bluuebunny/crossref_metadata_2025 huggingface dataset:
Big Data at Scale for Digital Humanities
Authors: Kowalczyk Stacy T. , Sun Yiming , Peng Zong , Plale Beth , Todd Aaron , Auvil Loretta , Willis Craig , Zeng Jiaan , Pathirage Milinda , Liyanage Samitha , Ruan Guangchen , Downie J. Stephen
Published: April 2016

Big Data in the humanities is a new phenomenon that is expected to revolutionize the process of humanities research. The HathiTrust Research Center (HTRC) is a cyberinfrastructure to support humanities research on big humanities data. The HathiTrust Research Center has been designed to make the technology serve the researcher to make the content easy to find, to make the research tools efficient and effective, to allow researchers to customize their environment, to allow researchers to combine their own data with that of the HTRC, and to allow researchers to contribute tools. The architecture has multiple layers of abstraction providing a secure, scalable, extendable, and generalizable interface for both human and computational users.
Construct a Bipartite Signed Network in YouTube
Authors: Yu Tianyuan , Bai Liang , Guo Jinlin , Yang Zheng
Published: April 2016

Nowadays, the video-sharing websites are becoming more and more popular, which leads to latent social networks among videos and users. In this work, results are integrated with the data collected from YouTube, one of the largest user-driven online video repositories, and are supported by Chinese sentiment analysis which excels the state of art. Along with it, the authors construct two types of bipartite signed networks, video network (VN) and topic participant network (TPN), where nodes denote videos or users while weights of edges represent the correlation between the nodes. Several indices are defined to quantitatively evaluate the importance of the nodes in the networks. Experiments are conducted by using YouTube videos and corresponding metadata related to two specific events. Experimental results show that both the analysis of social networks and indices correspond very closely with the events' evolution and the roles that topic participants play in spreading Internet videos. Finally, the authors extend the networks to summarization of a video set related to an event.
Rapid Development of Service-Based Cloud Applications
Authors: Gonidis Fotis , Paraskakis Iraklis , Simons Anthony J. H.
Published: April 2016

Cloud application platforms gain popularity and have the potential to alter the way service-based cloud applications are developed involving utilisation of platform basic services. A platform basic service provides certain functionality and is usually offered via a web API. However, the diversification of the services and the available providers increase the challenge for the application developers to integrate them and deal with the heterogeneous providers' web APIs. Therefore, a new approach of developing applications should be adopted in which developers leverage multiple platform basic services independently from the target application platforms. To this end, the authors present a development framework assisting the design of service-based cloud applications. The objective of the framework is to enable the consistent integration of the services, and to allow the seamless use of the concrete providers. The optimal service provider each time can vary depending on criteria such as pricing, quality of service and can be determined based upon Big Data analysis approaches.
Big Data Warehouse Automatic Design Methodology
Authors: Di Tria Francesco , Lefons Ezio , Tangorra Filippo
Published: April 2016

Traditional data warehouse design methodologies are based on two opposite approaches. The one is data oriented and aims to realize the data warehouse mainly through a reengineering process of the well-structured data sources solely, while minimizing the involvement of end users. The other is requirement oriented and aims to realize the data warehouse only on the basis of business goals expressed by end users, with no regard to the information obtainable from data sources. Since these approaches are not able to address the problems that arise when dealing with big data, the necessity to adopt hybrid methodologies, which allow the definition of multidimensional schemas by considering user requirements and reconciling them against non-structured data sources, has emerged. As a counterpart, hybrid methodologies may require a more complex design process. For this reason, the current research is devoted to introducing automatisms in order to reduce the design efforts and to support the designer in the big data warehouse creation. In this chapter, the authors present a methodology based on a hybrid approach that adopts a graph-based multidimensional model. In order to automate the whole design process, the methodology has been implemented using logical programming.
Big Data Paradigm for Healthcare Sector
Authors: Wassan Jyotsna Talreja
Published: April 2016

The digitization of world in various areas including health care domain has brought up remarkable changes. Electronic Health Records (EHRs) have emerged for maintaining and analyzing health care real data online unlike traditional paper based system to accelerate clinical environment for providing better healthcare. These digitized health care records are form of Big Data, not because of the fact they are voluminous but also they are real time, dynamic, sporadic and heterogeneous in nature. It is desirable to extract relevant information from EHRs to facilitate various stakeholders of the clinical environment. The role, scope and impact of Big Data paradigm on health care is discussed in this chapter.
Knowledge as a Service Framework for Collaborative Data Management in Cloud Environments - Disaster Domain
Authors: Grolinger Katarina , Mezghani Emna , Capretz Miriam A. M. , Exposito Ernesto
Published: April 2016

Decision-making in disaster management requires information gathering, sharing, and integration by means of collaboration on a global scale and across governments, industries, and communities. Large volume of heterogeneous data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in NoSQL, cloud computing, and Big Data open the door for new solutions in disaster data management. This chapter presents a Knowledge as a Service (KaaS) framework for disaster cloud data management (Disaster-CDM), with the objectives of facilitating information gathering and sharing; storing large amounts of disaster-related data; and facilitating search and supporting interoperability and integration. In the Disaster-CDM approach NoSQL data stores provide storage reliability and scalability while service-oriented architecture achieves flexibility and extensibility. The contribution of Disaster-CDM is demonstrated by integration capabilities, on examples of full-text search and querying services.
Techniques for Sampling Online Text-Based Data Sets
Authors: Webb Lynne M. , Wang Yuanxin
Published: April 2016

The chapter reviews traditional sampling techniques and suggests adaptations relevant to big data studies of text downloaded from online media such as email messages, online gaming, blogs, micro-blogs (e.g., Twitter), and social networking websites (e.g., Facebook). The authors review methods of probability, purposeful, and adaptive sampling of online data. They illustrate the use of these sampling techniques via published studies that report analysis of online text.
Web Intelligence
Authors: Jaafar Jafreezal , Danyaro Kamaluddeen Usman , Liew M. S.
Published: April 2016

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.
Pattern Recognition for Large-Scale Data Processing
Authors: Basirat Amir , Khan Asad I. , Schmidt Heinz W.
Published: April 2016

One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping with the rate at which unprocessed data is being accumulated. Transforming big data into valuable information requires a fundamental re-think of the way in which future data management models will need to be developed on the Internet. Unlike the existing relational schemes, pattern-matching approaches can analyze data in similar ways to which our brain links information. Such interactions when implemented in voluminous data clouds can assist in finding overarching relations in complex and highly distributed data sets. In this chapter, a different perspective of data recognition is considered. Rather than looking at conventional approaches, such as statistical computations and deterministic learning schemes, this chapter focuses on distributed processing approach for scalable data recognition and processing.
Expressing Data, Space, and Time with Tableau Public™
Authors: Hai-Jew Shalin
Published: April 2016

Virtually every subject area depicted in a learning object could conceivably involve a space-time element. Theoretically, every event may be mapped geospatially, and in time, these spatialized event maps may be overlaid with combined data (locations of particular natural and human-made objects, demographics, and other phenomena) to enable the identification and analysis of time-space patterns and interrelationships. They enable hypothesis formations, hunches, and the asking and answering of important research questions. The ability to integrate time-space insights into research work is enhanced by the wide availability of multiple new sources of free geospatial data: open data from governments and organizations (as part of Gov 2.0), locative information from social media platforms (as part of Web 2.0), and self-created geospatial datasets from multiple sources. The resulting maps and data visualizations, imbued with a time context and the potential sequencing of maps over time, enable fresh insights and increased understandings. In addition to the wide availability of validated geospatial data, Tableau Public is a free and open cloud-based tool that enables the mapping of various data sets for visualizations that are pushed out onto a public gallery for public consumption. The interactive dashboard enables users to explore the data and discover insights and patterns. Tableau Public is a tool that enables enhanced visual- and interaction-based knowing, through interactive Web-friendly maps, panel charts, and data dashboards. With virtually zero computational or hosting costs (for the user), Tableau Public enables the integration of geospatial mapping and analysis stands to benefit research work, data exploration and discovery and analysis, and learning.
A Survey of Big Data Analytics Systems
Authors: Nirmala M. Baby
Published: April 2016

In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.
Aesthetics in Data Visualization
Authors: Jung Heekyoung , Kim Tanyoung , Yang Yang , Carli Luis , Carnesecchi Marco , Rizzo Antonio , Gurrin Cathal
Published: April 2016

Data visualization has been one of the major interests among interaction designers thanks to the recent advances of visualization authoring tools. Using such tools including programming languages with Graphics APIs, websites with chart topologies, and open source libraries and component models, interaction designers can more effectively create data visualization harnessing their prototyping skills and aesthetic sensibility. However, there still exist technical and methodological challenges for interaction designers in jumping into the scene. In this article, the authors introduce five case studies of data visualization that highlight different design aspects and issues of the visualization process. The authors also discuss the new roles of designers in this interdisciplinary field and the ways of utilizing, as well as enhancing, visualization tools for the better support of designers.
A Distributed and Scalable Solution for Applying Semantic Techniques to Big Data
Authors: Amato Alba , Venticinque Salvatore , Di Martino Beniamino
Published: April 2016

The digital revolution changes the way culture and places could be lived. It allows users to interact with the environment creating an immense availability of data, which can be used to better understand the behavior of visitors, as well as to learn about their thoughts on what the visit creates excitement or disappointment. In this context, Big Data becomes immensely important, making possible to turn this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at modeling and designing a scalable solution that integrates semantic techniques with Cloud and Big Data technologies to deliver context aware services in the application domain of the cultural heritage. The authors started from a baseline framework that originally was not conceived to scale when huge workloads, related to big data, must be processed. They provide an original formulation of the problem and an original software architecture that fulfills both functional and not-functional requirements. The authors present the technological stack and the implementation of a proof of concept.
Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets
Authors: Sommer Stefan , Miller Tom , Hilbert Andreas
Published: April 2016

In the World Wide Web, users are an important information source for companies or institutions. People use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments of products, politics, society, or even private situations. In 2014, the Twitter users worldwide submitted 582 million messages (tweets) per day. To process the mass of Web 2.0's data (e.g. Twitter data) is a key functionality in modern IT landscapes of companies or institutions, because sentiments of users can be very valuable for the development of products, the enhancement of marketing strategies, or the prediction of political elections. This chapter's aim is to provide a framework for extracting, preprocessing, and analyzing customer sentiments in Twitter in all different areas.
Classification of Failures in Photovoltaic Systems using Data Mining Techniques
Authors: Serrano-Luján Lucía , Cadenas Jose Manuel , Urbina Antonio
Published: April 2016

Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.
Blending Technology, Human Potential, and Organizational Reality
Authors: Janssens Jurgen
Published: April 2016

To make the deeply rooted layers of catalyzing technology and optimized modelling gain their true value for education, healthcare or other public services, it is necessary to prepare well the Big Data environment in which the Big Data will be developed, and integrate elements of it into the project approach. It is by integrating and managing these non-technical aspects of project reality that analytics will be accepted. This will enable data power to infuse the organizational processes and offer ultimately real added value. This chapter will shed light on complementary actions required on different levels. It will be analyzed how this layered effort starts by a good understanding of the different elements that contribute to the definition of an organization's Big Data ecosystem. It will be explained how this interacts with the management of expectations, needs, goals and change. Lastly, a closer look will be given at the importance of portfolio based big picture thinking.
The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects
Authors: Bishop Jonathan
Published: April 2016

The current phenomenon of Big Data – the use of datasets that are too big for traditional business analysis tools used in industry – is driving a shift in how social and economic problems are understood and analysed. This chapter explores the role Big Data can play in analysing the effectiveness of crowd-funding projects, using the data from such a project, which aimed to fund the development of a software plug-in called ‘QPress'. Data analysed included the website metrics of impressions, clicks and average position, which were found to be significantly connected with geographical factors using an ANOVA. These were combined with other country data to perform t-tests in order to form a geo-demographic understanding of those who are displayed advertisements inviting participation in crowd-funding. The chapter concludes that there are a number of interacting variables and that for Big Data studies to be effective, their amalgamation with other data sources, including linked data, is essential to providing an overall picture of the social phenomenon being studied.
The Impact of Big Data on Security
Authors: Al-Hamami Mohammad Alaa Hussain
Published: April 2016

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.
Generational Differences Relative to Data-Based Wisdom
Authors: Maxi Gino , Klein Deanna
Published: April 2016

The purpose of this chapter is to present research findings and address the Generational Differences Relative to Data-Based Wisdom. Data-Based Wisdom is defined as the use of technology, leadership, and culture to create, transfer, and preserve the organizational knowledge embedded in its data, with a view to achieving the organizational vision. So what will comparing Generational Differences effectively do to help achieve organizational vision? If you don't know your history, you are doomed to repeat it; therefore, with the accumulation of ever growing data, understanding the necessary steps to store them properly and ability to retrieve them in an efficient manner are both explicit and tacit knowledge that are outside the scope of the conventional multi-disciplined approach to achieving organizational objectives. With time, technology, leadership, and culture have transformed into more than tangible items, social leadership concepts, and learned behavioral patterns. The latter three ideas have evolved along with the technological advances infused into society as we know it today. Therefore, the value and emphasis to develop and maintain intricate and efficient knowledge management databases suitable to create, transfer, and preserve organizational knowledge embedded in its data has never been more vital. The importance will continue to grow as changes in technology, leadership concepts, and culture continue to inundate.
The Impact of Virtualization on High Performance Computing Clustering in the Cloud
Authors: Achahbar Ouidad , Abid Mohamed Riduan
Published: April 2016

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.
The Sentiment Revealed in Social Networks during the Games of the Brazilian Team in the 2014 World Cup
Authors: Paulino Rita
Published: April 2016

The participation of people in social networks is undeniably a contemporary phenomenon that presents as a characteristic not only the flow of explicit information in data form, natural and complex, but also some information (data) from the network's own movement. It is in this context that this article fits with the purpose of revealing information that is implied in participatory movements of sociotechnical networks. For this, one can rely on the conceptual theoretical contribution about Actor-Network Theory (ANT), by Bruno Latour (2012): “follow things through the networks they carry”. It is believed that by following the movements of social networks, one can view information that reflects feelings and actions that are implied in the connections about facts and events. In this article, the author will analyze and monitor social networks during the games of the 2014 FIFA World Cup Brazil. This approach brings us to an applied research and experimental.
Here Be Dragons
Authors: Prinsloo Paul , Slade Sharon
Published: April 2016

Learning analytics is an emerging but rapidly growing field seen as offering unquestionable benefit to higher education institutions and students alike. Indeed, given its huge potential to transform the student experience, it could be argued that higher education has a duty to use learning analytics. In the flurry of excitement and eagerness to develop ever slicker predictive systems, few pause to consider whether the increasing use of student data also leads to increasing concerns. This chapter argues that the issue is not whether higher education should use student data, but under which conditions, for what purpose, for whose benefit, and in ways in which students may be actively involved. The authors explore issues including the constructs of general data and student data, and the scope for student responsibility in the collection, analysis and use of their data. An example of student engagement in practice reviews the policy created by the Open University in 2014. The chapter concludes with an exploration of general principles for a new deal on student data in learning analytics.
Business Analytics and Big Data
Authors: Kennedy Dennis T. , Crossen Dennis M. , Szabat Kathryn A.
Published: April 2016

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.
SLOD-BI
Authors: Berlanga Rafael , García-Moya Lisette , Nebot Victoria , Aramburu María José , Sanz Ismael , Llidó Dolores María
Published: April 2016

The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of a high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention to the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial to put in context the results of their analyses. Recent advances in Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper deals with these issues and proposes a novel semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI. This infrastructure follows the principles of the Linked Open Data initiative.
Digital Businesses
Authors: Wahi Ashok Kumar , Medury Yajulu
Published: April 2016

Customers are no longer at the receiving end in the new digital economies. They have a say in everything and are co-creating products and services. Their connection with other customers is stronger and the influence they exert collectively on businesses is phenomenal. All this has been made possible by the technologies that the collaborative internet has made possible. Businesses have discarded hierarchies and functional pyramid structures in favor of flat empowered structures to improve decision responsiveness in the new age. Competency is fast replacing compatibility amongst successful employees. Geography is dead and interactions take place across boundaries of distance, time, language and culture. This transformation of the business enterprise to Enterprise 2.0 has become possible due to the use of Web 2.0 tools becoming common place and has had far reaching implications. The question that it raises is that are all organizations equally well equipped to take advantage of these changes or is it going to change the relative power equation amongst them to make some small forward looking technology savvy organizations suddenly more powerful than the erstwhile successful large giants who had built themselves on the strength of their products and markets over time. This paper aims at creating a framework that can help evaluate this emerging equation and assess the state of readiness of all organizations to meet this onslaught of business change. The framework addresses these technologies, the way they are impacting business strategy and spells out all that organizations need to do to be able to gear up to face the changing fabric of the new age enterprise.
From Data Quality to Big Data Quality
Authors: Batini Carlo , Rula Anisa , Scannapieco Monica , Viscusi Gianluigi
Published: April 2016

This chapter investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.
Towards an Intelligent Integrated Approach for Clinical Decision Support
Authors: Akerkar Rajendra
Published: April 2016

Nowadays, making use of big data is becoming mainstream in different enterprises and industry sectors. The medical sector is no exception. Specifically, medical services, which generate and process enormous volumes of medical information and medical device data, have been quickening big data utilization. In this chapter, we present a concept of an intelligent integrated system for direct support of decision making of physicians. This is a work in progress and the focus is on decision support for pharmacogenomics, which is the study of the relationship between a specific person's genetic makeup and his or her response to drug treatment. Further, we discuss a research direction considering the current shortcomings of clinical decision support systems.
Scientific Data Management and Visualization
Authors: Goranova Mariana
Published: April 2016

One of the challenges of modern science is data exploration (eScience) that synthesizes theory, experimentation, and computation with advanced data management and statistics. The scientific community produces and consumes massive volumes of unstructured and heterogeneous data from various data sources. State-of-the-art research in “intelligent labs” explores scientific data management and visualization in distributed and heterogeneous environments. The goal of this chapter is to propose and describe a scientific data management and visualization system for scientists to perform specialized data browsing, processing, and visualization using a service-driven integration approach. In order to make scientific data more usable from the Internet, a SOA-based system that uses Web services to manage data is proposed. This chapter discusses the methodology to describe and access scientific data from various sources with different formats, and transform raw data into standard datasets that can be analyzed, processed, and visualized in an effective manner.
Efficient Metaheuristic Approaches for Exploration of Online Social Networks
Authors: Stanimirović Zorica , Mišković Stefan
Published: April 2016

This study presents a novel approach in analyzing big data from social networks based on optimization techniques for efficient exploration of information flow within a network. Three mathematical models are proposed, which use similar assumptions on a social network and different objective functions reflecting different search goals. Since social networks usually involve a large number of users, solving the proposed models to optimality is out of reach for exact methods due to memory or time limits. Therefore, three metaheuristic methods are designed to solve problems of large-scaled dimensions: a robust Evolutionary Algorithm and two hybrid methods that represent a combination of Evolutionary Algorithm with Local Search and Tabu Search methods, respectively. The results of computational experiments indicate that the proposed metaheuristic methods are efficient in detecting trends and linking behavior within a social network, which is important for providing a support to decision-making activities in a limited amount of time.
The New “ABC” of ICTs (Analytics + Big Data + Cloud Computing)
Authors: Cavalcanti José Carlos
Published: April 2016

Analytics (discover and communication of patterns, with significance, in data) of Big Data (basically characterized by large structured and unstructured data volumes, from a variety of sources, at high velocity - i.e., real-time data capture, storage, and analysis), through the use of Cloud Computing (a model of network computing) is becoming the new “ABC” of information and communication technologies (ICTs), with important effects for the generation of new firms and for the restructuring of those ones already established. However, as this chapter argues, successful application of these new ABC technologies and tools depends on two interrelated policy aspects: 1) the use of a proper model which could help one to approach the structure and dynamics of the firm, and, 2) how the complex trade-off between information technology (IT) and communication technology (CT) costs is handled within, between and beyond firms, organizations and institutions.
Scalable Data Mining, Archiving, and Big Data Management for the Next Generation Astronomical Telescopes
Authors: Mattmann Chris A. , Hart Andrew , Cinquini Luca , Lazio Joseph , Khudikyan Shakeh , Jones Dayton , Preston Robert , Bennett Thomas , Butler Bryan , Harland David , Glendenning Brian , Kern Jeff , Robnett James
Published: April 2016

Big data as a paradigm focuses on data volume, velocity, and on the number and complexity of various data formats and metadata, a set of information that describes other data types. This is nowhere better seen than in the development of the software to support next generation astronomical instruments including the MeerKAT/KAT-7 Square Kilometre Array (SKA) precursor in South Africa, in the Low Frequency Array (LOFAR) in Europe, in two instruments led in part by the U.S. National Radio Astronomy Observatory (NRAO) with its Expanded Very Large Array (EVLA) in Socorro, NM, and Atacama Large Millimeter Array (ALMA) in Chile, and in other instruments such as the Large Synoptic Survey Telescope (LSST) to be built in northern Chile. This chapter highlights the big data challenges in constructing data management systems for these astronomical instruments, specifically the challenge of integrating legacy science codes, handling data movement and triage, building flexible science data portals and user interfaces, allowing for flexible technology deployment scenarios, and in automatically and rapidly mitigating the difference in science data formats and metadata models. The authors discuss these challenges and then suggest open source solutions to them based on software from the Apache Software Foundation including Apache Object-Oriented Data Technology (OODT), Tika, and Solr. The authors have leveraged these solutions to effectively and expeditiously build many precursor and operational software systems to handle data from these astronomical instruments and to prepare for the coming data deluge from those not constructed yet. Their solutions are not specific to the astronomical domain and they are already applicable to a number of science domains including Earth, planetary, and biomedicine.
Renewed Image of Higher Education
Authors: Arslan-Cansever Belgin
Published: April 2016

Rapid technological and scientific developments that have emerged in the second half of the 20th century have profoundly transformed societies, the environment, and human life. With globalization, technologies that require manhandling have transformed into technologies that require educated human labor. In order to reach a certain level of quality, globalization forces education institutions, especially higher education institutions, to change and transform. In this context, organizational justice and culture, formed commonly across institutions of higher education, are expected to improve and increase the quality of education. There is little research on the topic of organizational justice and culture in education organizations. There is almost no study on the impact of these two important concepts on higher education in our globalizing world. Therefore, from an analytical perspective, this section aims to explain the content summarized above and the dynamics through which organizational justice and culture impact the globalization of higher education.
The Relationship Between Social Justice Issues and Emotional Intelligence (EI)
Authors: Chakraborty Misha , Chlup Dominique T.
Published: April 2016

This chapter discussed issues of injustice as often affecting the emotional, and in some cases, the physical well-being of a person. In recent years, researchers have begun to explore the role of Emotional Intelligence (EI) in creating awareness when it comes to social justice issues related to areas such as racism, sexism, heterosexism, etc. The purpose of this chapter is twofold: First, to concentrate on the area of social justice issues to find out what the literature has explored in terms of the role Emotional Intelligence (EI) might play when it comes to dealing with social oppression, and second, to advocate emotional intelligence traits that can be successfully used to cope with social oppression. We reviewed the literature as a way to deepen our understanding of how to foster “socially conscious” practices within the workplace. The chapter has implications for Human Resource Development (HRD) practitioners to remind them of the responsibility they have to encourage and welcome studies and practices addressing critical aspects such as social justice issues as a way to help ensure a productive and safe workplace. Through this review of the literature, we found that emotional intelligence traits, if practiced responsibly, can make society a better place for everyone to live and work in.
A Forensic Psychological Perspective on Racism in Schools of Educational Leadership
Authors: Johnson Ronn , Lee Jojo Yanki , Kim Ji Youn Cindy
Published: April 2016

Schools theoretically operate under a shared value of fairness in the workplace. The fairness includes a notable sense of egalitarian beliefs, values, and people who hold themselves out to be unprejudiced. When defensible and culturally responsive justice measures are applied in schools of education, leadership in Schools of Education must consistently demonstrate clear and convincing evidence of fairness. The chapter is an analysis of aversive or unconscious racism and in the ways that it might manifest itself through individuals in leadership positions. Evaluating the impact of the institutional racism. In the analysis of forensic psychological perspective, ways of using a forensic psychological approach to assess the stakeholders in the School of Education as it pertains to the experience of racism will be discussed. A forensic psychological portrait of racism in schools of education is examined. Finally, implications for practice, training, and research are discussed.
The Impact of the Model Minority Culture in Higher Education Institutions
Authors: Tran Ben
Published: April 2016

Asian American and Pacific Islanders (AAPIs) are one of the fastest growing ethnic communities in the United States. Often referred to as the model minority, first coined by William Peterson, in 1966, AAPIs in the U.S. number approximately 15 million and represent more than 100 languages and dialects. However, few studies have investigated psychological consequences of internalizing the model minority. The purpose of this chapter is delve into the other side of the model minority façade, the less glamorous side of model minority, one of the causes of Asian Americans' psychological and mental health issues: the history of model minority, the concept of stereotype, and the various psychological and mental health issues, the reputation of the concept of psychological issues, and mental health services. While there is sacred empirical evidence in the support of the claim that direct causality exists between model minority and psychological and mental health issues, validities and correlations can be established, through the shared classification of diagnosed symptoms.
Faculty Motivation in Management Education
Authors: Baporikar Neeta
Published: April 2016

It is often observed that some institutions though better endowed in terms of, the management, leadership style, qualification and experience of the faculty and staff, the availability of material resources, yet do not fare well as compared to those where there exists motivated faculty. This is because motivated faculty can drive excellence in institutions though quality teaching and research. Management Educational institutions are no exception. Management education has undergone radical changes in last few decades. The content, instructional methodologies and facilities are among the key indicators of change as is the background and motivation of people who choose to become management teachers. Moreover, in the light of higher education becoming more complex and institutions becoming social undertakings globally, what is it about teaching management education that makes outstanding teachers love their jobs? The chapter delves into these issues and focuses on faculty motivation in management education.
Barriers to and Facilitators of Older Adult's Adherence to Health Recommendations
Authors: Gaspar Rui , Domingos Samuel , Diniz António M. , Falanga Roberto
Published: April 2016

Non-adherence to health recommendations (e.g. medical prescriptions) presents potential costs for healthcare, which could be prevented or mitigated. This is often attributed to a person's rational choice, to not adhere. However, this may also be determined by individual and contextual factors implied in the recommendations communication process. In accordance, this chapter focuses specifically on barriers to and facilitators of adherence to recommendations and engagement with the healthcare process, particularly concerning the communication between health professionals and patients. For this, the authors present examples of engagement increment through different degrees of participation, from a one-way/directive towards a two-way/engaging communication process. This focuses specifically on a vulnerable population group with increasing healthcare needs: older adults. Future possibilities for two-way engaging communications are discussed, aimed at promoting increased adherence to health recommendations and people's self-regulation of their own health.
The Patient Centered Organizational Model in Italian Hospitals
Authors: Gorli Mara , Liberati Elisa G. , Galuppo Laura , Scaratti Giuseppe
Published: April 2016

The concepts of patient-centered care and patient engagement are often used interchangeably in health policy debates. In fact, it is frequently – and implicitly – assumed that putting patients at the centre of care will more or less spontaneously lead to fully engaged patients. This chapter questions these assumptions. By focusing on the hospital's patient-centered reform in Italy, the chapter discusses the overlaps but also the discrepancies between the practices of putting patients at the centre and those of engaging patients. Discrepancies may appear at three levels: the new hospital model may challenge the establishment of a close alliance between patients and professionals, the new hospital spaces may not be perceived as a catalyst for improving patients' experiences, and the inter- and intra- professional struggles derived from the new configuration of hospital teams at an early stage may hinder patient care delivery. The chapter proposes some strategies for patient-centered hospitals to increase their capacity to engage patients and families in the management of their care plan.
Engagement in Health and Safety at the Workplace
Authors: Magnavita Nicola
Published: April 2016

Dramatic changes in the age structure of the population have led to a rise in the age of retirement. An ageing working population may be a problem for companies and for their health and safety services that must face the long-term management of active, chronically ill workers. For sustainability reasons the discipline of occupational medicine must be replaced by occupational health, which not only combats occupational diseases, but actively works to promote the health of older workers. More in general, occupational health has a strong interest in promoting engagement in professional activities. Shifting from a reactive to a proactive logic will take time and require a big effort on the part of employers, employees and health and safety staff in order to develop participatory ergonomics and best health promotion practices in the workplace.
Optimized Energy Consumption and Demand Side Management in Smart Grid
Authors: Ahmad Sadiq , Ahmad Ayaz , Yaqub Raziq
Published: April 2016

This chapter reviews prevailing methodologies and future techniques to optimize energy consumption. It discerns that smart grid provides better tools and equipment to control and monitor the consumer load, and optimize the energy consumption. Smart grid is essentially composed of smart energy equipment, advance metering infrastructure and Phasor Measurement Units (Synchrophaors) that helps to achieve optimized energy consumption. The chapter also places focus on demand side management and optimized energy consumption scheduling; and establishes that both, the utilities, as well as the users can play a vital role in intelligent energy consumption and optimization. The literature review also reveals smart protection, self-healing systems and off-peak operation result in minimizing transmission and distribution losses, as well as optimizing the energy consumption.
Efficient Control Strategies to Optimize Electricity Cost and Consumer Satisfaction
Authors: Minhas Daud Mustafa , Hussain Sajjad
Published: April 2016

Increasing consumer demand of electricity is difficult for the conventional power system network to handle, regarding both cost and infrastructure. Instead of expanding the expensive infrastructure, power engineers are now focusing on improving efficiencies and effectiveness of existing power networks. This chapter specifically focuses on low cost electricity supply, by introducing the novel concept of digital energy management system in hybrid AC/DC micro-grid. It is assumed that grid is partially powered by time varying renewable resources. The concept of minimizing time average electricity cost is introduced by efficient utilization of these renewable resources and by making the load demands more flexible to operate while taking converter losses into account. Real time pricing model is introduced to elaborate the advantage of time-of-use pricing. Control decisions will be achieved by proposing a load scheduling and hybrid switching (LSHS) algorithm. This algorithm will be capable of supplying low cost electricity while serving the load demands under specific delay bounds.