bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Spontaneous Spinal Cerebrospinal Fluid Leaks Disease by Computerized Image Processing
Authors: Kara Sadık , Albayram Mehmet , Okkesim Şükrü , Yıldırım Mustafa
Published: April 2016

Background: Spontaneous Spinal Cerebro -spinal Fluid Leaks (SSCFL) is a disease based on tears on the dura mater. Due to widespread symptoms and low frequency of the disease, diagnosis is problematic. Diagnostic lumbar puncture is commonly used for diagnosing SSCFL, though it is invasive and may cause pain, inflammation or new leakages. T2-weighted MR imaging is also used for diagnosis; however, the literature on T2-weighted MRI states that findings for diagnosis of SSCFL could be erroneous when differentiating the diseased and control. One another technique for diagnosis is CT-myelography, but this has been suggested to be less successful than T2-weighted MRI and it needs an initial lumbar puncture.

Objectives: This study aimed to develop an objective, computerized numerical analysis method using noninvasive routine Magnetic Resonance Images that can be used in the evaluation and diagnosis of SSCFL disease.

Methods: Brain boundaries were automatically detected using methods of mathematical morphology, and a distance transform was employed. According to normalized distances, average densities of certain sites were proportioned and a numerical criterion related to cerebrospinal fluid distribution was calculated.

Results: The developed method was able to differentiate between 14 patients and 14 control subjects significantly with p = 0.0088 and d = 0.958. Also, the pre and post-treatment MRI of four patients was obtained and analyzed. The results were differentiated statistically (p = 0.0320, d = 0.853).

Conclusions: An original, noninvasive and objective diagnostic test based on computerized image processing has been developed for evaluation of SSCFL. To our knowledge, this is the first computerized image processing method for evaluation of the disease. Discrimination between patients and controls shows the validity of the method. Also, post-treatment changes observed in four patients support this verdict.
Characterization of spherical domains at the polystyrene thin film–water interface
Authors: Ahmad Khurshid , Zhao Xuezeng , Pan Yunlu , Hussain Danish
Published: April 2016

Spherical domains that readily form at the polystyrene (PS)–water interface were studied and characterized using atomic force microscopy (AFM). The study showed that these domains have similar characteristics to micro- and nanobubbles, such as a spherical shape, smaller contact angle, low line tension, and they exhibit phase contrast and the coalescence phenomenon. However, their insensitivity to lateral force, absence of long-range hydrophobic attraction, and the presence of possible contaminants and scratches on these domains suggested that these objects are most likely blisters formed by the stretched PS film. Furthermore, the analysis of the PS film before and after contact with water suggested that the film stretches and deforms after being exposed to water. The permeation of water at the PS–silicon interface, caused by osmosis or defects present on the film, can be a reasonable explanation for the nucleation of these spherical domains.
HIERARCHICAL FORMATION IN ACTION: CHARACTERIZING ACCELERATED GALAXY EVOLUTION IN COMPACT GROUPS USING WHOLE-SKY WISE DATA
Authors: Zucker Catherine , Walker Lisa May , Johnson Kelsey , Gallagher Sarah , Alatalo Katherine , Tzanavaris Panayiotis
Published: April 2016

Compact groups provide an environment to study the growth of galaxies amid multiple prolonged interactions. With their dense galaxy concentrations and relatively low velocity dispersions, compact groups mimic the conditions of hierarchical galaxy assembly. Compact group galaxies are known to show a bimodality in Spitzer IRAC infrared color space: galaxies are preferentially either quiescent with low specific star formation rates (SSFRs) or prolifically forming stars—galaxies with moderate levels of specific star formation are rare. Previous Spitzer IRAC studies identifying this “canyon” have been limited by small number statistics. We utilize whole-sky Wide-field Infrared Survey Explorer (WISE) data to study 163 compact groups, thereby tripling our previous sample and including more galaxies with intermediate mid-IR colors indicative of moderate SSFRs. We define a distinct WISE mid-IR color space

log

f

12

f

4.6

versus

log

f

22

f

3.4

that we use to identify canyon galaxies from the larger sample. We confirm that compact group galaxies show a bimodal distribution in the mid-infrared and identify 37 canyon galaxies with reliable photometry and intermediate mid-IR colors. Morphologically, we find that the canyon harbors a large population of both Sa–Sbc and E/S0 type galaxies, and that they fall on the optical red sequence rather than the green valley. Finally, we provide a catalog of WISE photometry for 567 of 652 galaxies selected from the sample of 163 compact groups.
BROWN DWARFS IN YOUNG MOVING GROUPS FROM PAN-STARRS1. I. AB DORADUS
Authors: Aller Kimberly M. , Liu Michael C. , Magnier Eugene A. , Best William M. J. , Kotson Michael C. , Burgett William S. , Chambers Kenneth C. , Hodapp Klaus W. , Flewelling Heather , Kaiser Nick , Metcalf Nigel , Tonry John L. , Wainscoat Richard J. , Waters Christopher
Published: April 2016

Substellar members of young (≲150 Myr) moving groups are valuable benchmarks to empirically define brown dwarf evolution with age and to study the low-mass end of the initial mass function. We have combined Pan-STARRS1 (PS1) proper motions with optical–IR photometry from PS1, Two Micron All Sky Survey (2MASS), and WISE to search for substellar members of the AB Dor Moving Group within ≈50 pc and with spectral types of late M to early L, corresponding to masses down to ≈30 M
Jup at the age of the group (≈125 Myr). Including both photometry and proper motions allows us to better select candidates by excluding field dwarfs whose colors are similar to young AB Dor Moving Group members. Our near-IR spectroscopy has identified six ultracool dwarfs (M6–L4; ≈30–100 M
Jup) with intermediate surface gravities (int-g) as candidate members of the AB Dor Moving Group. We find another two candidate members with spectra showing hints of youth but consistent with field gravities. We also find four field brown dwarfs unassociated with the AB Dor Moving Group, three of which have int-g gravity classification. While signatures of youth are present in the spectra of our ≈125 Myr objects, neither their J – K nor W1 – W2 colors are significantly redder than field dwarfs with the same spectral types, unlike younger ultracool dwarfs. We also determined PS1 parallaxes for eight of our candidates and one previously identified AB Dor Moving Group candidate. Although radial velocities (and parallaxes, for some) are still needed to fully assess membership, these new objects provide valuable insight into the spectral characteristics and evolution of young brown dwarfs.
TWO STARS TWO WAYS: CONFIRMING A MICROLENSING BINARY LENS SOLUTION WITH A SPECTROSCOPIC MEASUREMENT OF THE ORBIT
Authors: Yee Jennifer C. , Johnson John Asher , Skowron Jan , Gould Andrew , Pineda J. Sebastian , Eastman Jason , Vanderburg Andrew , Howard Andrew
Published: April 2016

Light curves of microlensing events involving stellar binaries and planetary systems can provide information about the orbital elements of the system due to orbital modulations of the caustic structure. Accurately measuring the orbit in either the stellar or planetary case requires detailed modeling of subtle deviations in the light curve. At the same time, the natural, Cartesian parameterization of a microlensing binary is partially degenerate with the microlens parallax. Hence, it is desirable to perform independent tests of the predictions of microlens orbit models using radial velocity (RV) time series of the lens binary system. To this end, we present 3.5 years of RV monitoring of the binary lens system OGLE-2009-BLG-020 L, for which Skowron et al. constrained all internal parameters of the 200–700 day orbit. Our RV measurements reveal an orbit that is consistent with the predictions of the microlens light curve analysis, thereby providing the first confirmation of orbital elements inferred from microlensing events.
Software quality information needs
Authors: Graziotin Daniel
Published: April 2016

Modern software methodologies, e.g., agile and continuous integration and deployment, rely on frequent feedback loops to respond to sudden changes. Improvements to feedback, development activities, and quality rely on information needs. We found a lack of understanding on the information needs of software engineers when the software quality is the main concern. I propose the term "software quality information needs" to refer to a software engineer’s eagerness to define, locate, obtain, and use information to satisfy a conscious or unconscious concern towards software quality. I want to understand what information about software quality does a developer need while performing code changes or designing new parts of a system. For understanding the broad research question, I propose three sub-questions, namely (1) How can we conceptualize information needs when dealing with software quality? (2) What information is needed when dealing with software quality? and (3) How can software quality information needs be detected unobtrusively through behavioral patterns? I propose a 24 months plan for a research fellowship comprising of three empirical studies, which will involve 120 students in computer science and the companies Daimler, Porsche, and Bosch. The studies will make use of multidisciplinary research in software engineering and behavioral science. The theoretical results of the execution of this proposal are the software quality information needs theory and instances, which will add to the body of knowledge in software engineering. The practical implications might be large. Providing a software developer with the right kind of information about the current state of and the effect of changes on software quality can prevent catastrophic software failures and avoid opening up security holes.
Revolutionary music or music for revolution? Thirteen paragraphs on Cornelius Cardew: the composer and his communism
Authors: Waite Mike
Published: April 2016

Paragraphs on the English composer: his life and work through different phases, and his musics' political contexts and implications. Many regret Cardew's adoption of maoism from the early 1970s, not least because it led to his bitter renunciation of his earlier avant garde work. A suggestion:
Cardew's political and artistic choices came from dilemmas which anyone concerned with the left and culture can usefully revisit.
Culture, Class, and Communism: The Politics of Rock in the West German 1968
Authors: Brown Timothy Scott
Published: April 2016

Rock and communism were uneasy bedfellows in 1968. This was true in every country in which they came into contact, but nowhere more than in West Germany, where the student movement and counterculture had a particularly strong Marxist flavour, and where the proximity of the Cold War
frontier forced young nonconformists to grapple more forcefully than was typically the case elsewhere with competing conceptions of the nature and proper goals of revolutionary struggle. Occupying a conspicuous position in debates around issues of subcultural authenticity, the dangers of capitalist
recuperation, and the validity of, respectively, communist and anarchist approaches to the revolution, rock music was a key site of the political in 1968.
Religion et communautés juives : pour une approche spatiale de la diversité communautaire
Authors: Endelstein Lucine
Published: April 2016

À partir de l’exemple du monde juif, on donne ici quelques éléments de réflexion sur l’usage et la pertinence de la notion de communauté, et sur l’intérêt de la dimension spatiale pour analyser le fait communautaire liée à la religion. En se démarquant des visions essentialisantes qui considèrent les communautés comme des groupes homogènes et fermés, on souligne le contraste entre la représentation très courante de l’existence d’ « une » communauté juive unie et uniforme, et la réalité de la diversité interne au monde juif. Il s’agit moins de poser des frontières autour d’une communauté que de comprendre la dimension spatiale dans la construction de communautés plurielles.
Les temples hindous et le développement local en Inde du Sud. L’exemple du Tamil Nadu
Authors: Trouillet Pierre-Yves
Published: April 2016

Basé sur six cas d’étude pris dans le contexte régional du Tamil Nadu (le « pays tamoul » en Inde du Sud), l’article montre que les temples hindous participent de multiples façons au développement de leur localité et qu’ils produisent de la centralité capable de faire émerger des phénomènes de développement. Le cas des temples anciens indique notamment que ces lieux de culte ont un effet structurant sur la morphologie urbaine et que des activités économiques sont souvent installées sur des terres leur appartenant. L’étude des nouveaux temples confirme ces observations tout en mettant en valeur des processus d’émergence de développement local liés à différentes stratégies d’acteurs.
L’encadrement urbanistique des lieux de culte : le pouvoir local à l’épreuve de la diversité religieuse à travers l’exemple de Montréal
Authors: Dejean Frédéric
Published: April 2016

Alors que la ville occidentale contemporaine apparaît de prime abord comme l’espace de prédilection du processus de sécularisation et, à ce titre, est la scène d’une crise profonde des institutions et des pratiques religieuses, l’exemple de Montréal que nous traitons dans cet article témoigne de l’émergence de dynamiques nouvelles des faits religieux dans l’espace urbain. Nous montrons, en particulier, comment les arrondissements montréalais se saisissent de la question du « zonage religieux » et expérimentent des cadres réglementaires nouveaux. À ce titre, nous proposons l’idée que l’analyse de la production de l’espace religieux doit tenir compte de ces nouveaux dispositifs qui ont pour effet d’orienter les dimensions spatiales des faits religieux dans l’espace urbain.
Managing Opioid Abuse in Older Adults: Clinical Considerations and Challenges
Authors: Loreck David , Brandt Nicole J. , DiPaula Bethany
Published: April 2016

Opioid use disorder is a public health epidemic. There is increasing attention being given to opioid abuse and overdose in the United States. The overall use of illicit substances by older adults is on the rise and in part can be attributed to the aging of Baby Boomers. Furthermore, much attention is being given to prescription opioid drug overdose, but it is important to note that heroin-related deaths have also increased sharply. Heroin use is part of a larger substance abuse problem, with more than nine in 10 individuals who use heroin also using at least one other drug (e.g., cocaine, prescription opioid medication). The current article highlights treatment approaches, namely buprenorphine, buprenorphine/naloxone, and naltrexone; insurance considerations; and resources to aid in understanding and managing this public health crisis. [
Journal of Gerontological Nursing, 42
(4), 10–15.]
Grandir en une nuit : le discret redécoupage des municipalités allemandes
Authors: Florentin Daniel
Published: April 2016

Le redécoupage des municipalités allemandes est un processus discret et souvent négligé par les études urbaines sur le pays. Il repose sur une situation paradoxale : les municipalités disposent d’une forte autonomie garantie par un droit municipal très touffu, mais le découpage des communes est l’apanage unique des Länder. Le découpage est ainsi une question traitée par l’échelon régional, qui a cherché, depuis une cinquantaine d’années, à constamment transformer la carte municipale en réduisant le nombre de communes. Ces processus de transformation territoriale sont l’objet de contestations, et participent de changements dans la façon de concevoir la ville (allemande) qui sont l’objet de cet article.
On Efficient Acquisition and Recovery Methods for Certain Types of Big Data
Authors: Avirappattu George
Published: April 2016

Big data is characterized in many circles in terms of the three V's – volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.
Aspects Regarding Detection of Sentiment in Web Content
Authors: Bucur Cristian
Published: April 2016

Last decade evolutions on telecommunications changed the way the information is created and presented. New technologies, has allowed the transition from static presentation of information, to a dynamic way, directly involving users. The Web is currently a platform that allows users to interact with each other, to facilitate the exchange of information. Users have become, from mere consumers of information in the online environment, active participants which increases the information content.
Philosophising Data
Authors: Campbell Jackie , Chang Victor , Hosseinian-Far Amin
Published: April 2016

This chapter aims to critically reflect on the processes, agendas and use of Big Data by presenting existing issues and problems in place and consolidating our points of views presented from different angles. This chapter also describes current practices of handling Big Data, including considerations of smaller scale data analysis and the use of data visualisation to improve business decisions and prediction of market trends. The chapter concludes that alongside any data collection, analysis and visualisation, the ‘researcher' should be fully aware of the limitations of the data, by considering the data from different perspectives, angles and lenses. Not only will this add the validation and validity of the data, but it will also provide a ‘thinking tool' by which to explore the data. Arguably providing the ‘human skill' required in a process apparently destined to be automated by machines and algorithms.
A Hierarchical Security Model for Multimedia Big Data
Authors: Chen Min
Published: April 2016

In this chapter, the author proposes a hierarchical security model (HSM) to enhance security assurance for multimedia big data. It provides role hierarchy management and security roles/rules administration by seamlessly integrating the role-based access control (RBAC) with the object-oriented concept, spatio-temporal constraints, and multimedia standard MPEG-7. As a result, it can deal with challenging and unique security requirements in the multimedia big data environment. First, it supports multilayer access control so different access permission can be conveniently set for various multimedia elements such as visual/audio objects or segments in a multimedia data stream when needed. Second, the spatio-temporal constraints are modeled for access control purpose. Finally, its security processing is efficient to handle high data volume and rapid data arrival rate.
Visualization in Learning
Authors: Osinska Veslava , Osinski Grzegorz , Kwiatkowska Anna Beata
Published: April 2016

Visualization is currently used as a data analysis tool and considered a way of communicating knowledge and ideas in many areas of life such as science, education, medicine, marketing, and advertisement. The chapter contains complex interdisciplinary material and attempts to construct a general framework of visualization roles in learning. The structure of this content is presented on Figure 1 as a kind of mind map. Authors emphasize that visualization mechanisms are designed taking into account analytical and content-related potential, timeliness, online availability, and aesthetics. But classical (tabular) forms still remain dominant in information presentation. A good portion of the discussion is dedicated to the alternative solution – non-linear layout, such as network or fractals. Several visualization maps with specifically designed architecture are demonstrated as important elements of contemporary education. Authors consider the potential and implementation of these tools in e-learning platforms. Parallely, they underline the role of interdisciplinary collaboration in map making processes. Researchers in different fields can apply contemporary trends in visualization including natural shape perception, 3D representation problems, as well as the aspects of neuroaesthetics.
A Cognitive Analytics Management Framework (CAM-Part 3)
Authors: Osman Ibrahim H. , Anouze Abdel Latef
Published: April 2016

The main objectives of the chapter are to evaluate the impact of the tsunami of big data, business analytics, and technology on the delivery and diffusion of knowledge around the world through the use of Internet-of-things and to design future academic education and training programs. Global and local trends are analyzed to evaluate the impact of the digital tsunami on the delivery and diffusion of knowledge; to identify the shortage of critical skills, drivers of challenges, hot skills in demand, and salaries in big data/business analytics; to highlight obstacles to make informed decisions. CAM education framework is proposed to design customized higher education and training programs to meet current shortage and future generation with the relevant and rigorous skills to boost productivity growth and to impact society and professional domains in the digital economy. Finally, new ideas on how governments, academic institutions, technology companies, and professional employers can work together to reform the traditional education value chain and integrate the “massive open online courses” to achieve mass diffusion of knowledge, to transform people from loyalty to parties, clergies, and dictatorships to society's loyalty, and to develop a culture of shared-value in a move towards a smarter and fairer planet in the 21st century.
Data Intensive Cloud Computing
Authors: S. Jayalakshmi D. , Srinivasan R. , Srinivasa K. G.
Published: April 2016

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.
Restful Web Service and Web-Based Data Visualization for Environmental Monitoring
Authors: Lee Sungchul , Jo Ju-Yeon , Kim Yoohwan
Published: April 2016

The Nevada Solar Energy-Water-Environment Nexus project collects a large amount of environmental data from a variety of sensors such as soil, atmosphere, biology, and ecology. Mostly, the environmental data is related to a development of renewable energy resources in the Nexus project. The environmental data can have an impact on other research fields if it can easily be shared with other researchers, students, teachers, and general users. Therefore, Nevada Climate Change Portal (NCCP) site was created for Nexus project with a purpose of sharing such data. However, there are some challenges to address in utilizing such data, collecting the data, and sharing the data among the users. In this research, the authors propose Extended Web Service Architecture for solving these challenges. The authors implement Arduino instead of CR1000 as a collector due to its cost effectiveness. The authors also use REST API to overcome the limitations of Arduino. Moreover, the authors experiment with popular Web-based data visualization tools such as Google Chart, Flex, OFC, and D3 to visualize NCCP data.
Parallel Data Reduction Techniques for Big Datasets
Authors: Yıldırım Ahmet Artu , Özdoğan Cem , Watson Dan
Published: April 2016

Data reduction is perhaps the most critical component in retrieving information from big data (i.e., petascale-sized data) in many data-mining processes. The central issue of these data reduction techniques is to save time and bandwidth in enabling the user to deal with larger datasets even in minimal resource environments, such as in desktop or small cluster systems. In this chapter, the authors examine the motivations behind why these reduction techniques are important in the analysis of big datasets. Then they present several basic reduction techniques in detail, stressing the advantages and disadvantages of each. The authors also consider signal processing techniques for mining big data by the use of discrete wavelet transformation and server-side data reduction techniques. Lastly, they include a general discussion on parallel algorithms for data reduction, with special emphasis given to parallel wavelet-based multi-resolution data reduction techniques on distributed memory systems using MPI and shared memory architectures on GPUs along with a demonstration of the improvement of performance and scalability for one case study.
Big Data and National Cyber Security Intelligence
Authors: Rekha A. G.
Published: April 2016

With the availability of large volumes of data and with the introduction of new tools and techniques for analysis, the security analytics landscape has changed drastically. To face the challenges posed by cyber-terrorism, espionage, cyber frauds etc. Government and law enforcing agencies need to enhance the security and intelligence analysis systems with big data technologies. Intelligence and security insight can be improved considerably by analyzing the under-leveraged data like the data from social media, emails, web logs etc. This Chapter provides an overview of the opportunities presented by Big Data to provide timely and reliable intelligence in properly addressing terrorism, crime and other threats to public security. This chapter also discusses the threats posed by Big Data to public safety and the challenges faced in implementing Big Data security solutions. Finally some of the existing initiatives by national governments using Big Data technologies to address major national challenges has been discussed.
Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing
Authors: Cordeschi Nicola , Shojafar Mohammad , Amendola Danilo , Baccarelli Enzo
Published: April 2016

In this chapter, the authors develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. The objective is to minimize the communication-plus-computing energy which is wasted by processing streams of Big Data under hard real-time constrains on the per-job computing-plus-communication delays. In order to deal with the inherently nonconvex nature of the resulting resource management optimization problem, the authors develop a solving approach that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The resulting optimal scheduler is amenable of scalable and distributed adaptive implementation. The performance of a Xen-based prototype of the scheduler is tested under several Big Data workload traces and compared with the corresponding ones of some state-of-the-art static and sequential schedulers.
Web Usage Mining and the Challenge of Big Data
Authors: Abdalla Abubakr Gafar , Ahmed Tarig Mohamed , Seliaman Mohamed Elhassan
Published: April 2016

The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.
Graph Mining and Its Applications in Studying Community-Based Graph under the Preview of Social Network
Authors: Rao Bapuji , Mitra Anirban
Published: April 2016

One of the fundamental tasks in structured data mining is discovering of frequent sub-structures. These discovered patterns can be used for characterizing structure datasets, classifying and clustering complex structures, building graph indices & performing similarity search in large graph databases. In this chapter, the authors have discussed on use of graph techniques to identify communities and sub-communities and to derive a community structure for social network analysis, information extraction and knowledge management. This chapter contributes towards the graph mining, its application in social network using community based graph. Initial section is related literature and definition of community graph and its usage in social contexts. Detecting common community sub-graph between two community graphs comes under information extraction using graph mining technique. Examples from movie database to village administration were considered here. C++ programming is used and outputs have been included to enhance the reader's interest.
Evaluating NoSQL Databases for Big Data Processing within the Brazilian Ministry of Planning, Budget, and Management
Authors: Huacarpuma Ruben C. , Rodrigues Daniel da C. , Serrano Antonio M. Rubio , Lustosa da Costa João Paulo C. , Júnior Rafael T. de Sousa , Leite Lizane , Ribeiro Edward , Holanda Maristela , Araujo Aleteia P. F.
Published: April 2016

The Brazilian Ministry of Planning, Budget, and Management (MP) manages enormous amounts of data that is generated on a daily basis. Processing all of this data more efficiently can reduce operating costs, thereby making better use of public resources. In this chapter, the authors construct a Big Data framework to deal with data loading and querying problems in distributed data processing. They evaluate the proposed Big Data processes by comparing them with the current centralized process used by MP in its Integrated System for Human Resources Management (in Portuguese: Sistema Integrado de Administração de Pessoal – SIAPE). This study focuses primarily on a NoSQL solution using HBase and Cassandra, which is compared to the relational PostgreSQL implementation used as a baseline. The inclusion of Big Data technologies in the proposed solution noticeably increases the performance of loading and querying time.
Applying the Fuzzy Analytical Network Process in Digital Marketing
Authors: Kaltenrieder Patrick , D'Onofrio Sara , Portmann Edy
Published: April 2016

The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today's information overload makes digital marketing optimization, which is needed to continuously improve one's business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities' fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.
Shaping Content Strategies with User Analytics and Identities
Authors: Syed Tricia
Published: April 2016

User data has become the foundation of many businesses. The ability to increase the breadth and depth of user data to analyze trends is now the roadmap for information companies – providing direction for new business, content strategy, print to digital shifts and overall retention and engagement. In this chapter, the author will explore user identity and the three key core data buckets – Profile, Activity, and Behavioral – that define how to decipher audience members and their ‘user records.' The chapter will specifically showcase how user identity shapes editorial strategy, marketing messaging and drives revenue. It will look at the impact specific technologies are having on what data can be captured as well as the complexities around data capture in general – standardization, preservation, storage, relational data opportunities and data optimization.
Big Data Applications in Healthcare
Authors: Ranjan Jayanthi
Published: April 2016

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.
Network Text Analysis and Sentiment Analysis
Authors: Ravaglia Veronica , Zanazzi Luca , Mazzoni Elvis
Published: April 2016

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.
Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks
Authors: Wang Yingxu , Wiebe Victor J.
Published: April 2016

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.
Big Data Analytics in Retail Supply Chain
Authors: Brajesh Saurabh
Published: April 2016

Retail sector is in the state of flux. On one hand they face hurdles such as new technologies, tumultuous economy, new sales & distribution channels, and on the other hand they have rapidly increasing population of demanding consumers. To overcome these challenges, and to remain relevant and competitive in the market, the retail sector needs to have paradigm shift in their approach. If we track and analyze the pattern of purchasing decisions of consumers, we would find that it involves various stages of decision lifecycle. Data generated at many of these stages can be recorded, digitized, and transformed into matrices and strategic information. These matrices & information would prove to be the vital element for retail industries in their strategic decisions. We need to focus on mechanism to extract valuable insights from retail supply chain. These insights could be further leveraged to provide competitive advantage to the retailers and at the same time a better retail experience to the customers.
The Role of Big Data in Radiation Oncology
Authors: El Naqa Issam
Published: April 2016

More than half of cancer patients receive ionizing radiation as part of their treatment and it is the main modality at advanced stages of disease. Treatment outcomes in radiotherapy are determined by complex interactions between cancer genetics, treatment regimens, and patient-related variables. A typical radiotherapy treatment scenario can generate a large pool of data, “Big data,” that is comprised of patient demographics, dosimetry, imaging features, and biological markers. Radiotherapy data constitutes a unique interface between physical and biological data interactions. In this chapter, the authors review recent advances and discuss current challenges to interrogate big data in radiotherapy using top-bottom and bottom-top approaches. They describe the specific nature of big data in radiotherapy and discuss issues related to bioinformatics tools for data aggregation, sharing, and confidentiality. The authors also highlight the potential opportunities in this field for big data research from bioinformaticians as well as clinical decision-makers' perspectives.
Synchronizing Execution of Big Data in Distributed and Parallelized Environments
Authors: Jung Gueyoung , Mukherjee Tridib
Published: April 2016

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.
Big Data Sharing Among Academics
Authors: Kim Jeonghyun
Published: April 2016

The goal of this chapter is to explore the practice of big data sharing among academics and issues related to this sharing. The first part of the chapter reviews literature on big data sharing practices using current technology. The second part presents case studies on disciplinary data repositories in terms of their requirements and policies. It describes and compares such requirements and policies at disciplinary repositories in three areas: Dryad for life science, Interuniversity Consortium for Political and Social Research (ICPSR) for social science, and the National Oceanographic Data Center (NODC) for physical science.
Big Collusion
Authors: Robson Garry , Olavarria C. M.
Published: April 2016

In the post-Snowden digital surveillance era, insufficient attention has been paid to the role of corporations and consumers in the onslaught on digital privacy by the largest surveillance state – the U.S. The distinction between corporations and the government is increasingly difficult to pinpoint, and there exists an exclusive arrangement of data sharing and financial benefits that tends towards the annihilation of individual privacy. Here the role of consumers in facilitating this alliance is examined, with consideration given to the “social” performances treated as free and exploitable data-creating labor. While consumers of the digital economy often assume that everything should be free, the widespread tendency to gratify desires online inevitably leads to hidden costs and consequences. The permanent data extracted from consumer behavior helps agencies sort and profile individuals for their own agendas. This trilateral relationship of ‘Big Collusion' seems to have gained an irreversibly anti-democratic momentum, producing new transgressions of privacy without proper consent.
A Data Mining Approach for Risk Assessment in Car Insurance
Authors: Kašćelan Ljiljana , Kašćelan Vladimir , Novović-Burić Milijana
Published: April 2016

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.
Challenges and Opportunities in Big Data Processing
Authors: Pokorny Jaroslav , Stantic Bela
Published: April 2016

The development and extensive use of highly distributed and scalable systems to process Big Data have been widely considered. New data management architectures, e.g. distributed file systems and NoSQL databases, are used in this context. However, features of Big Data like their complexity and data analytics demands indicate that these concepts solve Big Data problems only partially. A development of so called NewSQL databases is highly relevant and even special category of Big Data Management Systems is considered. In this work we will discuss these trends and evaluate some current approaches to Big Data processing, identify the current challenges, and suggest possible research directions.
From Data to Vision
Authors: Joseph Rhoda
Published: April 2016

This chapter examines the use of big data in the public sector. The public sector pertains to government-related activities. The specific context in this chapter looks at the use of big data at the country level, also described as the federal level. Conceptually, data is processed through a “knowledge pyramid” where data is used to generate information, information generates knowledge, and knowledge begets wisdom. Using this theoretical backdrop, this chapter presents an extension of this model and proposes that the next stage in the pyramid is vision. Vision describes a future plan for the government agency or business, based on the current survey of the organization's environment. To develop these concepts, the use of big data is examined in three different countries. Both opportunities and challenges are outlined, with recommendations for the future. The concepts examined in this chapter are within the constraints of the public sector, but may also be applied to private sector initiatives pertaining to big data.
On the Road to Ephesus
Authors: Roff-Wexler Suzanne
Published: April 2016

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.
Big Data Literacy
Authors: Christozov Dimitar , Toleva-Stoimenova Stefka
Published: April 2016

The chapter addresses the problems of digital divide in the light of learning from data in the era of accumulated, available, and accessible Big Data. The phenomenon of Big Data arose in the last years and offered new dimensions of digital divide: the challenge that the human society faces since the appearance of computer technology. Objectives of this chapter are to highlight problems and barriers in learning from Big Data and to initiate discussion on the ways to overcome those new challenges. The chapter tries to define the “Big Data Phenomenon,” to identify the phases and activities in the process of learning from data, and to relate them to learning from Big Data. As a result, a paradigm of competences and barriers for acquiring Big Data literacy are proposed as a new dimension of literacy in dividing the human society.