Big Data Visualization Tools and Techniques

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

Managerial Controversies in Artificial Intelligence and Big Data Analytics

This chapter discusses several fundamental and managerial controversies associated with artificial intelligence and big data analytics which will be of interest to quantitative professionals and practitioners in the fields of computing, e-commerce, e-business services, and e-government. The authors utilized the systems thinking technique within an action research framework. They used this approach because their ideology was pragmatic, the problem at hand, was complex and institutional (healthcare discipline), and they needed to understand the problems from both a practitioner and a nonhuman technology process viewpoint. They used the literature review along with practitioner interviews collected at a big data conference. Although they found many problems, they considered these to be already encompassed into the big data five V's (volume, velocity, variety, value, veracity). Interestingly, they uncovered three new insights about the hidden healthcare artificial intelligence and big data analytics risks; then they proposed solutions for each of these problems.

Pemanfaatan Big Data pada Instansi Pelayanan Publik

Munculnya internet telah merevolusi cara dunia bekerja begitu cepat. Dunia kini memasuki era digitalisasi, era yang menjadi pola ekonomi digital dan big data. Big Data melibatkan proses pembuatan data, penyimpanan, penggalian informasi, dan analisis data yang menonjol dalam hal volume, velocity, dan variasi. Bagi industri atau praktisi, big data telah membuka peluang untuk menetapkan strategi bisnis. Penelitian ini ingin melihat sejauh mana teknologi Big Data telah digunakan di Instansi Pelayanan Publik Indonesia. Metode penelitian menggunakan studi pustaka dengan pendekatan tradisional-konseptual. Data primer adalah jurnal yang sudah berhubungan dengan topik yang penulis pelajari dan beberapa berita yang bersumber dari media sosial. Kesimpulan penggunaan Big Data di Indonesia sudah mulai berkembang di sektor publik. Hasil kajian pustaka dari penelitian ini merupakan kerangka konseptual yang akan dikembangkan untuk penelitian selanjutnya.

Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach

The era of Big Data has provided business organisations opportunities to improve their management processes. This developmental paper is adopting a mixed-method research approach where qualitative data will underpin a quantitative questionnaire. The early insights are based on an initial eleven qualitative interviews and conceptualised in the following three statements: (i) Project practitioners need to increase their data literacy; (ii) Project practitioners are not utilising the available Big Data based on the 3 Vs; Volume, Velocity and Variety; (iii) Project practitioners need to utilise the structured available data to augment the decision-making process to represent the complex environment of Big Data, the study adopts Complexity Theory as a theoretical framework. When completed, the research will demonstrate the results through System Dynamics modelling.

Support for Adaptive and Distributed Deployment of CEP Continuous Queries for the IoMT

The current dissemination of IoT increases the deployment of stream processing solutions for monitoring and controlling elements of the real-world. One of those solutions is Complex Event Processing (CEP), and to handle the high volume, velocity and volatility of data streams from IoT sensors the CEP pipeline should be distributed, preferably having CEP operators both in the cloud/cluster and in edge devices. In this paper, we present a model for a distributed CEP platform and an implementation of this model called Global CEP Manager (GCM). GCM is a service of the ContextNet middleware that supports the deployment and dynamic rearrangement of CEP queries to CEP engines executing in the cloud and in M-Hubs, that are ContextNet’s mobile edge devices.

Big data and atrial fibrillation – where we are?

The term Big data defines set of data that is characterized by its volume, velocity and variety. The authors present basic concepts of Big Data acquisition and analysis together with contemporary examples of its utilization in diagnosis and treatment of atrial fibrillation.

Artificial intelligence or manufactured stupidity? The need for injury informaticians in the big data era

The volume, velocity and variety of data collected about individuals have increased exponentially over the last decade, presenting new injury surveillance opportunities to identify risk factors, monitor trends, and evaluate the efficacy of interventions. But does the hype around big data and artificial intelligence (AI) apply to the injury prevention space, and how veracious is surveillance in this era? This commentary discusses the digital transformation of health as applied to injury prevention, but cautions on the challenges of maintaining data quality in integrated systems and discusses the need for an injury informatics strategy moving forward.