bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision-Making With Big Data Using Open Source Business Intelligence Systems
Authors: Bernardino Jorge , Neves Pedro Caldeira
Published: November 2018

The importance of supporting decision making for improving business performance is a crucial, yet challenging task in enterprise management. The amount of data in our world has been exploding and Big Data represents a fundamental shift in business decision-making. Analyzing such so-called Big Data is becoming a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business Intelligence (BI) is a collection of decision support technologies for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. We review the concept of BI as an open innovation strategy and address the importance of BI in revolutionizing knowledge towards economics and business sustainability. Using Big Data with Open Source Business Intelligence Systems will generate the biggest opportunities to increase competitiveness and differentiation in organizations. In this chapter, we describe and analyze four popular open source BI systems - Jaspersoft, Jedox, Pentaho and Actuate/BIRT.
A Synergetic Model for Implementing Big Data in Organizations
Authors: Halaweh Mohanad , El Massry Ahmed
Published: November 2018

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.
A Decentralized Framework for Semantic Web Services Discovery Using Mobile Agent
Authors: Ben Seghir Nadia , Kazar Okba , Rezeg Khaled
Published: November 2018

Web services discovery provided by the UDDI registries is relatively primitive. It does not take into account the continuous growth in the number of services on the Web. The UDDI standard has been proposed and used for Web service publication and discovery. However, it does not allow users to choose the best provider. It does not offer a mechanism to choose a Web service based on its quality. The standard also lacks of sufficient semantic description in the content of Web services, this lack makes it difficult to find and compose suitable Web services during analysis, search, and matching processes. In addition, a central UDDI suffers from one centralized point problem and the high cost of maintenance. To get around these problems, the authors propose in this paper a novel framework based on mobile agent and metadata catalogue for Web services discovery. Their approach is based on user profile in order to discover appropriate Web services, meeting customer requirements, in less time and taking into account the QoS properties.
Cloud Computing for Secure Services in E-Government Architecture
Authors: Hashemi Sajjad , Monfaredi Khalil , Hashemi Seyyed Yasser
Published: November 2018

E-government tries to take advantage of new technology to provide better service to citizens. Some of the main challenges in the face of E-government are query processing high volume applications, data center management, data security and E-government services. Cloud computing can be a good option for responding to these issues and fixing them, and guarantee the realization of E-government, with maximum efficiency and maximum safety. In this paper, the authors propose a novel architecture for E-government by using Cloud computing architecture which can largely increase the integrity and security service in E-government, and also increase users' confidence in the system and may lead to increased participation.
Supply Chain Coordination Based on Web Service
Authors: Pal Kamalendu
Published: November 2018

The importance of integrating and coordinating supply chain business partners have been appreciated in many industries. In the global manufacturing industry, supply chain business partners' information integration is technically a daunting task due to highly disconnected infrastructures and operations. Information, software applications, and services are loosely distributed among participant business partners with heterogeneous operating infrastructures. A secure, and flexible information exchange architecture that can interconnect distributed information and share that information across global service provision applications is, therefore, immensely advantageous. This chapter describes the main features of an ontology-based web service framework for integrating distributed business processes in a global supply chain. A Scalable Web Service Discovery Framework (SWSDF) for material procurement systems of a manufacturing supply chain is described. Description Logic (DL) is used to represent and explain SWSDF. The framework uses a hybrid knowledge-based system, which consists of Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR). SWSDF includes: (1) a collection of web service descriptions in Ontology Web Language-Based Service (OWL-S), (2) service advertisement using complex concepts, and (3) a service concept similarity assessment algorithm. Finally, a business scenario is used to demonstrate functionalities of the described system.
Big Data Mining Using Collaborative Filtering
Authors: Saini Anu
Published: November 2018

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.
Evaluation of Cloud System Success Factors in Supply-Demand Chains
Authors: Soliman Fawzy
Published: November 2018

Cloud systems could help companies to optimize the structure and operation of supply chain and improve organizational performance into a higher level. In this chapter, the advantages and basic operation methods of cloud computing are analyzed, and the essential factors that contribute to the success of the cloud system are evaluated through some business examples. Supply chain firms rely on cloud system to share resources and to achieve integration through the Internet. Cloud systems could transform customer relationship and internal operation to achieve sustainable competitive advantages. Cloud services are providing the ability to share information to the supply chain at any time anywhere. This chapter explores the relationship between supply chain organization and cloud computing. This chapter also discusses the benefits and advantage to evaluate of cloud system in successful factors.
On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees
Authors: Panda Mrutyunjaya
Published: November 2018

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.
Self-Adaptive QoS-Aware Web Service Discovery Using Ontology Approach
Authors: Win Nwe Nwe Htay , Jianmin Bao , Gang Cui , Rehman Saif Ur
Published: November 2018

In recent years, although semantic has been widely used in service discovery mechanisms, it still needs to exploit all semantic aspects included in service documents so that the discovered service can highly be relevant with user request. Moreover, it also needs to consider self-adaptability in discovering the services which can adapt to searching conditions or parameters in order to find other suitable and potential services if no feasible solution could exactly satisfy user QoS requirements. Therefore, this paper proposes a novel self-adaptive QoS-based service discovery mechanism which can adapt the discovery process with the help of semantically structured ontology trees if unexpected results are encountered. The discovery process matches the equivalences between service advertisement and requirement using three similarity evaluation criteria namely concept, attribute and constraint similarity. This discovery process is repeated until feasible solution is found and a set of most suitable services are returned to the users. The authors prototype their system called SQoSD to evaluate the efficiency and adaptability compared with OWLS-CPS and RQSS. The experimental results prove that our mechanism is superior to the other compared mechanisms.
Big Data in Healthcare and Social Sciences
Authors: López Victoria , Urgelés Diego , Sánchez Óscar , Valverde Gabriel
Published: November 2018

Healthcare providers and payers are increasingly turning to Big Data and analytics, to help them understand their patients and the context of their illnesses in more detail. Industry leaders are exploring/using Big Data to reduce costs, increase efficiency and improve patient care. The next future is an innovative approach to improving patient access using lean methods and predictive analytics. Social sciences are very much related to healthcare and both areas develop in a parallel way. In this article, we introduce one example of application: Bip4cast (a bipolar disorder CAD system). This paper shows how Bip4cast deals with different data sources to enrich the knowledge and improve predictive analysis.
Mobile Cloud Resource Management
Authors: Katzis Konstantinos
Published: November 2018

Providing mobile cloud services requires seamless integration between various platforms to offer mobile users optimum performance. To achieve this, many fundamental problems such as bandwidth availability and reliability, resource scarceness, and finite energy must be addressed before rolling out such services. This chapter aims to explore technological challenges for mobile cloud computing in the area of resource management focusing on both parts of the infrastructure: mobile devices and cloud networks. Starting with introducing mobile cloud computing, it then stresses the importance of resource management in the operation of mobile cloud services presenting various types of resources available for cloud computing. Furthermore, it examines the various types of resource management techniques available for mobile clouds. Finally, future directions in the field of resource management for mobile cloud computing environment are presented.
Knowledge Representation Technologies Using Semantic Web
Authors: Kumar Vudattu Kiran
Published: November 2018

The World Wide Web (WWW) is global information medium, where users can read and write using computers over internet. Web is one of the services available on internet. The Web was created in 1989 by Sir Tim Berners-Lee. Since then a great refinement has done in the web usage and development of its applications. Semantic Web Technologies enable machines to interpret data published in a machine-interpretable form on the web. Semantic web is not a separate web it is an extension to the current web with additional semantics. Semantic technologies play a crucial role to provide data understandable to machines. To achieve machine understandable, we should add semantics to existing websites. With additional semantics, we can achieve next level web where knowledge repositories are available for better understanding of web data. This facilitates better search, accurate filtering and intelligent retrieval of data. This paper discusses about the Semantic Web and languages involved in describing documents in machine understandable format.
Teaching Visualisation in the Age of Big Data
Authors: Chiera Belinda A. , Korolkiewicz Malgorzata W.
Published: November 2018

Technological advances have led to increasingly more data becoming available, a phenomenon known as Big Data. The volume of Big Data is to the order of zettabytes, offering the promise of valuable insights with visualisation the key to unlocking these insights, however the size and variety of Big Data poses significant challenges. The fundamental principles behind tried-and-tested methods for visualising data are still as relevant as ever, although the emphasis necessarily shifts to why visualisation is being attempted. This chapter outlines the use of graph semiotics to build data visualisations for exploration and decision-making and the formulation of elementary, intermediate- and overall-level analytical questions. The public scanner database Dominick's Finer Foods, consisting of approximately 98 million observations, is used as a demonstrative case study. Common Big Data analytic tools (SAS, R and Python) are used to produce visualisations and exemplars of student work are presented, based on the outlined visualisation approach.
Cloud Computing for Rural ICT Implementations
Authors: Firdhous Mohamed Fazil Mohamed
Published: November 2018

Half of the world's population live in rural areas and majority of them are in developing countries. The rural population face many challenges in their life compared to their urban counterparts. Some of these challenges include high unemployment rate, limited employment opportunities in their areas, high brain drain to more developed cities, lack of access to education and healthcare facilities. Information and communication technology has been identified as the enabling technology that can be used to overcome the present day problems. There are several ICT projects implemented across the world with the objective of helping these rural masses. But many of these projects face sustainability challenges due to lack of resources. In this chapter, the author takes an in depth look at how cloud computing can be leveraged successfully to address the sustainability problem of current rural ICT implementation.
Big Data Analytics With Service-Oriented Architecture
Authors: Mukherjee Triparna , Nath Asoke
Published: November 2018

This chapter focuses on Big Data and its relation with Service-Oriented Architecture. We start with the introduction to Big Data Trends in recent times, how data explosion is not only faced by web and retail networks but also the enterprises. The notorious “V's” – Variety, volume, velocity and value can cause a lot of trouble. We emphasize on the fact that Big Data is much more than just size, the problem that we face today is neither the amount of data that is created nor its consumption, but the analysis of all those data. In our next step, we describe what service-oriented architecture is and how SOA can efficiently handle the increasingly massive amount of transactions. Next, we focus on the main purpose of SOA here is to meaningfully interoperate, trade, and reuse data between IT systems and trading partners. Using this Big Data scenario, we investigate the integration of Services with new capabilities of Enterprise Architectures and Management. This has had varying success but it remains the dominant mode for data integration as data can be managed with higher flexibility.
Big Data and Innovation in the Delivery of Public Services
Authors: Asquer Alberto
Published: November 2018

This chapter aims to discuss the role of Information and Communication Technologies (ICTs)—especially of the so-called Big Data—in the innovation of public service delivery. After reviewing the relevant literature on innovation and innovation diffusion in the public sector and the rise of Big Data, the chapter presents a narrative of the case of the adoption and implementation of predictive policing in Kent County Police, UK, as an instance of (early) application of Big Data in public sector organizations. On the whole, the analysis of the case suggests that past conditions and adoption strategies play an important role in the introduction and acceptance of innovative work practices that exploit the potential of contemporary ICTs.
Business Applications of Big Data
Authors: Vidal-García Javier , Vidal Marta , Barros Rafael Hernández
Published: November 2018

Twenty-first century organizations face a challenge and an unprecedented opportunity: to build a new model of relationship with current and potential clients which is more efficient and innovative. And this necessarily implies to acquire a completely new corporate culture in which the collection, management and interpretation of information will inspire the entire business. Under this new business scenario, the data is not understood as part of a process but instead to be part of the core business as a decision tool. Only if companies can manage this flood of information will be able to understand what we do and where they are going; identify the tastes and preferences of consumers and, more importantly, anticipate their decisions to adapt the services in real time and in a personalized way. The Big Data points companies beyond the facts: transforms the actions into predictions.
Big Data Transforming Small and Medium Enterprises
Authors: Schaeffer Donna M. , Olson Patrick C.
Published: November 2018

The terms big data, analytics, and business intelligence are often used in the media, with much attention on Fortune 500 enterprises. Small and medium-sized businesses (SMEs) also handle large amounts of data, and it is important to their decision making and planning. This chapter explores options for handling Big Data in SMEs. It presents a framework that considers not just the volume of data, but the variety of types of data, the velocity in which data is created and transmitted, the importance of data veracity, and its value in transforming small and medium-sized enterprises. SMEs need to work with big data, and doing so will impact their business models and require them transform themselves. Their transformation will be ongoing because all indicators show that the volume of data is rising and will continue to do so simply because of the trends related to customer interaction.
Biometric Authentication for the Cloud Computing
Authors: Jaiswal Sumit , Kumar Santosh , Patel Subhash Chandra , Singh R. S. , Singh S. K.
Published: November 2018

As the data being stored to a distant server away from direct control of user cloud presents various security risks and threat issues associated with the user authentication and access control mechanisms, it is of upmost importance to ensure the security of confidential business data in the cloud storage along with making sure that only properly authenticated and authorized personnel can access the data and applications in the cloud. An important step in this regard is to execute biometric security mechanisms, which increases the competence level of security and only permits authenticated individuals by verifying different biometric parameters of human biometric characteristics (traits): patterns like fingerprints, retina, iris, voice, face, ear, palm, signature, and DNA recognition. Implementation of biometric authentication mechanism will take security of data and access control in cloud to higher level. This chapter discusses how a proposed biometrics system with respect to other recognition systems so far is more advantageous and result-oriented because it does not work on presumptions: it is unique and provides fast and contact-less authentication.
A New Approach in Cloud Computing User Authentication
Authors: Al-Hamami Alaa Hussein , Al-Khashab Rafal A.
Published: November 2018

Cloud computing provides the full scalability, reliability, high performance and relatively low cost feasible solution as compared to dedicated infrastructure. These features make cloud computing more attractive to users and intruders. It needs more and complex security measures to protect user privacy and data centers. The main concern in this chapter is security, privacy and trust. This chapter will give a discussion and a suggestion for using cloud computing to preserve security and privacy. The malicious hacker and other threats are considering the major cause of leaking security of the personal cloud due to centralized location and remote accesses to the cloud. According to attacks, a centralized location can be easier target rather than several goals and remote access is insecure technologies which offer a boundary of options for attackers to infiltrate enterprises. The biggest concern is attackers that will use the remote connection as a jumping point to get deeper into an organization.
Big Data and Healthcare
Authors: Moahi Kgomotso H.
Published: November 2018

Big data and its application to healthcare has captured the world's imagination because of the ability of data analysts to combine huge disparate datasets and be able to produce trends, patterns and predictions. This ability lends itself to the quest to improve healthcare in terms of quality as well as cost. This chapter explores what big data is and how it can be applied to health care and medicine. To do this, the first sections address the question of what big data and data analytics are and what they encompass. An exploration of the potential benefits of big data is provided, with examples of applications, most of which are from the more developed nations of the United States and Europe. The chapter then considers what might be possible from implementing big data in low resource countries, with some examples of what already pertains. It looks at the challenges of implementing big data in health care in both developed and low resource countries.
Autonomic Execution of Web Service Composition Using AI Planning Method
Authors: Yuan Chao-Qun , Chua Fang-Fang
Published: November 2018

Web Service Composition is one of the technologies in Service Oriented Architecture which significantly increases the flexibility and reusability of developing service-oriented system. One of the major problems which occurs in web service composition is the difficulties of maintaining the existing running web service composition solutions due to the changes of business requirements, deployment environment, and other dynamic factors. In this proposed work, an automated system had been built to autonomously execute the web service composition. To achieve this objective, the authors had embedded semantic engine and Prolog in C# program to automatically and dynamically discover, compose and execute web service composition, i.e. a web service composition could be self-configured to automatically recover from execution failure and automatically re-generate composition solution due to business protocol changes.
Need of Hadoop and Map Reduce for Processing and Managing Big Data
Authors: Narayanapppa Manjunath Thimmasandra , Channabasamma A. , Hegadi Ravindra S.
Published: November 2018

The amount of data around us in three sixty degrees getting increased second on second and the world is exploding as a result the size of the database used in today's enterprises, which is growing at an exponential rate day by day. At the same time, the need to process and analyze the bulky data for business decision making has also increased. Several business and scientific applications generate terabytes of data which have to be processed in efficient manner on daily bases. Data gets collected and stored at unprecedented rates. Moreover the challenge here is not only to store and manage the huge amount of data, but even to analyze and extract meaningful values from it. This has contributed to the problem of big data faced by the industry due to the inability of usual software tools and database systems to manage and process the big data sets within reasonable time limits. The main focus of the chapter is on unstructured data analysis.
Differential Privacy Approach for Big Data Privacy in Healthcare
Authors: Moussa Marmar , Demurjian Steven A.
Published: November 2018

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.
A Survey Study of Smartphones Behavior in Brunei
Authors: Anshari Muhammad , Alas Yabit , Sabtu Norakmarul Ihsan binti Pg Hj , Yunus Norazmah
Published: November 2018

The use of smartphones is generating large amounts of data. The term “big data” has become popular in describing a massive data, both structured and unstructured. Big data refers to high volume, high velocity, and high variety. This study used an inductive approach involving the distribution of a questionnaire to gain an understanding of smartphone users' habits in Brunei. Then, the authors identified several concerns that became the focal point of further analysis in the use of smartphones in daily activities. The findings is correlated to the growing of trends of big data application for users' benefit. The finding suggests that there is growing demand from smartphone users could create better services and value through big data application. The authors propose a big data approach that will enable service providers to enhance smartphone users' experiences. Big data application will enable smartphones to become smarter in terms of service and value.
Opportunities and Challenges of Big Data in Public Sector
Authors: Aggarwal Anil K.
Published: November 2018

Data has always been the backbone of modern society. It is generated by individuals, businesses and governments. It is used in many citizen-centric applications, including weather forecasts, controlling diseases, monitoring undesirables etc. What is changing is the source of data. Advances in technology are allowing data to be generated from any devise at any place in any form. The challenge is to “understand”, “manage” and make use of this data. It is well known that government generates unprecedented amount of data (ex: US census), the question remains: can this data be combined with technology generated data to make it useful for societal benefit. Governments and non-profits, however, work across borders making data access and integration challenging. Rules, customs and politics must be followed while sharing data across borders. Despite these challenges, big data application in public sector are beginning to emerge. This chapter discusses areas of government applications and also discusses challenges of developing such systems.
Strategic Management of Data and Challenges for Organizations
Authors: Roberts Stephen Andrew , Laurie Bruce
Published: November 2018

Public, organizational and personal data has never been so much in the forefront of discussion and attention as at the present time. The term ‘Big Data' (BD) has become part of public discourse, in the press, broadcast media and on the web. Most people in the wider public have very little idea of what it is and what it means but anyone who gives it a thought will see it as contemporary and relevant to life as much as to business. This paper is directed towards the perspectives of people working in, managing and developing organizations which are dedicated to fulfilling their respective purposes. All organizations need to understand their strategic purpose and to develop strategies and tactical responses accordingly. The organizations' purpose and the frameworks and resources adopted are part of its quest for achievement which creates value and worth. BD is a potential and actual source of value.
Exploring Information Security Governance in Cloud Computing Organisation
Authors: Gangwar Hemlata , Date Hema
Published: November 2018

The paper reveals factors impacting information security governance within the cloud computing technology implementation in organizations. Case study methodology was used and 15 semi-structured interviews were conducted with directors and information security professionals from 5 different types of organizations. The main component that were identified as playing a significant role in information security governance were: information security strategy, security policies and procedure, risk management and assessment program, compliance and standard, monitoring and auditing, business continuity and disaster recovery, asset management and access control and identity management. The results show that awareness through education and training of employees needs to be given very particular attention in cloud computing security. The paper does not include any end-user perspective in interviews and this end-user context is missing. Companies need to focus upon awareness through education and training of employees. Moreover, management and employee support is the critical component of the effective information security governance framework implementation. Also, organisations should develop their information security using a very precise and detailed planning process that ensures the right cloud computing acceptance by the users. The proposed information security governance framework offers organisations a holistic perspective for governing information security, and minimizes risk and cultivates an acceptable level of information security culture.
Legal Responses to the Commodification of Personal Data in the Era of Big Data
Authors: Douilhet Emile , Karanasiou Argyro P.
Published: November 2018

Big Data is a relatively recent phenomenon, but has already shown its potential to drastically alter the relationship between businesses, individuals, and governments. Many organisations now control vast amounts of raw data, and those industry players with the resources to mine that data to create new information have a significant advantage in the big data market. The aim of this chapter is to identify the legal grounds for the ownership of big data: who legally owns the petabytes and exabytes of information created daily? Does this belong to the users, the data analysts, or to the data brokers and various infomediaries? The chapter presents a succinct overview of the legal ownership of big data by examining the key players in control of the information at each stage of processing of big data. It then moves on to describe the current legislative framework with regard to data protection and concludes in additional techno-legal solutions offered to complement the law of big data in this respect.
Cloud Security Using Face Recognition
Authors: Kumar Santosh , Sadhya Debanjan , Singh Durgesh , Singh S. K.
Published: November 2018

Establishing identity has become very difficult in the vastly crowded cloud computing environment. The need for a trustworthy cloud authentication phenomenon has increased in the wake of heightened concerns about authentication and rapid advancements in cloud computing, database access, and Internet communication. Face recognition is a non-intrusive method, and facial characteristics are probably most common biometrics features used by humans to identify others. Authentication for cloud computing using face recognition is based on security issues related to data access and cloud database in a cloud. It can provide a satisfactory level of security measures to users and service providers, cloud consumers, and different organizations. In this chapter, the authors cover different research aspects related to cloud security.
Survey Paper on Semantic Web
Authors: Unadkat Rimpal
Published: November 2018

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. However, to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper the authors present survey on the role of search engines in intelligent web, Background, Challenges and some issues.
Big Data and Data Analytics Research
Authors: Lytras Miltiadis D. , Raghavan Vijay , Damiani Ernesto
Published: November 2018

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.
Marketing 4.0
Authors: Jiménez-Zarco Ana Isabel , Rospigliosi Asher , Martínez-Ruiz María Pilar , Izquierdo-Yusta Alicia
Published: November 2018

Marketing evolves in parallel with technology. During the last five years, Marketing 3.0 has become the most innovative marketing approach, but of growing, is research focusing on Marketing 4.0: the marketing of big data. Much has been speculated, but academic journals have published little about Marketing 4.0. Maybe, because the total understanding of Marketing 4.0 requires: firstly, a depth knowledge about the evolution of marketing, especially about Marketing 3.0, and secondly, an analysis of how a range of technology –not only the Internet and social media- can be used to design marketing strategies that enhance the brand-consumer relationship. Taking into account how consumers' behavior has been changing since the beginning of this century, this chapter seeks to review Marketing 4.0 concepts, analyzing how big data can be used to enhance the consumer-brand relationship.
Security and Privacy Issues of Big Data
Authors: Moura José , Serrão Carlos
Published: November 2018

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.
Computational Systems Biology Perspective on Tuberculosis in Big Data Era
Authors: Kahlon Amandeep Kaur , Sharma Ashok
Published: November 2018

The major concern in this chapter is to understand the need of system biology in prediction models in studying tuberculosis infection in the big data era. The overall complexity of biological phenomenon, such as biochemical, biophysical, and other molecular processes, within pathogen as well as their interaction with host is studied through system biology approaches. First, consideration is given to the necessity of prediction models integrating system biology approaches and later on for their replacement and refinement using high throughput data. Various ongoing projects, consortium, databases, and research groups involved in tuberculosis eradication are also discussed. This chapter provides a brief account of TB predictive models and their importance in system biology to study tuberculosis and host-pathogen interactions. This chapter also addresses big data resources and applications, data management, limitations, challenges, solutions, and future directions.
Application of Big Data in Economic Policy
Authors: Mishra Brojo Kishore , Sahoo Abhaya Kumar
Published: November 2018

Now-a-days main factor of any information in different fields is data. Different database like relational, object-oriented database is very different from traditional database in case of data retrieval, data management and data updation. So, Data mining is a process of extracting hidden knowledge or useful pattern from huge amount of data in case of efficient databases. As amount of data increases day by day, so it is very difficult to manage related data to different fields. Big data analysis takes a major role for accessing, managing and manipulating of both structured and unstructured data. It is possible to transform business, government and other aspects of the economy by big data. In this chapter, we discuss how big data analytics can be applied in economic policy of government and private sector for making better decision. Here we can use map-reduce programming model provided by Hadoop for analysis of big data in economic policy management.
Protected Natural Areas and Sustainable Development
Authors: Marian Zaharia , Gogonea Rodica-Manuela , Andrei Daniela Ruxandra
Published: November 2018

The process of tourism development has come to include, step by step, the expansion potential of areas where it could be practiced in less accessible natural spaces, which are more problematic from the point of view of tourist penetration and the organization of tourism activities. In this context, making tourism under the umbrella of this concept of expansion, has led, on the one hand, to the expansion of protected natural areas, to their advertising and implicitly to the increase of demand for this type of tourism, and, on the other hand, to the amplification of danger posed to the integrity of the ecosystems included in the tourism circuit. The paper, starting from the actual context of sustainable development, highlights the fact that the tourism potential of protected natural areas constitute an important factor for sustainable development only, if is doing in condition of responsibility and respect for environmental conservation and regeneration of environmental resources
The Global Implications of Ecotourism in Emerging Economies
Authors: Birău Felicia Ramona
Published: November 2018

The main purpose of this chapter is to highlight the global implications of ecotourism in emerging economies. In the recent past, the tourism industry has become a growing segment of the economy with an impressive expansion potential. Nevertheless, ecotourism in emerging economies establishes a great interdisciplinary research interest. Ecotourism has become extremely popular and highly beneficial on development of emerging economies. The definition of ecotourism is very complex and cannot be compressed in pure theory. Its practical implications are extremely heterogeneous. Ecotourism fundamentally contributes to nature conservation and improvement of livelihoods particularly in rural areas. The original approach provides a complex empirical analysis based on certain emerging economies, i.e. Poland, Brazil, India, China, Russia, Hungary, Slovenia, Slovakia, Romania and Bulgaria in the turbulent context of the global financial crisis.
From Earning Profits to Sustainability
Authors: Arora Manpreet , Walia Sandeep Kumar
Published: November 2018

In India the potential of tourism sector is considerable in terms of earning foreign exchange. That is why the government has initiated many programmes and schemes to attract foreign tourists. It has also given a rise to the allied industries to earn profits and diversify may it be hotel industry or aviation sector. As the means of communication are developing and the countries are becoming more open and globalised the tourism industry has got new impetus and dimensions. The spread of education and the development of internet have widened the scope of this industry. This new trend has made the tourism industry a very potential industry in many terms for a developing nation like India. But on the same hand it has posed a great threat to the natural resources also and the stakeholders involved in this sector has started responding to such initiatives through CSR measures. For measuring such initiatives no specific parameters have been defined and they are subjective to a great extent. Therefore a need arises to analyse such initiatives and their growing need for creating sustainability.
Sustainable Tourism Development in Asia
Authors: Herath Gamini
Published: November 2018

Sustainable Tourism as a concept respects both local people, the traveler, cultural heritage and the environment. It should meet the needs of and opportunity for the future. But operationalizing sustainable tourism is problematic due to analytical weaknesses, conceptual criticisms and the need to redefine better perspectives. Asia faces many challenges in promoting sustainable tourism including political leadership, poor participation by all stakeholders, adverse environmental and social impacts. Another challenge is to ensure social sustainability and creating opportunities to transfer income to the poorer. The empirical study of island tourism in Malaysia shows that Marine Parks regulations are not adequately enforced and Malaysian coral reefs can suffer from increasing degradation. Water pollution, overfishing, beach erosion, coral bleaching and damage, fishing, snorkeling and poor recycling and sewage disposal all are still significant challenges to the Marine Park in in Tioman Island in Malaysia.
Evaluating the Possibilities of Improving the Quality of Tourism Services of the “Eco- Guesthouses” From Mărginimea Sibiului
Authors: Nicula Virgil , Popșa Roxana Elena
Published: November 2018

This paper provides an overview of the possibilities of improving the quality of tourism services of the “eco-guesthouses” from Mărginimea Sibiului in the context where the international studies indicate that lately tourists have become more experienced and expect good quality products in chosen holiday destinations. Currently, the international market of ecotourism has developed with 10-12% annually, having the fastest growth in the world tourism sector and tending to become an independent market segment, under the influence of factors such as: travelers prefer to choose educational holidays, their desire to escape to nature and spend quality time with friends or family, etc. In these circumstances, the quality is important for both tourists and eco-guesthouses, as it can contribute to making better products at lower costs, ensuring continuous customer orientation and thus controlling and shaping their preferences and expectations.
Exploring the Impact of Tourism to the Sustainable Development of Mountain Regions
Authors: Tsiaras Stefanos
Published: November 2018

Sustainable development is difficult to be achieved in mountain regions because of their sensitivity to climate change. The rapid development of mountain tourism worldwide during the last years makes the goal for sustainable mountain tourism even more challenging. Key factor to the development of mountain tourism in Europe was the function of ski centres. According to the spirit of the Brundtland Report ski centres are not in general sustainable, because they may have economic benefits but they negatively affect the other two pillars of sustainability: environment and society. The present paper focuses on a ski resort in Greece, attempting to assess the impact of mountain tourism on the sustainable development of the area. Ski resorts played a crucial role to the bloom of mountain tourism in Greece. Its evolution is a relatively recent phenomenon which has been affected by the economic crisis that afflicts the country in the past years. Demographic data compared with tourism indicators were used to assess the environmental impact of tourism in the area. Moreover, meteorological data were used in order to examine the correlation of climatic conditions and the number of visitors. The results show that the ski centre in Elatohori despite the economic benefits has a major environmental impact, especially because of the increased carbon emissions caused by the visitors of the area. Additionally, the climatic conditions (precipitation and temperature, especially the absolute maximum air temperature) affect the number of visitors.
Regional Sustainable Development
Authors: Vargas-Hernández José G.
Published: November 2018

This study aims to determine the potential of cultural tourism in the municipalities that comprise region 6 of the State of Jalisco, territorially delimited in the South of Jalisco. These municipalities are tested first to identify the demand for cultural tourism to determine the profile of the cultural tourist under the assumptions that tourists especially motivated by culture tend to travel longer distances than most tourists. It discusses the motivations and satisfactions of cultural tourists in order to establish the potential market in accordance with the characteristics of the target market in the southern region of Jalisco. Any operation of cultural tourism companies should make the strategic diagnosis, which explains the use of SWOT analysis as a tool for strategic planning of cultural tourism enterprises. Finally, we propose some development strategies of cultural tourism in this region of Southern Jalisco.