Suchergebnisse

Big Data Analytics has been in advance more attention recently since researchers in business and academic world are trying to successfully mine and use all possible knowledge from the vast amount of data generated and obtained. Demanding a paradigm shift in the storage, processing and analysis of Big Data, traditional data analysis methods stumble upon large amounts of data in a short period of time. Because of its importance, the U.S. Many agencies, including the government, have in recent years released large funds for research in Big Data and related fields. This gives a concise summary of investigate growth in various areas related to big data processing and analysis and terminate with a discussion of research guidelines in the similar areas.

Today, stand-alone computers and devices can be injected by viruses using drones and aircraft to cripple a nation's cyber capability. Air Gaps placed at critical points in cyber infrastructure does not provide protection against a cyber-attack anymore. U.S. has been flying EC-130H "Compass Call" on daily missions to deny ISIS military leaders and fighters the ability to communicate and coordinate defensive actions by shutting down their cell phones, radios, IEDs and very likely their new weapon of choice, drones.

Big Data management (Storage, Handling, Analysis, Transmission) is directly linked to its security. Big Data security involves, infrastructure security, data management, data privacy, and integrity & reactive security. The Government of India (GoI) has appreciated the all-pervasive nature of the cyber space domain and has therefore structured a holistic approach to the issues of Cyber Security and Big Data.

"This book addresses the issue of big data analytics from a practical angle for entrepreneurs with a pedagogical explanation of the operation of its main methods and concrete demonstrations of their use. It also builds a common set of concepts, terms, references, methods, applications and approaches in this area"--

Data transfer, storage management, sharing, curation and most notably data analysis of often geographically dispersed large quantities of data of experiments, observations, or computational simulations become ever more important for science, research, industry and governments. Scientists and engineers that analyse these massive datasets require therefore reliable infrastructures as well as scalable tools in order to perform 'scientific big data analytics (SBDA)'. This keynote will take stock of selected scientific and engineering use cases that take advantage of parallel machine learning algorithms (e.g. classification, clustering, regression) in combination with established statistical data mining methods in the light of new challenges faced with 'big data'. It will critically review practice and experience of selected community approaches and thus address several important questions: Is big data always better data for analytics? Are big data analytics frameworks really providing the functionality they promise or scientists require? How can the scientific big data analytics process be properly structured? What is the role of the Research Data Alliance and Open Grid Forum in this context? Do we need a peer-review process for steering the scientific big data analytics applications and evolution when using valuable storage and compute resources?

This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership - while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.). The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively.

In the past few years, several demographers have pointed out the need to consider big data in population studies. Some are in favour of data-driven approaches, as statistical algorithms could discover novel patterns in the data. This paper examines some of the methods, both old and new, that have been developed for detecting patterns and associations in the data. It concludes with a discussion on how big data and big data analytics can contribute to improving the explanatory power of models in the social sciences and in demography in particular.

Big Data Analytics in the Insurance Market is an industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. This book will be a 'must' for people seeking to broaden their knowledge of big data concepts and their real-world applications, particularly in the field of insurance. The insurance industry is largely dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential. Yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. This book an invaluable resource for any insurance professional from practitioners and policymakers working at insurance companies, to undergraduate and graduate students of economics management, and finance. Providing high quality academic research, ESFIRM provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale.

Storing, managing, sharing, curating and especially analysing huge amounts of data face an immense visibility and importance in industry and economy as well as in science and research. Industry and economy exploit "Big Data" for predictive analysis, to increase the efficiency of infrastructures, customer segmentation, and tailored services. In science, Big Data allows for addressing problems with complexities that were impossible to deal with so far. The amounts of data are growing exponentially in many areas and are becoming a drastical challenge for infrastructures, software systems, analysis methods, and support structures, as well as for funding agencies and legislation.In this contribution, we argue that the Helmholtz Association, with its objective to build and operate large-scale experiments, facilities, and research infrastructures, has a key role in tackling the pressing Scientific Big Data Analytics challenge. DataLabs and SimLabs, sustained on a long-term basis in Helmholtz, can bring research groups together on a synergistic level and can transcend the boundaries between different communities. This allows to translate methods and tools between different domains as well as from fundamental research to applications and industry. We present an SBDA framework concept touching its infrastructure building blocks, the targeted user groups and expected benefits, also concerning industry aspects. Finally, we give a preliminary account on the call for "Expressions of Interest" by the John von Neumann-Institute for Computing concerning Scientific Big Data Analytics by HPC.

Purpose
The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data.


Design/methodology/approach
The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards.


Findings
The authors' model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework.


Practical implications
The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model.


Social implications
Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework.


Originality/value
The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.