bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Approach for Data Warehouse View Selection
Authors: Shah Biren , Ramachandran Karthik , Raghavan Vijay
Published: February 2011

Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Static selection of views may materialize certain views that are not beneficial as the data and usage trends change over time. On the contrary, dynamic selection of views works better only for queries demanding a high degree of aggregation. These facts point to the need for a technique that combines the improved response time of the static approach and the automated tuning capability of the dynamic approach. In this article, we propose a hybrid approach for the selection of materialized views. The idea is to partition the collection of all views into a static and dynamic set such that views selected for materialization from the static set are persistent over multiple query (and maintenance) windows, whereas views selected from the dynamic set can be queried and/or replaced on the fly. Highly aggregated views are selected on the fly based on the query access patterns of users, whereas the more detailed static set of views plays a significant role in the efficient maintenance of the dynamic set of views and in answering certain detailed view queries. We prove that our proposed strategy satisfies the monotonicity requirements, which is essential in order for the greedy heuristic to deliver competitive solutions. Experimental results show that our approach outperforms Dynamat, a well-known dynamic view management system that is known to outperform optimal static view selection.
A TOPSIS Data Mining Demonstration and Application to Credit Scoring
Authors: Wu Desheng , Olson David L.
Published: February 2011

The technique for order preference by similarity to ideal solution (TOPSIS) is a technique that can consider any number of measures, seeking to identify solutions close to an ideal and far from a nadir solution. TOPSIS has traditionally been applied in multiple criteria decision analysis. In this paper we propose an approach to develop a TOPSIS classifier. We demonstrate its use in credit scoring, providing a way to deal with large sets of data using machine learning. Data sets often contain many potential explanatory variables, some preferably minimized, some preferably maximized. Results are favorable by a comparison with traditional data mining techniques of decision trees. Proposed models are validated using Mont Carlo simulation.
Navigation Rules for Exploring Large Multidimensional Data Cubes
Authors: Kumar Navin , Gangopadhyay Aryya , Karabatis George , Bapna Sanjay , Chen Zhiyuan
Published: February 2011

Navigating through multidimensional data cubes is a nontrivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data through rollup, drill-down, and slicing-dicing, it offers minimal guidance to end users in the actual knowledge discovery process. In this article, we address this knowledge discovery problem by identifying novel and useful patterns concealed in multidimensional data that are used for effective exploration of data cubes. We present an algorithm for the DIscovery of Sk-NAvigation Rules (DISNAR), which discovers the hidden interesting patterns in the form of Sk-navigation rules using a test of skewness on the pairs of the current and its candidate drill-down lattice nodes. The rules then are used to enhance navigational capabilities, as illustrated by our rule-driven system. Extensive experimental analysis shows that the DISNAR algorithm discovers the interesting patterns with a high recall and precision with small execution time and low space overhead.
A Parallel Implementation Scheme of Relational Tables Based on Multidimensional Extendible Array
Authors: Azharul Hasan K. M. , Tsuji Tatsuo , Higuchi Ken
Published: February 2011

In this article, an efficient parallel implementation scheme of relational tables is proposed and evaluated. The scheme implements a relational table by employing an extendible multidimensional array. Data allocation is a key performance factor for parallel database systems. This holds especially for data warehousing environments in which huge amounts of data have to be dealt with. In our scheme, an efficient data allocation technique is used, based on the notion of extendible array. The dynamic load balancing is conducted when load on each processor is not uniformly distributed in order to maximize processor utilization.
Statistical Sampling to Instantiate Materialized View Selection Problems in Data Warehouses
Authors: Ahmed Mesbah U. , Agrawal Vikas , Nandkeolyar Udayan , Sundararaghavan P. S.
Published: February 2011

In any online decision support system, the backbone is a data warehouse. In order to facilitate rapid response to complex business decision support queries, it is a common practice to materialize an appropriate set of the views at the data warehouse. However, it typically requires the solution of the Materialized View Selection (MVS) problem to select the right set of views to materialize in order to achieve a certain level of service given a limited amount of resource such as materialization time, storage space, or view maintenance time. Dynamic changes in the source data and the end users requirement necessitate rapid and repetitive instantiation and solution of the MVS problem. In an online decision support context, time is of the essence in finding acceptable solutions to this problem. In this chapter, we have used a novel approach to instantiate and solve four versions of the MVS problem using three sampling techniques and two databases. We compared these solutions with the optimal solutions corresponding to the actual problems. In our experimentation, we found that the sampling approach resulted in substantial savings in time while producing good solutions.
GeoCache
Authors: Savary Lionel , Gardarin Georges , Zeitouni Karine
Published: February 2011

GML is a promising model for integrating geodata within data warehouses. The resulting databases are generally large and require spatial operators to be handled. Depending on the size of the target geographical data and the number and complexity of operators in a query, the processing time may quickly become prohibitive. To optimize spatial queries over GML encoded data, this chapter introduces a novel cache-based architecture. A new cache replacement policy is then proposed. It takes into account the containment properties of geographical data and predicates, and allows evicting the most irrelevant values from the cache. Experiences with the GeoCache prototype show the effectiveness of the proposed architecture with the associated replacement policy, compared to existing works.
A Look back at the PAKDD Data Mining Competition 2006
Authors: Noriel Nathaniel B. , Tan Chew Lim
Published: February 2011

The 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition involved the problem of classifying mobile telecom network customers into second generation (2G) and third generation (3G) services, with the ultimate aim of identi-fying existing 2G network customers who had a high potential of switching to using the mobile operator’s new 3G mobile network and services. This paper discusses the background behind the preparation of the dataset, the choice of judging criteria incorporating both a quantitative measure of accuracy and a set of subjective qualitative assessments, and finally a summary of the participation and results. We also highlight, in the report, interesting observations and find-ings from some of the participating teams.
Predicting Future Customers via Ensembling Gradually Expanded Trees
Authors: Yu Yang , Zhan De-Chuan , Liu Xu-Ying , Li Ming , Zhou Zhi-Hua
Published: February 2011

Our LAMDAer team has won the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition (open category) grand champion. This report presents our solution to the PAKDD 2006 Data Mining Competition. Following a brief description of the task, we discuss the difficulties of the task and explain the motivation of our solution. Then, we propose the Gradually Expanded Tree Ensemble (GetEnsemble) method, which handles the difficulties via ensembling expanded trees. We evaluated the proposed method and several other methods using AUC, and found the proposed method beats others in this task. Besides, we show how to obtain cues on which kind of second generation (2G) customers are likely to become third generation (3G) users with the proposed method.
Mining for Mutually Exclusive Items in Transaction Databases
Authors: Tzanis George , Berberidis Christos
Published: February 2011

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.
Feature Selection for the Promoter Recognition and Prediction Problem
Authors: Potamias George , Kanterakis Alexandros
Published: February 2011

With the completion of various whole genomes, one of the fundamental bioinformatics tasks is the identification of functional regulatory regions, such as promoters, and the computational discovery of genes from the produced DNA sequences. Confronted with huge amounts of DNA sequences, the utilization of automated computational sequence analysis methods and tools is more than demanding. In this article, we present an efficient feature selection to the promoter recognition, prediction, and localization problem. The whole approach is implemented in a system called MineProm. The basic idea underlying our approach is that each position-nucleotide pair in a DNA sequence is represented by a distinct binary-valued feature—the binary position base value (BPBV). A hybrid filter-wrapper, featuredeletion (or addition) algorithmic process is called for in order to select those BPBVs that best discriminate between two DNA sequences target classes (i.e., promoter vs. nonpromoter). MineProm is tested on two widely used benchmark data sets. Assessment of results demonstrates the reliability of the approach.
Algebraic and Graphic Languages for OLAP Manipulations
Authors: Ravat Franck , Teste Olivier , Tournier Ronan , Zurfluh Gilles
Published: February 2011

This article deals with multidimensional analyses. Analyzed data are designed according to a conceptual model as a constellation of facts and dimensions, which are composed of multi-hierarchies. This model supports a query algebra defining a minimal core of operators, which produce multidimensional tables for displaying analyzed data. This user-oriented algebra supports complex analyses through advanced operators and binary operators. A graphical language, based on this algebra, is also provided to ease the specification of multidimensional queries. These graphical manipulations are expressed from a constellation schema and they produce multidimensional tables.
Dynamic View Selection for OLAP
Authors: Lawrence Michael , Rau-Chaplin Andrew
Published: February 2011

In a data warehousing environment, aggregate views are often materialized in order to speed up aggregate queries of online analytical processing (OLAP). Due to the increasing size of data warehouses, it is often infeasible to materialize all views. View selection, the task of selecting a subset of views to materialize based on updates and expectations of the query load, is an important and challenging problem. In this article, we explore dynamic view selection in which the distribution of queries changes over time and the set of materialized views must be tuned by replacing some of the previously materialized views with new ones.
Ensemble PROBIT Models to Predict Cross Selling of Home Loans for Credit Card Customers
Authors: Wang Hualin , Yu Yan , Zhang Kaixia
Published: February 2011

Ensemble a set of PROBIT models to predict the likelihood of buying a home loan from the current company which has the credit card base customers. The buying rate is very low and data is very limited. This approach offers a stable and robust way to solve this extremly difficult and yet very common business problem.
Identify Cross-Selling Opportunities via Hybrid Classifier
Authors: Qiu Dahong , Wang Ye , Bi Bin
Published: February 2011

This article presents our solution to PAKDD’07 Data Mining Competition, whose task is to build a classifier to score the propensity of a credit card customer to take up a home loan with a finance company. After analyzing the task, we first describe the data preparation steps in detail. Then, a mixed resampling method is put forward to deal with the problem that model samples are redundant and class imbalance. Following that, a hybrid classifier that integrates Logistic Regression, Adaboost with Decision Stump and Voting Feature Intervals, is built. It is evaluated via cross-identification. Finally, some useful business insights gained from our solution are interpreted.
RCUBE
Authors: Dehne Frank , Eavis Todd , Rau-Chaplin Andrew
Published: February 2011

This article addresses the query performance issue for Relational OLAP (ROLAP) datacubes. We present RCUBE, a distributed multidimensional ROLAP indexing scheme which is practical to implement, requires only a small communication volume, and is fully adapted to distributed disks. Our solution is efficient for spatial searches in high dimensions and scalable in terms of data sizes, dimensions, and number of processors. Our method is also incrementally maintainable. Using “surrogate” group-bys, it allows for the efficient processing of arbitrary OLAP queries on partial cubes, where not all of the group-bys have been materialized. Our experiments with RCUBE show that the ROLAP advantage of better scalability, in comparison to MOLAP, can be maintained while providing a fast and flexible index for OLAP queries.
Vertical Fragmentation in Databases Using Data-Mining Technique
Authors: Gorla Narasimhaiah , Betty Pang W.Y.
Published: February 2011

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.
Introducing the Elasticity of Spatial Data
Authors: Gadish David A.
Published: February 2011

The data quality of a vector spatial data can be assessed using the data contained within one or more data warehouses. Spatial consistency includes topological consistency, or the conformance to topological rules (Hadzilacos & Tryfona, 1992, Rodríguez, 2005). Detection of inconsistencies in vector spatial data is an important step for improvement of spatial data quality (Redman, 1992; Veregin, 1991). An approach for detecting topo-semantic inconsistencies in vector spatial data is presented. Inconsistencies between pairs of neighboring vector spatial objects are detected by comparing relations between spatial objects to rules (Klein, 2007). A property of spatial objects, called elasticity, has been defined to measure the contribution of each of the objects to inconsistent behavior. Grouping of multiple objects, which are inconsistent with one another, based on their elasticity is proposed. The ability to detect groups of neighboring objects that are inconsistent with one another can later serve as the basis of an effort to increase the quality of spatial data sets stored in data warehouses, as well as increase the quality of results of data-mining processes
A Survey on Temporal Data Warehousing
Authors: Golfarelli Matteo , Rizzi Stefano
Published: February 2011

Data warehouses are information repositories specialized in supporting decision making. Since the decisional process typically requires an analysis of historical trends, time and its management acquire a huge importance. In this paper we consider the variety of issues, often grouped under term temporal data warehousing, implied by the need for accurately describing how information changes over time in data warehousing systems. We recognize that, with reference to a three-levels architecture, these issues can be classified into some topics, namely: handling data/schema changes in the data warehouse, handling data/schema changes in the data mart, querying temporal data, and designing temporal data warehouses. After introducing the main concepts and terminology of temporal databases, we separately survey these topics. Finally, we discuss the open research issues also in connection with their implementation on commercial tools.
A Parameterized Framework for Clustering Streams
Authors: Bhatnagar Vasudha , Kaur Sharanjit , Mignet Laurent
Published: February 2011

Clustering of data streams finds important applications in tracking evolution of various phenomena in medical, meteorological, astrophysical, seismic studies. Algorithms designed for this purpose are capable of adapting the discovered clustering model to the changes in data characteristics but are not capable of adapting to the user’s requirements themselves. Based on the previous observation, we perform a comparative study of different approaches for existing stream clustering algorithms and present a parameterized architectural framework that exploits nuances of the algorithms. This framework permits the end user to tailor a method to suit his specific application needs. We give a parameterized framework that empowers the end-users of KDD technology to build a clustering model. The framework delivers results as per the user’s application requirements. We also present two assembled algorithms G-kMeans and G-dbscan to instantiate the proposed framework and compare the performance with the existing stream clustering algorithms.
A Hybrid Method for High-Utility Itemsets Mining in Large High-Dimensional Data
Authors: Guangzhu Yu Guangzhu , Shao Shihuang , Luo Bin , Zeng Xianhui
Published: February 2011

Existing algorithms for high-utility itemsets mining are column enumeration based, adopting an Apriorilike candidate set generation-and-test approach, and thus are inadequate in datasets with high dimensions or long patterns. To solve the problem, this paper proposed a hybrid model and a row enumeration-based algorithm, i.e., Inter-transaction, to discover high-utility itemsets from two directions: an existing algorithm can be used to seek short high-utility itemsets from the bottom, while Inter-transaction can be used to seek long high-utility itemsets from the top. Inter-transaction makes full use of the characteristic that there are few common items between or among long transactions. By intersecting relevant transactions, the new algorithm can identify long high-utility itemsets, without extending short itemsets step by step. In addition, we also developed new pruning strategies and an optimization technique to improve the performance of Inter-transaction.
A Survey of Multidimensional Modeling Methodologies
Authors: Romero Oscar , Abelló Alberto
Published: February 2011

Many methodologies have been presented to support the multidimensional design of the data warehouse. First methodologies introduced were requirement-driven but the semantics of a data warehouse require to also consider data sources along the design process. In the following years, data sources gained relevance in multidimensional modeling and gave rise to several data-driven methodologies that automate the data warehouse design process from relational sources. Currently, research on multidimensional modeling is still a hot topic and we have two main research lines. On the one hand, new hybrid automatic methodologies have been introduced proposing to combine data-driven and requirement-driven approaches. On the other hand, new approaches focus on considering other kind of structured data sources that have gained relevance in the last years such as ontologies or XML. In this article we present the most relevant methodologies introduced in the literature and a detailed comparison showing main features of each approach.
A Survey of Managing the Evolution of Data Warehouses
Authors: Wrembel Robert
Published: February 2011

Methods of designing a data warehouse (DW) usually assume that its structure is static. In practice, however, a DW structure changes among others as the result of the evolution of external data sources and changes of the real world represented in a DW. The most advanced research approaches to this problem are based on temporal extensions and versioning techniques. This article surveys challenges in designing, building, and managing data warehouses whose structure and content evolve in time. The survey is based on the so-called Multiversion Data Warehouse (MVDW). In details, this article presents the following issues: the concept of the MVDW, a language for querying the MVDW, a framework for detecting changes in data sources, a structure for sharing data in the MVDW, index structures for indexing data in the MVDW.
A Survey of Parallel and Distributed Data Warehouses
Authors: Furtado Pedro
Published: February 2011

Data Warehouses are a crucial technology for current competitive organizations in the globalized world. Size, speed and distributed operation are major challenges concerning those systems. Many data warehouses have huge sizes and the requirement that queries be processed quickly and efficiently, so parallel solutions are deployed to render the necessary efficiency. Distributed operation, on the other hand, concerns global commercial and scientific organizations that need to share their data in a coherent distributed data warehouse. In this article we review the major concepts, systems and research results behind parallel and distributed data warehouses.
A Survey of Extract–Transform–Load Technology
Authors: Vassiliadis Panos
Published: February 2011

The software processes that facilitate the original loading and the periodic refreshment of the data warehouse contents are commonly known as Extraction-Transformation-Loading (ETL) processes. The intention of this survey is to present the research work in the field of ETL technology in a structured way. To this end, we organize the coverage of the field as follows: (a) first, we cover the conceptual and logical modeling of ETL processes, along with some design methods, (b) we visit each stage of the E-T-L triplet, and examine problems that fall within each of these stages, (c) we discuss problems that pertain to the entirety of an ETL process, and, (d) we review some research prototypes of academic origin.
A Survey of Spatio-Temporal Data Warehousing
Authors: Gómez Leticia , Kuijpers Bart , Moelans Bart , Vaisman Alejandro
Published: February 2011

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.
When Spatial Analysis Meets OLAP
Authors: Bimonte Sandro , Tchounikine Anne , Miquel Maryvonne , Pinet François
Published: February 2011

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.
Combining kNN Imputation and Bootstrap Calibrated
Authors: Qin Yongsong , Zhang Shichao , Zhang Chengqi
Published: February 2011

The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the kNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with kNN imputation method.
Quantifying Factors Influencing the Adoption of Internet Banking Services in Greece
Authors: Makris M. , Koumaras V. , Koumaras H. , Konstantopoulou A. , Konidis S.
Published: February 2011

A growing phenomenon in the Internet is the rising exploitation of sophisticated security means (e.g. cryptography, digital signatures etc.) toward the development of novel commerce services for providing electronic transactions, collaborating with business partners or serving customers, regardless of geographical and time limitations. This paper discusses, presents and elaborates on the various factors that affect the adaption of Internet banking services in Greece. In particular, it deals with the factors that have been developed within the framework of providing e-banking services over an insecure shared medium like the Internet and affect the Internet Banking customer acceptance. A factor analysis is performed based on the gathered results provided by customer-questionnaires of ALPHA Bank branch in Greece in order to quantify the various parameters that affect the use of an Internet Banking System. The findings of the analysis show that despite the fact that Internet Banking in Greece is steadily increasing its penetration, factors like security, ease of use and perceived usefulness of a system play a major role on the final decision of the customer to adopt an Internet Banking System.
Information Systems Innovations Adoption and Diffusion Among SMEs
Authors: Ramdani Boumediene , Lorenzo Oswaldo , Kawalek Peter
Published: February 2011

The attention of software vendors has moved recently to Small to Medium-sized Enterprises (SMEs) offering them a vast range of Information Systems’ (IS) innovations including enterprise systems (ES), which were formerly adopted by large firms only. Although the number of SMEs adopting new IS innovations has increased over time, strong empirical evidence is still lacking. This paper aims to fill this gap by reporting the findings of a survey on SMEs located in the Northwest of England. The survey results reveal that even more complex IS innovations are increasingly adopted by SMEs. Also, nearly half of the surveyed SMEs are willing to adopt ES in the next three years. These findings suggest that there is a considerable opportunity and a need for further research in the adoption and diffusion of new IS innovations among SMEs.
The Different Key Processes in the Implementation of Knowledge Management Among IC Designers, Distributors and Manufacturers
Authors: Shen Pei-Di , Lee Tsang-Hsiung , Tsai Chia-Wen , Chen Yi-Fen
Published: February 2011

This study is an exploratory investigation of the enabling roles of knowledge management for integrated circuit (IC) Designers, Distributors, and Manufacturers. This study explores the different enabling roles in terms of knowledge creation, storage/retrieval, transfer and application when businesses implement knowledge management in upstream, midstream, and downstream firms in the IC industry. Three cases, Winbond, Worldpeace, and Taiwan Semiconductor Manufacturing Company (TSMC) were studied and analyzed systemically to illustrate the findings and insights in this study. The findings in this study point out that IC designers may focus more on knowledge storage, while IC distributors pay more attention to knowledge application and IC Manufacturers emphasize knowledge creation. The necessity to implement knowledge management in the distribution industry is also emphasized in this study. Moreover, the reasons for the different enabling roles are presented in the ‘Insights from Case Studies’ section of the paper.
Costs of E-Learning Support
Authors: Lam Paul , Csete Josephine , McNaught Carmel
Published: February 2011

Understanding the costing of e-learning informs decision-making on how to support the development and implementation of teaching and learning technologies in higher education. This paper describes costings and processes in a central e-learning support service that is especially applicable to face-to-face universities that use e-learning in a blended or supplemental mode. We differentiate three types of costs: infrastructure costs that are less sensitive to variation in the complexity of e-learning strategies, and edevelopment and e-delivery costs that are directly related to the nature of the strategies used. Using actual data from a three-year e-learning support project (e3Learning) with 139 sub-projects, the paper illustrates how the calculations promote an understanding of e-learning in the following four dimensions: 1) total cost of running an e-learning support service, 2) individual costs attributable to each of the sub-projects, 3) ‘price-tags’ of e-learning strategies, and 4) initial exploration of the cost-effectiveness issue.
Adoption of E-Government Services
Authors: Sang Sinawong , Lee Jeong-Dong , Lee Jongsu
Published: February 2011

The purpose of this study is to assess and test the factors that influence user adoption of e-Government services: the Electronic Approval System (EAS). This study uses the Technology Acceptance Model (TAM), the extended TAM (TAM2), the Diffusion of Innovation (DOI), and trust to build a parsimonious yet comprehensive model of factors that influence user acceptance of the EAS. We collected data from a total of 112 public officers in 12 ministries in Cambodia. We assessed the model with regression analyses. The findings in this article show that the determinants of the model (perceived usefulness, relative advantage, and trust) explain 30.5% of the variance in user acceptance of the EAS. At the same time, image, output quality, and perceived ease of use explain 38.4% of the variance in user perception of the usefulness of the EAS. In this article, we discuss our findings, implications, and suggestions for future research.
Web Site Localization Practices
Authors: Singh Nitish , Spillan John E. , Little Joseph P.
Published: February 2011

The e-commerce industry has experienced spectacular growth, change and development. This situation has initiated an enormous business revolution that has affected the process of globalization tremendously. The goal of this study was to analyze the Web sites of localization companies that provide localization and translation services to other companies and see if they themselves are practicing what they are preaching. The results suggest that localization companies are indeed not practicing what they are preaching. Analysis shows that localization company Web sites are less localized than the Web sites of their clients, the multinational companies. The findings provide some implications to domestic and international marketers who currently operate in or are planning to enter into the global markets in the near future.
Impact of E-Adoption on Teaching and Learning in the Context of Teaching French
Authors: Joly Christèle , Iseli-Chan Nathalie
Published: February 2011

Growing use of information technology and communication (ICT) tools in language courses with communication at their core has brought opportunities as well as challenges in the predominantly conventional face-to-face context of the classroom. When the French programme in the Department of Linguistics and Modern Languages at The Chinese University of Hong Kong started to integrate an e-learning platform into all language courses, students as well as teachers showed reservations and even disbelief. However, it was unexpected to observe such an interdependent relationship between new technologies and the conventional teaching approach. In this paper, the broad implications of the e-adoption applied to learning French as a foreign language are investigated to highlight students’ learning habits and learning process. The strategies used to make technology act as a facilitator across cultures, and various ways to savoir-faire diffusion are also discussed. The study shows how new technologies modify in-class teaching, while the traditional face-to-face teaching and learning approach can influence choices in the use of different web tools that lead to blended models of education.
Wiki Interaction Tracks in Geometry Learning
Authors: Daher Wajeeh
Published: February 2011

The constant comparative method (Lincoln & Guba, 1985) was used to analyze preservice teachers’ discussions and interactions in wiki discussion sections regarding geometric lessons that were written by other preservice teachers in the year before. The data was compared for the following interaction aspects of knowledge building: dialogical actions, participants’ roles, and discussion tracks. Research shows that building their content and pedagogic content knowledge, the preservice teachers together with the lecturer used mainly proposing, asking, requesting, arguing, presenting, and moving the discussion forward as dialogical actions. Proposing and asking were used for various goals such as proposing various ideas and actions, and asking about different issues concerned with geometric content and pedagogic content knowledge. The lecturer asked questions more than the preservice teachers, while the preservice teachers proposed more than the lecturer. The knowledge building was collaborative in nature, and one important aspect which enabled the collaboration is the topology of the wiki discussion section. This topology enables presenting the content of the messages; not just the titles, where the contents are presented as having the same level and thus the same importance.
E-Business Models in B2B
Authors: Raisinghani Mahesh S. , Melemez Turan , Zou Lijie , Paslowski Chris , Kimvidze Irma , Taha Susanne , Simons Klaus
Published: February 2011

The business models in business-to-business (B2B) e-commerce and their effectiveness have been a major topic of research in recent years. Due to the variety of existing models, it seems difficult to find a widely accepted categorization that can be analyzed and assessed. An in-depth study that provides a process-based approach to B2B e-commerce is presented and illustrated with examples from industry. A comparative examination of both the buy and the sell side based on a process-related approach provides extensive insights for further comparative research and evaluation of products, services, and models. Selling services and e-procurement using Web electronic-data interchange (EDI) as submodels are clarified using real-world examples. Problems and trends in the B2B area form the conclusion of the examination with suggestions for further research.
Semiautomatic Derivation and Use of Personal Privacy Policies in E-Business
Authors: Yee George , Korba Larry
Published: February 2011

The growth of the Internet has been accompanied by the growth of Internet e-business services (e.g., electronic bookseller services, electronic stock-transaction services). This proliferation of e-business services has in turn fueled the need to protect the personal privacy of e-business users or consumers. We advocate a privacy policy approach to protecting personal privacy. However, it is evident that the specification of a personal privacy policy must be as easy as possible for the consumer. In this paper, we define the content of personal privacy policies using privacy principles that have been enacted into legislation. We then present two semiautomated approaches for the derivation of personal privacy policies. The first approach makes use of common privacy rules obtained through community consensus. This consensus can be obtained from research and/or surveys. The second approach makes use of existing privacy policies in a peer-to-peer community. We conclude the paper by explaining how personal privacy policies can be applied in e-business to protect consumer privacy.
Critical Success Factors of Web-Based E-Service
Authors: Lee Sang M. , Cata Teuta
Published: February 2011

This study focused on the adoption of Web-based applications in the insurance industry. An in-depth investigation of relevant literature on the technology adoption process and related issues, and the data collected from auto and life insurance companies identified several factors that affect e-insurance performance (in terms of both tangible and intangible benefits). Web site availability, organizational support, customer pressure, degree of business integration, an e-business plan, organization age, and organizational size were identified as critical factors for online performance. This study also revealed that insurance companies perceive more tangible and intangible benefits when they do substantial business through online sales.
On Personalizing Web Services Using Context
Authors: Maamar Zakaria , Mostefaoui Soraya Kouadri , Mahmoud Qusay H.
Published: February 2011

This paper presents a context-based approach for Web services personalization so that user preferences are accommodated. Preferences are of different types, varying from when the execution of a Web service should start to where the outcome of this execution should be delivered according to user location. Besides user preferences, it will be discussed in this paper that the computing resources on which the Web services operate have an impact on their personalization. Indeed, resources schedule the execution requests that originate from multiple Web services. To track the personalization of a Web service from a temporal perspective (i.e., what did happen, what is happening, and what will happen), three types of contexts are devised and referred to as user context, Web service context, and resource context.
Challenges for Deploying Web Services-Based E-Business Systems in SMEs
Authors: Bose Ranjit , Suumaran Vijayan
Published: February 2011

E-business initiative in many companies had started in the 1990s. These companies have recently begun to explore the use of Web Services (WS) technologies within their e-business context, since they provide an attractive, language-neutral, environment-neutral programming model that accelerates application development and integration inside and outside the enterprise. Despite these advantages, companies are slow to deploy WS because it requires a considerable shift in their application development process. While a few studies have reported on some of the reasons for this wait-and-see approach, a thorough and systematic investigation of the challenges from the stakeholders’ — providers, consumers, and standards organizations — perspective is needed. This study addresses that and provides a framework for studying the factors that impact the deployment and use of WS. The framework is used to analyze small and medium-sized enterprises (SMEs), as they play a vital role in generating employment opportunity and turnover within many major economies globally.
Process-Oriented Assessment of Web Services
Authors: Sewing Jan-Hendrik , Rosemann Michael , Dumas Marlon
Published: February 2011

Though Web services offer unique opportunities for the design of new business processes, the assessment of the potential impact of Web services is often reduced to technical aspects. This paper proposes a four-phase methodology which facilitates the evaluation of the potential use of Web services in e-business systems both from a technical and from a strategic viewpoint. It is based on business process models, which are used to frame the adoption of Web services and to assess their impact on existing business processes. The application of this methodology is described using a procurement scenario.
Usage Metering for Service-Oriented Grid Computing
Authors: Kumar Arun , Karnik Neeran , Agarwal Vikas
Published: February 2011

Currently, the grid computing community is undergoing a paradigm shift from the traditional resource-centric model to a service-oriented one. The Open Grid Services Architecture (OGSA) from the Global Grid Forum proposes that Web service-like abstractions be used to encapsulate shared grid resources for service-oriented grid computing. An important management component of such an infrastructure which is essential for its success in enterprise environments, is the metering and accounting for grid service usage. This paper explores the problem space and presents an architecture that addresses this need. We start by defining taxonomy of grid services from the perspective of usage metering, charging and business models. We discuss how service usage can be measured, aggregated and communicated in a uniform way. Finally, we report on a prototype design and implementation. Market economics-driven issues such as pricing are not discussed in this paper.
A Typology of Interorganizational Relationships
Authors: Lee Wonyoung , Aggarwal Praveen , Shin Hyonkil , Cha Taihoon , Kim Seunghan
Published: February 2011

The decreasing cost of IT has encouraged organizations to seek new ways of cooperating with members of the supply chain and other key strategic partners. This increased cooperation is giving rise to a new type of interorganizational system (IOS). Before the advent of the Internet, IS integration required significant investments on the part of organizations participating in an IOS. Such heavy investments, in turn, necessitated close strategic cooperation in the non-IS domain, as well. Thus, IS integration went hand-in-hand with non-IS (relational) integration in the pre-Internet era. However, advances in Internet technology have commoditized IS integration to a significant extent, thereby allowing the uncoupling of IS integration and relational integration. It is now possible for organizations to have IS integration without developing strong non-IS linkages. We propose a framework to account for this recent shift and present a typology for classifying interorganizational systems based on the segregation of IS and relational integration. We also verify the typology in case studies of four large firms.
Analyzing the Privacy of a Vickrey Auction Mechanism
Authors: Rodriguez Ismael , Lopez Natalia
Published: February 2011

This article studies the properties of a distributed mechanism to perform the Vickrey auction. This mechanism, which was originally presented in López, Núñez, Rodríguez, and Rubio (2004), has the main characteristic that most of the information concerning the bids is kept private for both bidders and the auctioneer without the necessity of any trusted third party. In particular, after the auction is finished, only the value of the second-highest bid and the identity of the highest bidder are publicly revealed. However, in that paper, several questions about the applicability of the protocol were left unanswered. In particular, no implementation was provided. Besides, the analysis of the collusion risk was too brief. In this paper, we address these issues in a deeper way. Let us note that, as it is stated in Brandt and Sandholm (2004), it is impossible to create a completely private mechanism to perform the Vickrey auction. In particular, we identify a gap between the proposed protocol and the complete privacy: If any n-2 bidders and the winning bidder collude, the privacy is lost. Besides, some privacy properties can be broken by chance if some specific situations appear, though the probability of this threat decreases as the number of bidders increases. In addition, we present and analyze a simple implementation of the protocol, and we consider its practical applicability.
Delivering Superior Customer Perceived Value in the Context of Network Effects
Authors: Tseng Fan-Chen , Teng Ching-I , Chiang David M.
Published: February 2011

Network effect refers to the phenomenon that the value of connecting to a network depends on the present number of customers connected to that network. The self-reinforcement characteristic of the network effect strengthens the strong firms and weakens the weak firms. In the extreme form, network effects may produce a winner-take-all market. Therefore, competitive strategies are critical for companies in markets characterized by network effects. To succeed in the market, a firm must be able to deliver superior value to customers compared to its competitors. This study proposes a conceptual model for describing the market situations characterized by network effects. Based on this model, this study clarifies how companies can deliver superior customer value in the context of network effects. This study proposes two major approaches: increasing total customer value and reducing total customer costs. Various practical methods are proposed for implementing these two approaches.