bluuebunny/crossref_metadata_2025 huggingface dataset:
Benchmarking Untrustworthiness
Authors: Neto Afonso Araújo , Vieira Marco
Published: February 2011

Benchmarking security is hard and, although there are many proposals of security metrics in the literature, no consensual quantitative security metric has been previously proposed. A key difficulty is that security is usually more influenced by what is unknown about a system than by what is known. In this paper, the authors propose the use of an untrustworthiness metric for benchmarking security. This metric, based on the idea of quantifying and exposing the trustworthiness relationship between a system and its owner, represents a powerful alternative to traditional security metrics. As an example, the authors propose a benchmark for Database Management Systems (DBMS) that can be easily used to assess and compare alternative database configurations based on minimum untrustworthiness, which is a low-cost and high-reward trust-based metric. The practical application of the benchmark in four real large database installations shows that untrustworthiness is a powerful metric for administrators to make informed security decisions by taking into account the specifics needs and characteristics of the environment being managed.
Trust Over the Net
Authors: Golan Oren
Published: February 2011

The Internet has been highly acclaimed for its innovation, financial and cultural benefits; however, a major obstacle to its expansion lies in the question of trust relations. This paper focuses on trust-building over the Internet among Israeli youth and how youth constructs trust over the Internet. The study is based on 38 in-depth interviews with adolescents ages 12-21 and an analysis of chat rooms, personal homepages, blogs and popular youth newsgroups. Findings uncover three strategies for identifying trust among youth—control over anonymity, “technological affordances” for maintaining trust, and continuity and durability of online relationships. Findings show the Internet’s potential for fostering social integration and reveal the ways youth communicate and fraternize in contemporary society.
A Mobile Service Platform for Trustworthy E-Learning Service Provisioning
Authors: Luo Zongwei , Zhang Tianle
Published: February 2011

Distant e-learning emerges as one of promising means for people to learn online. Although there is a substantial increase in computer and network performance in recent years, mainly as a result of faster hardware and more sophisticated software, there are still problems in the fields of integrating various resources towards enabling distant e-learning. Further, with the advances of technologies in RFID, sensors, GPS, GPRS, IP networks, and wireless networks, mobile learning is becoming a viable means for teaching and learning. In this book chapter, we develop a service platform for mobile learning with trustworthy service provisioning based on an organic integration of our prior research results in service grid, on demand e-learning, and trusted mobile asset tracking. In this platform, the virtual learning services for students, instructors and course providers are provided leveraging on service grid resource management capabilities on group collaboration, ubiquitous data access, and computing power. Challenges and requirements for mobile learning service platform are discussed. An RFID based e-learning data integration is proposed with integrated service networks for intelligent e-learning information access and delivery.
Trust Issues and Solutions in Multimedia Content Distribution
Authors: Lian Shiguo
Published: February 2011

Multimedia content distribution is a key technique for multimedia services, which transmits multimedia content from a sender to certain receiver(s). With the popularity of multimedia services, the trust issues in content distribution becomes urgent, including the authorized access, privacy protection, trusted payment, piracy surveillance, and so forth. This chapter introduces the trust issues in multimedia content distribution (e.g., authorization, authentication, privacy, payment, ownership, illegal distribution, forgery, etc.), reviews the latest research progress of the solutions, and presents some open issues and promising research topics. It is expected to provide valuable information to researchers or engineers in this field.
Incorporating Social Trust into Design Practices for Secure Systems
Authors: Cofta Piotr , Lacohée Hazel , Hodgson Paul
Published: February 2011

Companies are increasingly dependent on modern information and communication technology (ICT), yet the successful adoption of ICT systems stubbornly hovers at only around 50%, adding disappointment to business losses. Trust (both inter-personal and technology-related) has significant explanatory power when it comes to technology adoption, but only as part of a systematic methodology. Therefore, understanding more fully the interaction between human process and technology by adding the richness of socio-technical considerations to the design process of ICT systems should significantly improve adoption rates. At the same time, trust-based design has to demonstrate the (often neglected) business value of trust. ‘Designing for trust’, discussed in this chapter, is a design framework that consolidates trust governance and security management. Trust governance is a complete proposition that makes trust relevant to business practices, including the design and deployment of ICT systems. Trust governance incorporates the business justification of trust with an analytical framework, and a set of relevant tools and methods, as well as a maturity model. This chapter discusses how ‘designing for trust’ leverages trust governance into the design practices of ICT systems by complementing security-based methodologies, demonstrating the value of this approach.
The Use of Smart Tokens in Cleaning Integrated Warehouse Data
Authors: Ezeife Christie I. , Ohanekwu Timothy E.
Published: February 2011

Identifying integrated records that represent the same real-world object in numerous ways is just one form of data disparity (dirt) to be resolved in a data warehouse. Data cleaning is a complex process, which uses multidisciplinary techniques to resolve conflicts in data drawn from different data sources. There is a need for initial cleaning at the time a data warehouse is built, and incremental cleaning whenever new records are brought into the data warehouse during refreshing. Existing work on data cleaning have used pre-specified record match thresholds and multiple scanning of records to determine matching records in integrated data. Little attention has been paid to incremental matching of records. Determining optimal record match score threshold in a domain is hard. Also, direct long record string comparison is highly inefficient and intolerant to typing errors. Thus, this article proposes two algorithms, the first of which uses smart tokens defined from integrated records to match and identify duplicate records during initial warehouse cleaning. The second algorithm uses these tokens for fast, incremental cleaning during warehouse refreshing. Every attribute value forms either a special token like birth date or an ordinary token, which can be alphabetic, numeric, or alphanumeric. Rules are applied for forming tokens belonging to each of these four classes. These tokens are sorted and used for record match. The tokens also form very good warehouse identifiers for future faster incremental warehouse cleaning. This approach eliminates the need for match threshold and multiple passes at data. Experiments show that using tokens for record comparison produces a far better result than using the entire or greater part of a record.
A Methodology for Building XML Data Warehouses
Authors: Rusu Laura Irina , Rahayu J. Wenny , Taniar David
Published: February 2011

Developing a data warehouse for XML documents involves two major processes: one of creating it, by processing XML raw documents into a specified data warehouse repository; and the other of querying it, by applying techniques to better answer users’ queries. This paper focuses on the first part; that is identifying a systematic approach for building a data warehouse of XML documents, specifically for transferring data from an underlying XML database into a defined XML data warehouse. The proposed methodology on building XML data warehouses covers processes including data cleaning and integration, summarization, intermediate XML documents, and updating/linking existing documents and creating fact tables. In this paper, we also present a case study on how to put this methodology into practice. We utilise the XQuery technology in all of the above processes.
Discovering Frequent Embedded Subtree Patterns from Large Databases of Unordered Labeled Trees
Authors: Xiao Yongqiao , Yao J. F.
Published: February 2011

Recent years have witnessed a surge of research interest in knowledge discovery from data domains with complex structures, such as trees and graphs. In this paper, we address the problem of mining maximal frequent embedded subtrees which is motivated by such important applications as mining “hot” spots of Web sites from Web usage logs and discovering significant “deep” structures from tree-like bioinformatic data. One major challenge arises due to the fact that embedded subtrees are no longer ordinary subtrees, but preserve only part of the ancestor-descendant relationships in the original trees. To solve the embedded subtree mining problem, in this article we propose a novel algorithm, called TreeGrow, which is optimized in two important respects. First, it obtains frequency counts of root-to-leaf paths through efficient compression of trees, thereby being able to quickly grow an embedded subtree pattern path by path instead of node by node. Second, candidate subtree generation is highly localized so as to avoid unnecessary computational overhead. Experimental results on benchmark synthetic data sets have shown that our algorithm can outperform unoptimized methods by up to 20 times.
Conceptual and Systematic Design Approach for XML Document Warehouses
Authors: Nassis Vicky , Rajagopalapillai Rajugan , Dillon Tharam S. , Rahayu Wenny
Published: February 2011

EXtensible Markup Language (XML) has emerged as the dominant standard in describing and exchanging data among heterogeneous data sources. The increasing presence of large volumes of data appearing creates the need to investigate XML Document Warehouses as a means of handling the data. In this paper our focus is twofold. First we utilise Object Oriented (OO) concepts to develop and propose a conceptual design formalism to build meaningful XML Document Warehouses (XDW). This includes: (1) XML (warehouse) repository (xFACT) using OO concepts followed by the transformation of XML Schema constructs and (2) Conceptual Virtual Dimensions (VDims) using Conceptual views (Rajugan, Chang, Dillon, & Feng, 2003, 2004). Secondly we address several important outstanding issues related to our proposed design of an XML Document Warehouse. Specifically we note that the xFACT portion is now a complex structure, involving several entities and relationships as opposed to being a simple FACT table as was the case in relational data warehouses, and the notion of Virtual Dimensions (VDims) has considerably greater complexity.
Improved Data Partitioning for Building Large ROLAP Data Cubes in Parallel
Authors: Chen Ying , Dehne Frank , Eavis Todd , Rau-Chaplin A.
Published: February 2011

This paper presents an improved parallel method for generating ROLAP data cubes on a shared-nothing multiprocessor based on a novel optimized data partitioning technique. Since no shared disk is required, our method can be used for highly scalable processor clusters consisting of standard PCs with local disks only, connected via a data switch. Experiments show that our improved parallel method provides optimal, linear, speedup for at least 32 processors. The approach taken, which uses a ROLAP representation of the data cube, is well suited for large data warehouses and high dimensional data, and supports the generation of both fully materialized and partially materialized data cubes.
Discovering Surprising Instances of Simpson's Paradox in Hierarchical Multidimensional Data
Authors: Fabris Carem C. , Freitas Alex A.
Published: February 2011

This paper focuses on the discovery of surprising unexpected patterns based on a data mining method that consists of detecting instances of Simpson’s paradox. By its very nature, instances of this paradox tend to be surprising to the user. Previous work in the literature has proposed an algorithm for discovering instances of that paradox, but it addressed only flat data stored in a single relation. This work proposes a novel algorithm that considerably extends that previous work by discovering instances of Simpson’s paradox in hierarchical multidimensional data — the kind of data typically found in data warehouse and OLAP environments. Hence, the proposed algorithm can be regarded as integrating the areas of data mining and data warehousing by using an adapted data mining technique to discover surprising patterns from data warehouse and OLAP environments.
Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse
Authors: Koh Yun Sing , Rountree Nathan , O’Keefe Richard
Published: February 2011

Discovering association rules efficiently is an important data mining problem. We define sporadic rules as those with low support but high confidence; for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. To alleviate this problem, we propose a new method of discovering sporadic rules without having to produce all other rules above the minimum support threshold. The new method, called Apriori-Inverse, is a variation of the Apriori algorithm that uses the notion of maximum support instead of minimum support to generate candidate itemsets. Candidate itemsets of interest to us fall below a maximum support value but above a minimum absolute support value. Rules above maximum support are considered frequent rules, which are of no interest to us, whereas rules that occur by chance fall below the minimum absolute support value. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). This article is an expanded version of Koh and Rountree (2005).
Partially Supervised Classification
Authors: Liu Zhigang , Shi Wenzhong , Li Deren , Qin Qianqing
Published: February 2011

This paper addresses a new classification technique: Partially Supervised Classification (PSC), which is used to identify a specific land-cover class of interest from a remotely sensed image using unique training samples that belongs to a specified class. This paper also presents and discusses a newly proposed novel Support Vector Machine (SVM) algorithm for PSC. Accordingly, its training set includes labeled samples that belong to the class of interest and unlabeled samples of all classes randomly selected from a remotely sensed image. Moreover, all unlabeled samples are assumed to be training samples of other classes and each of them is assigned a weight factor indicating the likelihood of this assumption; hence, the algorithm is called ‘Weighted Unlabeled Sample SVM’ (WUS-SVM). Based on the WUS-SVM, a PSC method is proposed. Experimental results with both simulated and real datasets indicate that the proposed PSC method can achieve encouraging accuracy and is more robust than the 1-SVM and the Spectral Angle Mapping (SAM) method.
Domain-Driven Data Mining
Authors: Cao Longbing , Zhang Chengqi
Published: February 2011

Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraint-based context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business.
A Hyper-Heuristic for Descriptive Rule Induction
Authors: Pham Tho Hoan , Ho Tu Bao
Published: February 2011

There are in general three approaches to rule induction: exhaustive search, divide-and conquer, and separate-and-conquer (or its extension as weighted covering). Among them, the third approach, according to different rule search heuristics, can avoid the problem of producing many redundant rules (limitation of the first approach) or non-overlapping rules (limitation of the second approach). In this chapter, we propose a hyper-heuristic to construct rule search heuristics for weighted covering algorithms that allows producing rules of desired generality. The hyper-heuristic is based on a PN space, a new ROC-like tool for analysis, evaluation, and visualization of rules. Well-known rule search heuristics such as entropy, Laplacian, weight relative accuracy, and others are equivalent to ones proposed by the hyper-heuristic. Moreover, it can present new non-linear rule search heuristics, some are especially appropriate for description tasks. The non-linear rule search heuristics have been experimentally compared with others on the generality of rules induced from UCI datasets and used to learn regulatory rules from microarray data.
Super Computer Heterogeneous Classifier Meta-Ensembles
Authors: Bagnall Anthony , Cawley Gavin , Whittley Ian , Bull Larry , Studley Matthew , Pettipher Mike , Tekiner F.
Published: February 2011

This article describes the entry of the Super Computer Data Mining (SCDM) Project to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition. The SCDM project is developing data mining tools for parallel execution on Linux clusters. The code is freely available; please contact the first author for a copy. We combine several classifiers, some of them ensemble techniques, into a heterogeneous meta-ensemble, to produce a probability estimate for each test case. We then use a simple decision theoretic framework to form a classification. The meta-ensemble contains a Bayesian neural network, a learning classifier system (LCS), attribute selection based-ensemble algorithms (Filtered At-tribute Subspace based Bagging with Injected Randomness [FASBIR]), and more well-known classifiers such as logistic regression, Naive Bayes (NB), and C4.5.
Multi-Label Classification
Authors: Tsoumakas Grigorios , Katakis Ioannis
Published: February 2011

Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
Robust Classification Based on Correlations Between Attributes
Authors: Nanopoulos Alexandros , Papadopoulos Apostolos N. , Manolopoulos Yannis , Welzer-Druzovec Tatjana
Published: February 2011

The existence of noise in the data significantly impacts the accuracy of classification. In this article, we are concerned with the development of novel classification algorithms that can efficiently handle noise. To attain this, we recognize an analogy between k nearest neighbors (kNN) classification and user-based collaborative filtering algorithms, as they both find a neighborhood of similar past data and process its contents to make a prediction about new data. The recent development of item-based collaborative filtering algorithms, which are based on similarities between items instead of transactions, addresses the sensitivity of user-based methods against noise in recommender systems. For this reason, we focus on the item-based paradigm, compared to kNN algorithms, to provide improved robustness against noise for the problem of classification. We propose two new item-based algorithms, which are experimentally evaluated with kNN. Our results show that, in terms of precision, the proposed methods outperform kNN classification by up to 15%, whereas compared to other methods, like the C4.5 system, improvement exceeds 30%.
Semantics-Aware Advanced OLAP Visualization of Multidimensional Data Cubes
Authors: Cuzzocrea Alfredo , Sacca Domenico , Serafino Paolo
Published: February 2011

Efficiently supporting advanced OLAP visualization of multidimensional data cubes is a novel and challenging research topic, which results to be of interest for a large family of data warehouse applications relying on the management of spatio-temporal (e.g., mobile) data, scientific and statistical data, sensor network data, biological data, etc. On the other hand, the issue of visualizing multidimensional data domains has been quite neglected from the research community, since it does not belong to the well-founded conceptual-logical-physical design hierarchy inherited from relational database methodologies. Inspired from these considerations, in this article we propose an innovative advanced OLAP visualization technique that meaningfully combines (i) the so-called OLAP dimension flattening process, which allows us to extract two-dimensional OLAP views from multidimensional data cubes, and (ii) very efficient data compression techniques for such views, which allow us to generate “semantics-aware” compressed representations where data are grouped along OLAP hierarchies.
Empowering the OLAP Technology to Support Complex Dimension Hierarchies
Authors: Mansmann Svetlana , Scholl Marc H.
Published: February 2011

Comprehensive data analysis has become indispensable in a variety of domains. OLAP (On-Line Analytical Processing) systems tend to perform poorly or even fail when applied to complex data scenarios. The restriction of the underlying multidimensional data model to admit only homogeneous and balanced dimension hierarchies is too rigid for many real-world applications and, therefore, has to be overcome in order to provide adequate OLAP support. We present a framework for classifying and modeling complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. The properties of various hierarchy types are formalized and a two-phase normalization approach is proposed: heterogeneous dimensions are reshaped into a set of wellbehaved homogeneous subdimensions, followed by the enforcement of summarizability in each dimension’s data hierarchy. Mapping the data to a visual data browser relies solely on metadata, which captures the properties of facts, dimensions, and relationships within the dimensions. The navigation is schema-based, that is, users interact with dimensional levels with on-demand data display. The power of our approach is exemplified using a real-world study from the domain of academic administration.
Acquiring Semantic Sibling Associations from Web Documents
Authors: Brunzel Marko , Spiliopoulou Myra
Published: February 2011

The automated discovery of relationships among terms contributes to the automation of the ontology engineering process and allows for sophisticated query expansion in information retrieval. While there are many findings on the identification of direct hierarchical relations among concepts, less attention has been paid on the discovery sibling terms. These are terms that share a common, a priori unknown parent such as co-hyponyms and co-meronyms. In this study, we present our results on the discovery of pairs or groups of sibling terms with XTREEM-SA (Xhtml TREE mining for sibling associations), an algorithm that extracts semantics from Web documents. While conventional methods process an appropriately prepared corpus, XTREEM-SA takes as input an arbitrary collection of Web documents on a given topic and finds sibling relations between terms in this corpus. It is thus independent of domain and language, does not require linguistic preprocessing, and does not rely on syntactic or other rules on text formation. We describe XTREEM-SA and evaluate it toward two reference ontologies. In this context, we also elaborate on the challenges of evaluating semantics extracted from the Web against handcrafted ontologies of high quality but possibly low coverage.
An Information-Theoretic Framework for Process Structure and Data Mining
Authors: Greco Gianluigi , Guzzo Antonella , Pontieri Luigi
Published: February 2011

Mining process logs has been increasingly attracting the data mining community, due to the chances the development of process mining techniques can offer to the analysis and design of complex processes. Currently, these techniques focus on “structural” aspects by only considering which activities were executed and in which order, and disregard any other kind of data usually kept by real systems (e.g., activity executors, parameter values, and time-stamps). In this article, we aim at discovering different process variants by clustering process logs. To this purpose, an information-theoretic framework is used to simultaneously cluster the logged process traces, encoding structural information, as well as a number of performance metrics associated with them. Each cluster is equipped with a specific model, so providing the analyst with a compact and handy description of major execution scenarios for the process.
Medical Document Clustering Using Ontology-Based Term Similarity Measures
Authors: Zhang Xiaodan , Jing Liping , Hu Xiaohua , Ng Michael , Jiangxi Jiali Xia , Zhou Xiaohua
Published: February 2011

Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this article, we conduct a comparative study on how different term semantic similarity measures including path-based, information-content-based and feature-based similarity measure affect document clustering. Term re-weighting of document vector is an important method to integrate domain ontology to clustering process. In detail, the weight of a term is augmented by the weights of its cooccurred concepts. Spherical k-means are used for evaluate document vector re-weighting on two real-world datasets: Disease10 and OHSUMED23. Experimental results on nine different semantic measures have shown that: (1) there is no certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.
Classification of Imbalanced Data with Random sets and Mean-Variance Filtering
Authors: Nikulin Vladimir
Published: February 2011

Imbalanced data represent a significant problem because the corresponding classifier has a tendency to ignore patterns which have smaller representation in the training set. We propose to consider a large number of balanced training subsets where representatives from the larger pattern are selected randomly. As an outcome, the system will produce a matrix of linear regression coefficients where rows represent random subsets and columns represent features. Based on the above matrix we make an assessment of the stability of the influence of the particular features. It is proposed to keep in the model only features with stable influence. The final model represents an average of the single models, which are not necessarily a linear regression. The above model had proven to be efficient and competitive during the PAKDD-2007 Data Mining Competition.
Automated Integration of Heterogeneous Data Warehouse Schemas
Authors: Banek Marko , Vrdoljak Boris , Tjoa A Min , Skocir Zoran
Published: February 2011

A federated data warehouse is a logical integration of data warehouses applicable when physical integration is impossible due to privacy policy or legal restrictions. In healthcare systems federated data warehouses are a most feasible source of data for deducing guidelines for evidence-based medicine based on data material from different participating institutions. In order to enable the translation of queries in a federated approach, schemas of the federated warehouse and the local warehouses must be matched. In this paper we present a procedure that enables the matching process for schema structures specific to the multidimensional model of data warehouses: facts, measures, dimensions, aggregation levels and dimensional attributes. Similarities between warehouse-specific structures are computed by using linguistic and structural comparison. The calculated values are used to create necessary mappings.
Computing Join Aggregates Over Private Tables
Authors: She Rong , Want Ke , Fu Ada Waichee , Xu Yabo
Published: February 2011

We propose a privacy-preserving protocol for computing aggregation queries over the join of private tables. In this problem, several parties wish to share aggregated information over the join of their tables, but want to conceal the details that generate such information. The join operation presents a challenge to privacy preservation because it requires matching individual records from private tables without letting any non-owning party know the actual join values or make any inference about the data in other parties??. We solve this problem by using a novel private sketching protocol that securely exchanges some randomized summary information about private tables. This protocol (1) conceals individual private values and their distributions from all non-owning parties, (2) works on many general forms of aggregation functions, (3) handles group-by aggregates, and (4) handles roll-up/drill-down operations. Previous works have not provided this level of privacy for such queries
Effectiveness of Fuzzy Classifier Rules in Capturing Correlations between Genes
Authors: Khabbaz Mohammad , Kianmehr Keivan , Alshalalfa Mohammad , Alhajj Reda
Published: February 2011

In this article, we take advantage of using fuzzy classifier rules to capture the correlations between genes. The main motivation to conduct this study is that a fuzzy classifier rule is essentially an “if-then” rule that contains linguistic terms to represent the feature values. This representation of a rule that demonstrates the correlations among the genes is very simple to understand and interpret for domain experts. In our proposed gene selection procedure, instead of measuring the effectiveness of every single gene for building the classifier model, we incorporate the impotence of a gene correlation with other existing genes in the process of gene selection. That is, we reject a gene if it is not in a significant correlation with other genes in the dataset. Furthermore, in order to improve the reliability of our approach, we repeat the process several times in our experiments, and the genes reported as the result are the genes selected in most experiments.
ASCCN
Authors: Wan Renxia , Wang Lixin , Su Xiaoke
Published: February 2011

Special clustering algorithms are attractive for the task of grouping an arbitrary shaped database into several proper classes. Until now, a wide variety of clustering algorithms for this task have been proposed, although the majority of these algorithms are density-based. In this paper, the authors extend the dissimilarity measure to compatible measure and propose a new algorithm (ASCCN) based on the results. ASCCN is an unambiguous partition method that groups objects to compatible nucleoids, and merges these nucleoids into different clusters. The application of cluster grids significantly reduces the computational cost of ASCCN, and experiments show that ASCCN can efficiently and effectively group arbitrary shaped data points into meaningful clusters.
A New Similarity Metric for Sequential Data
Authors: Kumar Pradeep , Raju Bapi S. , Krishna P. Radha
Published: February 2011

In many data mining applications, both classification and clustering algorithms require a distance/similarity measure. The central problem in similarity based clustering/classification comprising sequential data is deciding an appropriate similarity metric. The existing metrics like Euclidean, Jaccard, Cosine, and so forth do not exploit the sequential nature of data explicitly. In this paper, the authors propose a similarity preserving function called Sequence and Set Similarity Measure (S3M) that captures both the order of occurrence of items in sequences and the constituent items of sequences. The authors demonstrate the usefulness of the proposed measure for classification and clustering tasks. Experiments were conducted on benchmark datasets, that is, DARPA’98 and msnbc, for classification task in intrusion detection and clustering task in web mining domains. Results show the usefulness of the proposed measure.
A Query Language for Mobility Data Mining
Authors: Trasarti Roberto , Giannotti Fosca , Nanni Mirco , Pedreschi Dino , Renso Chiara
Published: February 2011

The technologies of mobile communications and ubiquitous computing pervade society. Wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems, provided that a suitable knowledge discovery process is enacted for mining this mobility data. In this paper, the authors examine a formal framework, and the associated implementation, for a data mining query language for mobility data, created as a result of a European-wide research project called GeoPKDD (Geographic Privacy-Aware Knowledge Discovery and Delivery). The authors discuss how the system provides comprehensive support for the Mobility Knowledge Discovery process and illustrate its analytical power in unveiling the complexity of urban mobility in a large metropolitan area, based on a massive real life GPS dataset.
Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data
Authors: Yildizli Can Brochmann , Pedersen Thomas , Saygin Yucel , Savas Erkay , Levi Albert
Published: February 2011

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.
User Acceptance of Internet Banking In Malaysia
Authors: Rouibah Kamel , Thurasamy Ramayah , May Oh Sook
Published: February 2011

This study aims to investigate factor(s) which influence the user intention to use Internet banking. It compares the explanatory power of three well known technology adoption models (TAM, TPB and TRA). These models were used to test the impact of five factors (perceived ease of use, perceived usefulness, attitude, subjective norms and perceived behavioral control) on intention to adopt internet banking by 239 individual bank customers in Malaysia. Results reveal that the five factors have a direct positive effect on behavioral intention to use. However, attitude toward behavior has the highest effect, followed by perceived usefulness, and subjective norm, while perceived behavioral control exerts the weakest effect. Results also found TPB model has the best explanatory power, followed by TRA and TAM models. Findings of the study should benefit banks in improving their use of e-banking technologies as a strategic weapon, and can be used to target more potential customers.
Diffusion of E-Learning Practice in an Educational Institution
Authors: Thomas Keith , Lam Paul , Ho Annisa
Published: February 2011

Successful knowledge transfer or diffusion of e-learning practice goes beyond precursor incentives and anticipated rewards for the individual lecturer. It also involves wider enabling of learning attributes and cultural capabilities in an organization. This paper examines how some of these attributes and capabilities play out in an educational institution in the context of web-enabled technology. An organizational-learning model is used to examine diffusion of practices after initial design and development. This paper is based on a case study of eight course-level e-learning projects in a university based in Hong Kong. The study illustrates a number of issues and challenges for the wider uptake of the initial idea from the individual course to the programme and wider institution.
E-Assessment as a Driver for Cultural Change in Network-Centric Learning
Authors: Eijkman Henk , Herrmann Allan , Savige Kathy
Published: February 2011

This paper explores the potentially powerful role e-assessment practices can have on culture change in learning and teaching. This paper demonstrates how new e-assessment practices can ‘push back’ through educational institutions. This is done by applying the work of Gibbs and Simpson (2004/5) to e-assessment practices. To illustrate the practical effects of this evidence-based framework, the authors use UNSW@ADFA to demonstrate the possibilities for new e-assessment practices and their potential to drive systemic change. The authors conclude that the incorporation of these structured, evidence-based e-assessment practices demonstrably improve learning outcomes and student engagement without increasing the workload of staff and students.
Examining Diffusion and Sustainability of E-Learning Strategies through Weblog Data
Authors: Lam Paul , Lo Judy , Yeung Antony , McNaught Carmel
Published: February 2011

The study focuses on ‘horizontal’ and ‘vertical’ adoption of e-learning strategies at The Chinese University of Hong Kong as revealed through computer log records in the centrally supported learning management systems. Horizontal diffusion refers to whether e-learning has spread to influence the practice of more teachers and students. In vertical diffusion, the authors examined whether or not teachers tend to adopt more varied online learning activities in successive years. The overall findings are that, while adoption of simple strategies is increasing, there is little evidence of horizontal and vertical diffusion of more complex strategies. Indeed, the use of some of the more complex strategies, which may relate to greater potential learning benefits, decreased. Results have led to discussions about new focuses and strategies for our institutional eLearning Service.
Investigating the Attitude of Students Towards Online Learning
Authors: Reis Zerrin Ayvaz
Published: February 2011

The evolution of Internet has provided an opportunity for offering online learning. The old online learning models are getting replaced by new e-learning models. Many universities worldwide have started offering e-learning or online learning through a variety of online learning methods. In fact, the current e-learning models are revolutionizing the instructional content delivery, learning activities, and social communication. Although online learning environments are becoming popular, there is minimal research on learners’ attitudes toward online learning environments. The purpose of this study is to explore learners’ attitudes toward online learning. Over 300 participants participated. The findings of this study of students’ perceptions and attitudes toward online learning not only will help assess pedagogical approach but also help university officials prepare Internet-based online education delivery.
Issues and Opportunities in E-Business Research
Authors: Chen Ye-Sho , Chen Guoqing , Wu Soushan
Published: February 2011

The third industrial revolution, combining Internet technology with globalization, produces an increasingly complicated e-business environment. There is no wonder that skew distributions, a striking empirical regularity in the hypercompetitive digital economy, have attracted the attention of many researchers recently. Little known by many, Herbert Simon had studied similar regularities in the industrial economy and developed empirically grounded, explanatory theories to help guide strategic decision making in the evolutionary processes of organizations. In this paper, we draw upon five seemingly unrelated research areas of Simon (skew distributions, near decomposability, causal reasoning, effectual reasoning, and attention management) and propose a holistic framework of attention-based information systems for firms to frame an enduring competitive strategy in the digital economy. As an ongoing project, the framework is applied to model Netchising, an emerging research topic in global e-business.
Relationships Between Supply Characteristics and Buyer-Supplier Coupling in E-Procurement
Authors: Nath Ravi , Angeles Rebecca
Published: February 2011

This study explores the resource dependency and relational exchange theories of understanding firms’ participation in e-procurement, and seeks to determine the degree to which the resource dependency theory variables — supply importance, supply complexity, supply market dynamism, and availability of alternatives — affect information exchange and operational linkages, the relational exchange theory variables. Data was gathered from the Institute for Supply Management and the Council of Logistics Management members using the survey technique. Supply importance and supply complexity primarily predict information exchange and operational linkages. Study findings reconfirm the important impact of environmental and market uncertainty on firm responses, as echoed in past studies.
Computer Self-Efficiency as an Antecedent to the Acceptance of Instant Messenger Technology
Authors: Stafford Thomas F.
Published: February 2011

AOL Instant Messenger (IM) is a widely used Internet chat technology. There are indications that users initially do not find it easy to use, and this concerns AOL management; they consider that if popular applications are not easy to use, the ability of AOL to attract and hold loyal customers will be impeded. In this paper, the acceptance of IM technology is investigated within the familiar framework of the Technology Acceptance Model (TAM), in which ease of use plays a pivotal role in promoting acceptance and subsequent use of a given technology. Computer Self Efficacy (CSE) is examined for possible antecedent roles in structural models of acceptance processes. It is determined that CSE does operate in a mediating relationship among some of the critical subcomponents of the TAM model but that it does not operate within the strictly defined theoretical boundaries established for general antecedents to the overall TAM process.
An Exploratory Study of Consumer Adoption of Online Shipping
Authors: Kulviwat Songpol , Thaku Ramendra , Guo Chiquan
Published: February 2011

An exploratory study was conducted to investigate consumer adoption of online purchase using a survey data set. Based upon the theory of innovation and self-efficacy theory, risk aversion, online proficiency, shopping convenience, and product choice variety were proposed to influence consumer intention to shop online, which, in turn, affects online purchases. Results of regression analyses revealed that all but shopping convenience were significant predictors of consumer intention to purchase online. In addition, consumer intention directly determines consumer purchases online. Finally, consumer intention to purchase online mediates the relationship of risk aversion, shopping convenience, and product choice variety to online shopping. Research and managerial implications of the findings were discussed.
Scalable Security and Accounting Services for Content-Based Publish/Subscribe Systems
Authors: Khurana Himanshu , Koleva Radostina K.
Published: February 2011

Content-based publish/subscribe systems offer an interaction scheme that is appropriate for a variety of large-scale dynamic applications. However, widespread use of these systems is hindered by a lack of suitable security services. In this paper, we present scalable solutions for confidentiality, integrity, and authentication for these systems. We also provide verifiable usage-based accounting services, which are required for e-commerce and e-business applications that use publish/subscribe systems. Our solutions are applicable in a setting where publishers and subscribers may not trust the publish/subscribe infrastructure.
Performance Evaluation of Consumer Decision Support Systems
Authors: Zhang Jiyong , Pu Pearl
Published: February 2011

Consumer decision support systems (CDSSs) help online users make purchasing decisions in e-commerce Web sites. To more effectively compare the usefulness of the various functionalities and interface features of such systems, we have developed a simulation environment for decision tasks of any scale and structure. Furthermore, we have identified three criteria in an evaluation framework for assessing the quality of such CDSSs: users’ cognitive effort, preference expression effort, and decision accuracy. A set of experiments carried out in such simulation environments showed that most CDSSs employed in current e-commerce Web sites are suboptimal. On the other hand, a hybrid decision strategy based on four existing ones was found to be more effective. The interface improvements based on the new strategy correspond to some of the advanced tools already developed in the research field. This result is therefore consistent with our earlier work on evaluating CDSSs with real users. That is, some advanced tools do produce more accurate decisions while requiring a comparable amount of user effort. However, the simulation environment will enable us to efficiently compare more advanced tools among themselves, and indicate further opportunities for functionality and interface improvements.