Importance of Big Data and Hadoop in E-Servicing

2018 ◽

pp. 39-52

Author(s):

Karthiga Shankar ◽

Suganya R.

Keyword(s):

Big Data ◽

Data Analytics ◽

Opinion Mining ◽

Big Data Analytics ◽

Online Information ◽

Campaign Strategies ◽

Security Issues ◽

Customer Group ◽

Big Data Applications ◽

The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

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Big Data Analytics for Search Engine Optimization

Big Data and Cognitive Computing ◽

10.3390/bdcc4020005 ◽

2020 ◽

Vol 4 (2) ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Ioannis C. Drivas ◽

Damianos P. Sakas ◽

Georgios A. Giannakopoulos ◽

Daphne Kyriaki-Manessi

Keyword(s):

Big Data ◽

Cultural Heritage ◽

Search Engine ◽

Data Analytics ◽

User Behavior ◽

Big Data Analytics ◽

Data Types ◽

Search Engine Optimization ◽

The Impact ◽

The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

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Big Data Applications in Healthcare

Big Data ◽

10.4018/978-1-4666-9840-6.ch056 ◽

2016 ◽

pp. 1247-1259 ◽

Cited By ~ 2

Author(s):

Jayanthi Ranjan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Healthcare Industry ◽

Big Data Applications ◽

Business Functions ◽

Automated Algorithms ◽

Almost All ◽

Tools And Techniques ◽

Research Centres

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.

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A Big Data Analytics Approach for the Development of Advanced Cardiology Applications

Information ◽

10.3390/info11020060 ◽

2020 ◽

Vol 11 (2) ◽

pp. 60 ◽

Cited By ~ 2

Author(s):

Lorenzo Carnevale ◽

Antonio Celesti ◽

Maria Fazio ◽

Massimo Villari

Keyword(s):

Big Data ◽

Data Analytics ◽

Response Times ◽

Distributed Processing ◽

Big Data Analytics ◽

Reliable System ◽

Huge Amount ◽

Ecg Signals ◽

Big Data Applications ◽

Electrocardiogram Ecg

Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues. In this paper, we focus on the arrhythmia detection problem. Specifically, our objective is to address the problem of distributed processing considering big data generated by electrocardiogram (ECG) signals in order to carry out pre-processing analysis. Specifically, an algorithm for the identification of heartbeats and arrhythmias is proposed. Such an algorithm is designed in order to carry out distributed processing over the Cloud since big data could represent the bottleneck for cardiology applications. In particular, we implemented the Menard algorithm in Apache Spark in order to process big data coming form ECG signals in order to identify arrhythmias. Experiments conducted using a dataset provided by the Physionet.org European ST-T Database show an improvement in terms of response times. As highlighted by our outcomes, our solution provides a scalable and reliable system, which may address the challenges raised by big data in healthcare.

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Big data analytics for proactive industrial decision support

atp magazin ◽

10.17560/atp.v58i09.580 ◽

2016 ◽

Vol 58 (09) ◽

pp. 62 ◽

Cited By ~ 10

Author(s):

Martin Atzmueller ◽

Benjamin Klöpper ◽

Hassan Al Mawla ◽

Benjamin Jäschke ◽

Martin Hollender ◽

...

Keyword(s):

Big Data ◽

Data Analytics ◽

Historical Data ◽

Big Data Analytics ◽

Lessons Learned ◽

Support Functions ◽

Big Data Applications ◽

Big Data Technologies ◽

Process Plants ◽

Plant Data

Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.

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Big Data Analytics and Firm Performance: A Systematic Review

Information ◽

10.3390/info10070226 ◽

2019 ◽

Vol 10 (7) ◽

pp. 226 ◽

Cited By ~ 8

Author(s):

Parisa Maroufkhani ◽

Ralf Wagner ◽

Wan Khairuzzaman Wan Ismail ◽

Mas Bambang Baroto ◽

Mohammad Nourani

Keyword(s):

Systematic Review ◽

Big Data ◽

Firm Performance ◽

Empirical Research ◽

Data Analytics ◽

Web Of Science ◽

Big Data Analytics ◽

Future Research ◽

Science Index ◽

The Web

The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

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