qualitative analysis of the lithology and quantitative discriminant function analysis. Furthermore, the proposed approach reveals the joint effects of the parameters, while it is unclear from the discriminant function analysis. The proposed approach could be thus promising to various geological data analysis.     

Developing a framework for analyzing IS sourcing

This paper develops a framework for the consideration of internal markets as an alternative to information systems (IS) outsourcing. It is based on an assessment of the pros and cons of both outsourcing and of insourcing based on the internal markets approach. It is formulated in terms of the operational, tactical, and strategic impacts of the choice among the alternatives. The framework, and the propositions that are developed from it, should be useful both for researchers, who can use it for developing testable research hypotheses, and for practitioners, who may use it as a basis for developing a comprehensive set of criteria for the evaluation of these sourcing options.