bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovative Approaches for Efficiently Warehousing Complex Data from the Web
Authors: Bentayeb Fadila , Maïz Nora , Mahboubi Hadj , Favre Cécile , Loudcher Sabine , Harbi Nouria , Boussaïd Omar , Darmont Jérôme
Published: October 2011

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.
Incorporating Text OLAP in Business Intelligence
Authors: Park Byung-Kwon , Song Il-Yeol
Published: October 2011

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.
Interaction Mining
Authors: Pallotta Vincenzo , Vrieling Lammert , Delmonte Rodolfo
Published: October 2011

In this chapter we present the major challenges of a new trend in business analytics, namely Interaction Mining. With the proliferation of unstructured data as the result of people interacting with each other using digital networked devices, classical methods in text business analytics are no longer effective. We identified the causes of their failure as being related to the inadequacy of dealing with conversational data. We propose then to move from Text Mining towards Interaction Mining, and we make several cases for this transition in areas such as marketing research, social media analytics, and customer relationship management. We also propose a roadmap for the future development of Interaction Mining by challenging the current practices in business intelligence and information visualization.
OpAL
Authors: Balahur Alexandra , Boldrini Ester , Montoyo Andrés , Martínez-Barco Patricio
Published: October 2011

The past years have marked the birth and development of the Social Web, where people freely express and search for opinions on all possible topics. This phenomenon has been proven to have a great impact on many business sectors globally. Given the proven importance of the subjective data on the Web, but bearing in mind the difficulties inherent to their textual peculiarities and large volume, efficient techniques must be employed to process this data, so that it can be fully exploited to the benefit of potential users and companies. We present the OpAL system, which implements an efficient approach to mine, classify and statistically summarize opinions, grounded on the feature-based Opinion Mining paradigm. In this approach, all components are studied, implemented and optimized using different NLP techniques. Results of different in-house and competition evaluations show that the system components have a good performance and that the techniques considered are efficient. We finally complete the proposed approach by presenting a method for opinion retrieval, which is robust and multilingual. Thus, we offer an integrated solution to build a system that is able to fully respond to user needs, from the querying to the summarized output stage. Implemented at a large scale, such systems can benefit the business environment and its customers everywhere.
Business Intelligence-as-a-Service
Authors: Essaidi Moez , Osmani Aomar
Published: October 2011

In recent years, the data warehousing infrastructures have undergone many changes in various aspects. This is usually due to many factors: the emergence of Software-as-a-Service (SaaS) architecture model; the success of agile and iterative Data Warehouse (DW) development approaches; the introduction of new approaches based on the Model Driven Architecture (MDA); the changing needs of organizations and the extension of the DW into new application areas; and the evolving of standards and open-source technologies. This chapter explores several aspects that may influence the next-generation of data warehousing platforms: the architectural aspects for business intelligence-as-a-service deployment, the promising open industry standards and technologies recommended for use, and the emerging methodological aspects for DW components engineering.
A Data Mining Service to Assist Instructors Involved in Virtual Education
Authors: Zorrilla Marta E. , García Diego
Published: October 2011

In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.
BIN
Authors: Golfarelli Matteo , Mandreoli Federica , Penzo Wilma , Rizzi Stefano , Turricchia Elisa
Published: October 2011

Cooperation is seen by companies as one of the major means for increasing flexibility and innovating. Business intelligence (BI) platforms are aimed at serving individual companies, and they cannot operate over networks of companies characterized by an organizational, lexical, and semantic heterogeneity. In this chapter we propose a framework, called Business Intelligence Network (BIN), for sharing BI functionalities over complex networks of companies that are chasing mutual advantages through the sharing of strategic information. A BIN is based on a network of peers, one for each company participating in the consortium. Peers are equipped with independent BI platforms that expose some querying functionalities aimed at sharing business information for the decision-making process. After proposing an architecture for a BIN, we outline the main research issues involved in its building and operating, and we focus on the definition of an ad hoc language for expressing semantic mappings between the multidimensional schemata owned by the different peers, aimed at enabling query reformulation over the network.
Towards Ad-Hoc and Collaborative Business Intelligence
Authors: Berthold Henrike , Rösch Philipp , Zöller Stefan , Wortmann Felix , Carenini Alessio , Campbell Stuart
Published: October 2011

The success of organizations and business networks depends on fast and well-founded decisions taken by the relevant people in their specific area of responsibility. To enable timely and well-founded decisions, it is often necessary to perform ad-hoc analyses in a collaborative manner involving domain experts, line-of-business managers, key suppliers, or customers. Current Business Intelligence (BI) solutions fail to meet the challenges of ad-hoc and collaborative decision support, thus slowing down and hurting organizations. To move towards ad-hoc and collaborative BI, we envision a highly scalable and flexible BI platform. The main building blocks of this platform are a flexible and efficient concept for the management of business context information, an intuitive and powerful methodology for the configuration of a BI system, a concept of an information self-service for business users over data sources within and across organizations, a collaborative decision making environment, and an architecture for the whole system that complements current BI systems.
Factors of Spatial Distribution of Recreation Areas in Peri-Urban Beijing
Authors: Liu Jiaming
Published: October 2011

With the increase of disposable income and leisure time, recreation has become an important lifestyle of urban residents in China. Peri-urban recreation areas, as the important one, could not only meet the outdoor recreational demands of both tourists and residents, but also contribute to sustaining urban ecological security. Rational spatial patterns for recreational land use will mediate the contradictions between urban sprawl and natural preservation, and, economic growth and social development. Based on the case study of peripheral urban areas in Beijing, this chapter aims to identify the factors which will influence the spatial distribution of peri-urban recreation areas, by analyzing the collected data from questionnaires, online survey, documentation and field investigations. In order to achieve sound information, relevant data from different management departments, owners and land-use types involved in the case study area is collected. A sampling database for peri-urban recreation areas in Beijing is established, and GIS spatial analyses as well as statistic analyses are applied. The result indicates that spatial distribution of recreation areas is majorly influenced by four factors, e.g. tourism attractions and environmental conditions, policy and spatial governance, consumption demand and preference, land price and availability. The influencing mechanism of each factor towards different type of recreation areas is various. Tourism attractions and environmental conditions are dominant factors for public recreation areas. Commercial recreation areas are highly related with accessibility. Agricultural recreation areas are usually attached to special farmlands near large-scaled scenic areas. Meanwhile, recreational business clusters have appeared in suburbs influenced by mass recreation market growth. Controlled by the land price, commercial recreation areas are differentiated on their scales and developing intensity. Policy and spatial governance have made arrangements of recreation areas more balancing and more human-oriented. A peri-urban recreation area model is therefore established on basis of this analysis, which can guide urban planning and designing, land-use planning and recreation resource development.
Knowledge-Driven Customer Support Services
Authors: Aviv Itzhak , Levy Meira , Hadar Irit
Published: October 2011

A Customers Relationship Management (CRM) program aspires to manage the relationship between a company and its customers as a key to success, in view of the fact that good relationships with customers lead to higher customers’ satisfaction. Despite the importance of CRM programs, their failure rates are high, partly because CRM service providers cannot resolve customers’ claims on time, which often occur due to the difficulty to find valuable knowledge and reproduce solutions. Therefore, integrating Knowledge Management (KM) activities, and in particular social Web 2.0 applications, within a CRM solution suit may enable to significantly enhance the efficiency of the organizational CRM program and build a knowledge-driven customer support services solution. The proposed CRM solution is based on a research case study conducted within customer service department of a large software organization.
Improving CRM 2.0 through Collective Intelligence by Using CBIR Algorithms
Authors: Perez-Gallardo Yuliana , Alor-Hernandez Giner , Cortes-Robles Guillermo
Published: October 2011

With the implementation of CBIR paradigms and Collective Intelligence into Web 2.0 application, CRM 2.0 can be improved by providing a new strategy for presenting products or services. This integration materializes a link where customers have the ability to enrich their search before the purchase; to effectively compare products, and to clarify their preferences. Finally, it is important to underline that the proposed integration represents a decision support in the feedback phase of CRM.
Semantic Web Applications to Enhance the Market Opportunities of SMEs
Authors: Morandi Valentina , Sgobbi Francesca
Published: October 2011

This chapter contributes to the debate on Semantic Web applications as business-enablers by focusing the perspective of smaller business users. A case study investigates the benefits and the challenges of adopting an inter-organisational Information System based on Semantic Web identified by the members of a consortium of small ICT firms which collaborate in the provision of end-to-end services to their clients. Besides the opportunity of increased turnover, also appropriate technical skills, availability of a critical mass of digitalised information, change management competencies and innovativeness play a crucial role in driving the success of Semantic Web applications. These dimensions should be carefully considered when assessing the benefits of technological innovations based on Semantic Web, especially when addressing smaller businesses.
CRM for Innovation in European Companies
Authors: López-Nicolás Carolina , Molina-Castillo Francisco-José
Published: October 2011

In recent years, customer relationship management (CRM) has been a topic of the utmost importance for scholars and managers. Literature suggests, however, that it is difficult to demonstrate both tangible returns and also intangible attributes of these benefits, such as innovation derived from the use of CRM. The aim of the chapter is to analyze the implementation of CRM and its influence on a firm’s ability for innovation. Literature on CRM and innovation is reviewed and a theoretical model is proposed. Research findings from a sample of European companies prove that CRM is beneficial for innovation in many countries. However, there were no conclusive results from other countries. These findings have important implications in assisting organizations in their CRM initiatives for managing customer knowledge and enhancing innovation.
Customer Relationship Management (CRM) Implementation Intensity and Performance
Authors: Liew Bryan Soh Yuen , Ramayah T. , Leen Jasmine Yeap Ai
Published: October 2011

Competition in the Web-hosting industry has become very intense in recent years as the market becomes saturated with existing as well as new players. To survive in this very competitive environment, Web-hosting companies need to be more responsive to their customer needs. Customer relationship management is thought of as a key solution to this. Thus the purpose of this study was to test the effect of CRM implementation intensity on CRM performance among Web-hosting companies. The more intense the implementation the better the CRM performance should be. Data was collected via online questionnaires from 81 respondents representing various online Web-hosting companies around the globe. The results indicated CRM implementation intensity had a direct positive influence on CRM performance.
A Framework for Customer Knowledge Management based on Social Semantic Web
Authors: Chaves Marcirio Silveira , Trojahn Cássia , Pedron Cristiane Drebes
Published: October 2011

Social websites contain a wealth of data that constitute new information sources to be integrated in Customer Knowledge Management (CKM) companies’ initiatives. The hotel sector is strongly affected by the comments written in such sites. These comments often help guests decide whether making a reservation. Hotel managers need Information Systems to better explore customers knowledge from Social Web to support decision making. In this chapter, we propose a framework to integrate knowledge from Social Web to support CKM. This framework is supported by a strong CKM theoretical referential, principles of Social Web, and the core of Semantic Web, an ontology. This is a new multilingual ontology for the hotel sector. We are researching the hotel sector and analysing the actual use of emerging technologies in order to customize the framework proposed.
Exploding Web 3.0 and Web 2.0 for Sales Processes Definition
Authors: López-Cuadrado José Luis , González-Carrasco Israel , García-Crespo Ángel , Ruiz-Mezcua Belén
Published: October 2011

The evolution of Web 2.0 and Web 3.0 technologies has lead to a new way of Customer Relationship Management. Web 2.0 provides communication and collaboration elements that can improve on the one hand the quality of the relationships with customers and, on the other hand, the collaboration among the members of the company. Web 3.0 provides semantic information for the knowledge available, as well as shared vocabularies by means of ontologies. In this chapter the main trends and implications of these technologies are summarized. Taking into account the characteristics of social and semantic technologies, a new architecture is proposed in order to facilitate the communication among customers and sales experts. Furthermore a framework for automating sales processes based in the collaborative knowledge representation collected by the experts and, if required, the customers is presented. The proposed framework goes beyond a single recommender system or a social tool. Its aim is to cover the issues related to the necessity of integrate the customer in the overall sales process, exploiting the mentioned advantages of these new Web technologies.
Sentiment Analysis in Business Intelligence
Authors: Plaza Laura , Carrillo de Albornoz Jorge
Published: October 2011

Sentiment Analysis is a novel and broad area of Natural Language Processing (NLP) aiming to understand people’s sentiments and opinions about a given topic. In particular, this chapter focuses on the application of Sentiment Analysis to automatically evaluate online products and services reviews. Undoubtedly, the information in customer reviews is of great interest to both companies and consumers. Companies and organizations spend a huge amount of money to find customers’ opinions and sentiments, since this information is useful to exploit their marketing-mix in order to affect consumer satisfaction. Individuals are interested in others’ experiences when purchasing a product or hiring a service. Moreover, online opinions clearly influence the companies’ reputation. For this reason, Sentiment Analysis is expected to become a key component of Customer Relationship Management (CRM) solutions. However, the task of mining opinions in text, as any other NLP task, is a very challenging one. The objective of this chapter is to present the reader the main ideas of Sentiment Analysis and its practical applications in business intelligence. It also discussed the approaches and techniques used so far, and the corpora and resources most widely used in the development of sentiment-driven systems.
Social Network Analysis in Marketing
Authors: Kazienko Przemyslaw , Doskocz Piotr , Kajdanowicz Tomasz
Published: October 2011

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.
The Use of Customer Relationship Management Software in Meta-enterprises for Virtual Enterprise Integration
Authors: Cruz-Cunha Maria Manuela , Putnik Goran  D. , Varajão João
Published: October 2011

To make the Virtual Enterprise (VE) a real, competitive, and widely implemented organizational and management concept, it is fundamental to assure that the requirements of this organizational model are achieved in the several phases of the Virtual Enterprise life-cycle. The support to VE (comprising design, creation, operation and reconfiguration) must be assured by meta-enterprises based on a broker and computer-aided tools, capable of managing, controlling and enabling virtual enterprise creation/ operation /reconfiguration, which may represent a strong contribution towards the VE performance. The chapter introduces the VE model requirements, focuses one specific meta-enterprise environment called the Market of Resources, and introduces the incorporation of CRM in this environment as one of the computer-aided systems.
Knowledge Management in Agile Methods Context
Authors: Alzoabi Zaidoun
Published: October 2011

Agile methods are characterized with flexibility, reliance on tacit knowledge, and face to face communication in contrast to traditional methods that rely on explicit knowledge sharing mechanism, extensive documentation, and formal means of communication. In this chapter, the authors will have a look at the knowledge management techniques used in different software development processes with focus on agile methods. Then they will test the claim of more informal knowledge sharing and see the mechanisms used to exchange and document knowledge. The test is on the basis of a survey conducted by Scott Ambler in 2009, where he surveyed over 300 agile practitioners asking them about mechanisms used and in which context every mechanism is applied.
Knowledge Discovery Process Models
Authors: Alnoukari Mouhib , El Sheikh Asim
Published: October 2011

Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process model. The common factor of all data-driven discovery process is that knowledge is the final outcome of this process. In this chapter, the authors will analyze most of the KD process models suggested in the literature. The chapter will have a detailed discussion on the KD process models that have innovative life cycle steps. It will propose a categorization of the existing KD models. The chapter deeply analyzes the strengths and weaknesses of the leading KD process models, with the supported commercial systems and reported applications, and their matrix characteristics.
Agile Methodologies for Business Intelligence
Authors: Larson Deanne
Published: October 2011

Agile methodologies were introduced in 2001. Since this time, practitioners have tried to create and apply Agile methodologies to many delivery disciplines. This chapter will explore the application of Agile methodologies and principles to business intelligence delivery. The practice of business intelligence delivery with an Agile methodology has yet be proven to the point of maturity and stability; this chapter will outline Agile principles and practices that have emerged as best practices and formulate a framework to outline how an Agile methodology could be applied to business intelligence delivery.
Business Intelligence and Agile Methodology for Risk Management in Knowledge-Based Organizations
Authors: Almustafa Muhammad Mazen , Alkhaldi Dania
Published: October 2011

In this highly technology - dependent, knowledge- based economy, the causes for failure of most software development projects are related to rapid technology changes, in-flux business requirements, or failure to tackle risk. Accordingly, risk management plays significant and crucial role in organizations’ response to this rapidly changing economy. Risk management process is illustrated in four main steps: identify the risk, analyze the risk, treat the risk and monitor the risk. This chapter discusses and explores the role of business intelligence and agile methodology to manage risk effectively and efficiently. It explores the risk management traditional tools that are commonly used, the role of business intelligence in risk management, and the role of agile methodology in risk management.
Towards a Business Intelligence Governance Framework Within E-Government System
Authors: Atieh Kamal , Farzali Elias
Published: October 2011

The need for such framework increase or decrease in each country depending on its status and on the relationship existed between business and IT teams.
Graph Representation
Authors: Dominguez-Sal D. , Muntés-Mulero V. , Martínez-Bazán N. , Larriba-Pey J.
Published: October 2011

In this chapter, we review different graph implementation alternatives that have been proposed in the literature. Our objective is to provide the readers with a broad set of alternatives to implement a graph, according to their needs. We pay special attention to the techniques that enable the management of large graphs. We also include a description of the most representative libraries available for representing graphs.
Labelling-Scheme-Based Subgraph Query Processing on Graph Data
Authors: Wang Hongzhi , Li Jianzhong , Gao Hong
Published: October 2011

When data are modeled as graphs, many research issues arise. In particular, there are many new challenges in query processing on graph data. This chapter studies the problem of structural queries on graph data. A hash-based structural join algorithm, HGJoin, is first proposed to handle reachability queries on graph data. Then, it is extended to the algorithms to process structural queries in form of bipartite graphs. Finally, based on these algorithms, a strategy to process subgraph queries in form of general DAGs is proposed. It is notable that all the algorithms above can be slightly modified to process structural queries in form of general graphs.
Matrix Decomposition-Based Dimensionality Reduction on Graph Data
Authors: Saigo Hiroto , Tsuda Koji
Published: October 2011

Graph is a mathematical framework that allows us to represent and manage many real-world data such as relational data, multimedia data and biomedical data. When each data point is represented as a graph and we are given a number of graphs, a task is to extract a few common patterns that capture the property of each population. A frequent graph mining algorithm such as AGM, gSpan and Gaston can enumerate all the frequent patterns in graph data, however, the number of patterns grows exponentially, therefore it is essential to output only discriminative patterns. There are many existing researches on this topic, but this chapter focus on the use of matrix decomposition techniques, and explains the two general cases where either i) no target label is available, or ii) target label is available for each data point. The reuslting method is a branch and bound pattern mining algorithm with efficient pruning condition, and we evaluate its effectiveness on cheminformatics data.
Clustering Vertices in Weighted Graphs
Authors: Wijaya Derry Tanti , Bressan Stephane
Published: October 2011

Clustering is the unsupervised process of discovering natural clusters so that objects within the same cluster are similar and objects from different clusters are dissimilar. In clustering, if similarity relations between objects are represented as a simple, weighted graph where objects are vertices and similarities between objects are weights of edges; clustering reduces to the problem of graph clustering. A natural notion of graph clustering is the separation of sparsely connected dense sub graphs from each other based on the notion of intra-cluster density vs. inter-cluster sparseness. In this chapter, we overview existing graph algorithms for clustering vertices in weighted graphs: Minimum Spanning Tree (MST) clustering, Markov clustering, and Star clustering. This includes the variants of Star clustering, MST clustering and Ricochet.
Large Scale Graph Mining with MapReduce
Authors: Tsourakakis Charalampos E.
Published: October 2011

In this Chapter, we present state of the art work on large scale graph mining using MapReduce. We survey research work on an important graph mining problem, counting the number of triangles in large-real world networks. We present the most important applications related to the count of triangles and two families of algorithms, a spectral and a combinatorial one, which solve the problem efficiently.
On the Efficiency of Querying and Storing RDF Documents
Authors: Vidal Maria-Esther , Martínez Amadís , Ruckhaus Edna , Lampo Tomas , Sierra Javier
Published: October 2011

In the context of the Semantic Web, different approaches have been defined to represent RDF documents, and the selected representation affects storage and time complexity of the RDF data recovery and query processing tasks. This chapter addresses the problem of efficiently querying and storing RDF documents, and presents an alternative representation of RDF data, Bhyper, which is based on hypergraphs. Additionally, access and optimization techniques to efficiently execute queries with low cost, are defined on top of this hypergraph based representation. The chapter’s authors have empirically studied the performance of the Bhyper based techniques, and their experimental results show that the proposed hypergraph based formalization reduces the RDF data access time as well as the space needed to store the Bhyper structures, while the query execution time of state-the-of-art RDF engines can be sped up by up to two orders of magnitude.
Graph Applications in Chemoinformatics and Structural Bioinformatics
Authors: Gardiner Eleanor Joyce
Published: October 2011

The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bioinformatics. There is a long history of chemical graph theory dating back to the 1860’s and Kekule’s structural theory. It is natural to regard the atoms of a molecule as nodes and the bonds as edges (2D representations) of a labeled graph (a molecular graph). This chapter will concentrate on the algorithms developed to exploit the computer representation of such graphs and their extensions in both two and three dimensions (where an edge represents the distance in 3D space between a pair of atoms), together with the algorithms developed to exploit them. The algorithms will generally be summarized rather than detailed. The methods were later extended to larger macromolecules (such as proteins); these will be covered in less detail.
A Graph-Based Approach for Semantic Process Model Discovery
Authors: Gater Ahmed , Grigori Daniela , Bouzeghoub Mokrane
Published: October 2011

One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications or user needs. OWL-S is one of the proposals for describing semantic metadata about Web services, which is based on the OWL ontology language. Majority of current approaches for matching OWL-S processes take into account only the inputs/outputs service profile. This chapter argues that, in many situations the service matchmaking should take into account also the process model. We present matching techniques that operate on OWL-S process models and allow retrieving in a given repository, the processes most similar to the query. To do so, the chapter proposes to reduce the problem of process matching to a graph matching problem and to adapt existing algorithms for this purpose. It proposes a similarity measure used to rank the discovered services. This measure captures differences in process structure and semantic differences between input/outputs used in the processes.
Unbalanced Sequential Data Classification using Extreme Outlier Elimination and Sampling Techniques
Authors: Padmaja T.Maruthi , Bapi Raju S. , Krishna P. Radha
Published: October 2011

Predicting minority class sequence patterns from the noisy and unbalanced sequential datasets is a challenging task. To solve this problem, we proposed a new approach called extreme outlier elimination and hybrid sampling technique. We use k Reverse Nearest Neighbors (kRNNs) concept as a data cleaning method for eliminating extreme outliers in minority regions. Hybrid sampling technique, a combination of SMOTE to oversample the minority class sequences and random undersampling to undersample the majority class sequences is used for improving minority class prediction. This method was evaluated in terms of minority class precision, recall and f-measure on syntactically simulated, highly overlapped sequential dataset named Hill-Valley. We conducted the experiments with k-Nearest Neighbour classifier and compared the performance of our approach against simple hybrid sampling technique. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions for both fraud and non-fraud classes.
Identification of Genomic Islands by Pattern Discovery
Authors: Parekh Nita
Published: October 2011

Pattern discovery is at the heart of bioinformatics, and algorithms from computer science have been widely used for identifying biological patterns. The assumption behind pattern discovery approaches is that a pattern that occurs often enough in biological sequences/structures or is conserved across organisms is expected to play a role in defining the respective sequence’s or structure’s functional behavior and/or evolutionary relationships. The pattern recognition problem addressed here is at the genomic level and involves identifying horizontally transferred regions, called genomic islands. A horizontally transferred event is defined as the movement of genetic material between phylogenetically unrelated organisms by mechanisms other than parent to progeny inheritance. Increasing evidence suggests the importance of horizontal transfer events in the evolution of bacteria, influencing traits such as antibiotic resistance, symbiosis and fitness, virulence, and adaptation in general. In the genomic era, with the availability of large number of bacterial genomes, the identification of genomic islands also form the first step in the annotation of the newly sequenced genomes and in identifying the differences between virulent and non-virulent strains of a species. Considerable effort is being made in their identification and analysis and in this chapter a brief summary of various approaches used in the identification and validation of horizontally acquired regions is discussed.
Discovering Patterns for Architecture Simulation by Using Sequence Mining
Authors: Senkul Pinar , Onder Nilufer , Onder Soner , Maden Engin , Nyew Hui Meen
Published: October 2011

The goal of computer architecture research is to design and build high performance systems that make effective use of resources such as space and power. The design process typically involves a detailed simulation of the proposed architecture followed by corrections and improvements based on the simulation results. Both simulator development and result analysis are very challenging tasks due to the inherent complexity of the underlying systems. The motivation of this work is to apply episode mining algorithms to a new domain, architecture simulation, and to prepare an environment to make predictions about the performance of programs in different architectures. We describe our tool called Episode Mining Tool (EMT), which includes three temporal sequence mining algorithms, a preprocessor, and a visual analyzer. We present empirical analysis of the episode rules that were mined from datasets obtained by running detailed micro-architectural simulations.
The Impact of Technology on Early Childhood Education
Authors: Gibbons Andrew Neil
Published: October 2011

This chapter explores the bridging of the communication-generation gap through an analysis of the child’s play with hi-tech toys. The analysis of the young child’s play with these toys employs narrative from the show Digimon in order to critique predominant themes in relation to learning and development. These themes highlight challenges to binary oppositions of adult-child, self-other, and artificial-real. In troubling these oppositions, the very idea of the gap becomes of interest in that in the gap are potential alternatives for adults who seek a critical understanding of the complex terrains in which they engage the young child.
Children’s Power for Learning in the Age of Technology
Authors: McLeod Julie , Lin Lin , Vasinda Sheri
Published: October 2011

This chapter situates discussions of children’s power for learning in the context of new media and technology. We assert that for learning to take place, children must exert their own power and take initiatives in their learning; yet, the current power structure of classrooms inhibits children from exerting their power and motivation for learning. Tracing the seminal works on power, we provide examples of children’s power in learning and argue for a power structure transformation necessary in a technology-rich classroom of the twenty-first century.
Early Childhood Teachers
Authors: Thomas Kevin , Cooter Kathleen Spencer
Published: October 2011

This chapter reviews the state of technology training for early childhood educators in teacher preparation institutions across the country. Using NCATE and NAEYC standards as benchmarks of practice, the chapter outlines some current issues and research on technology training at the preservice level, such as course sequence, textbook choice, content infusion, field experiences, et cetera. The chapter also outlines three technologies, Web 2.0, Google Earth, and the virtual manipulatives that are accessible, free to users, require little teacher training, and have evidence to support their instructional benefits. These three well-developed technologies can easily be introduced to students and teachers as exemplars of constructivist pedagogical technology in early childhood science and mathematics classrooms. Activities using each are included.
Mathematics Learning through the Use of Technology
Authors: Smith Amy M. , Gentry Amy R. , Blake Sally
Published: October 2011

Technology can capture young children’s attention, motivate them, and help them construct early mathematics concepts in meaningful ways. This chapter examines the nature of children’s mathematics learning and how technology can support learning on three levels: (a) a teacher information resource; (b) teaching support; and (c) the learning process for children. It provides a description of how technology tools, when connected to sound inquiry-based pedagogy and formative assessment, can facilitate learning in today’s increasingly technological world. Considerations for future research as well as a list of relevant, practical resources for teachers to experiment with in their own classrooms are included.
Language Problems of First Year Science Students
Authors: Zhang Felicia
Published: October 2011

The chapter discusses the background of a project which aimed at addressing the language needs of a diverse student body (both domestic and international student body) by embedding strategic approaches to learning and teaching in first year sciences in tertiary education. These strategies consisted of active learning skills which are widely used in language learning. The disciplines covered by the project were Biology, Chemistry and Physics and involved the University of Canberra, University of Sydney, University of Tasmania, University of Technology, Sydney, and University of Newcastle in Australia. This project was funded by the Australian Learning and Teaching Council (ALTC).
Language Support Strategies in First Year Chemistry
Authors: Bridgeman Adam James
Published: October 2011

In-class and online active learning strategies, which have been implemented at The University of Sydney, are described to respond to the challenge of teaching chemistry at a first year level, to students with a wide range of abilities and levels of motivations. Core to the design of these activities is the belief that students learn chemical concepts most effectively when they are actively engaged in doing and talking, rather than rote learning and listening. The strategies described have been developed within the context of large classes and limited resources. They are fully adaptable to other topics beyond first chemistry and to other sciences. They are also necessarily designed to be scalable to large or small classes and to be sustainable. Online resources are useful for helping students become familiar with chemical language and symbolism and to provide them with a means of practicing their use. Online quizzes are an invaluable means of students self-assessing their progress and of providing meaningful assessment of their level of mastery. In-class activities involving student response systems and student-centred, inquiry based approaches are built around active learning and on-going formative assessment. To develop language skills, social interaction via peer instruction and group discussions can be utilized to ensure that classes are vibrant and engaging.
First Year Chemistry Language Support at the University of Tasmania
Authors: Yates Brian Francis , Gardiner Michael Guy
Published: October 2011

A number of in-class and post-lecture learning strategies are described that have recently been incorporated into the first year chemistry program at the University of Tasmania, Hobart. These methodologies are centered on supporting the various language based challenges that students face in achieving a firm introduction to the discipline. A number of delivery strategies were trialed for in-class activities that overcame institutional specific limitations in access to technologies in an attempt to increase student involvement in lectures and tutorials. Activities included mobile phone-based technologies (and simple overhead transparency/show-of-hand alternatives) for in-class audience surveys, word matching exercises, and chemical songs. The post-lecture approaches also covered a range of delivery platforms, including (i) on-line based quizzes that focused on providing specific, tailored feedback to allow maximal self-reflection on the understanding of key terms and concepts prior to attempting weekly summative assignments where feedback is deliberately kept limited; (ii) developing a broader range of weekly assignment questions (beyond conventional multiple choice) to focus on language issues; (iii) chemical Sudoku puzzles, and (iv) crossword activities. Evaluations of these approaches are presented.
Language Focus for Genetics and Molecular Biology Students
Authors: Lidbury Brett Andrew
Published: October 2011

This chapter examines the role of scientific language comprehension and confidence for senior undergraduate students in Genetics and Molecular Biology, and the impact of language-centred learning strategies to assessment outcomes. A number of online and tutorial language exercises and strategies are described that were designed to promote scientific language competence and subsequent genetics learning. The effect of these interventions was analysed through grade and assessment performance comparisons with earlier traditionally taught Genetics cohorts. While no significant grade improvements were found for cohorts taught via language, deeper statistical analysis revealed that motivation to adopt new learning strategies was crucial for best student performance. Language was found to be most influential for middle range performing students. Despite at least a year of tertiary education, students still had difficulty interpreting some everyday words in a science context. The study also encourages a greater evaluation of student motivation in adopting new learning techniques.
Evaluating a Genetics Concept Inventory
Authors: Zhang Felicia , Lidbury Brett Andrew
Published: October 2011

This chapter examines the reliability and validity of a subset of the Genetics concept inventory (S. Elrod) to discriminate good students from poor performing students in the undergraduate units Genetics and Molecular Biology taught at the University of Canberra, Australia. These two units went through a series of reforms since 2005. These reforms included the implementation of a number of online and tutorial language exercises and strategies designed to promote scientific language competence and subsequent genetics learning. The effect of these interventions was analyzed through grade and assessment performance comparisons with earlier traditionally taught Genetics cohorts as well using the genetic concept inventory. The genetic concept inventory questions used at the University of Canberra have been found to be reliable and valid according to a number of statistical tests.
The Benefits of Teaching Students the Language of Physics
Authors: Schulte Jurgen
Published: October 2011

First year physics courses for non-physics majors are among the most difficult course to cope with for science and engineering students alike. Not only are students confronted with physics specific concepts and mathematics applied in unfamiliar environment, students also have to learn the specific lingo of physics and keep it separate from common language and language used in their own field of major. Anecdotally, we know that students have to master the language of their respective field of study in order to master the field. We investigated the link between language of physics used in our first year physics lectures, understanding of related physics concept, and student performance after a language focused intervention. We found that a language conscious approach in first year physics not only improved the performance of students of non-English background but also benefited the performance of students of native English speaking background.
Clickers in a First Statistics Course
Authors: Richardson Alice Marion
Published: October 2011

This chapter reports on a three-year project to study to improve student outcomes in an introductory statistics course. Collaboration with a language expert led to the introduction of a variety of student-centred activities during the course. Some were online such as matching terms to definitions and multiple choice quizzes. Some were face-to-face such as the introduction of a student response system (commonly known as clickers). Results show a small but significant increase in grades following the introduction of these two interventions. A meta-analysis of research into the use of clickers was also conducted. This shows a small but significant increase in exam marks for students who used clickers in class. A Statistics Concept Inventory (SCI) was also used to assess students’ understanding of the course concepts. The usefulness of the SCI was partially supported, as many questions were better answered by more able students. The SCI was also used to assess retention from one semester to another. Again, the usefulness of the SCI is partially supported, in that students who remembered more of their introductory statistics study did better in a second statistics course.