Big Data Transforms Discovery-Utilization Therapeutics Continuum.

Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical-, and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization

Big data, modeling, simulation, computational platform and holistic approaches for the fourth industrial revolution

Naturally, the mathematical process starts from proving the existence and uniqueness of the solution by the using the theorem, corollary, lemma, proposition, dealing with the simple and non-complex model. Proving the existence and uniqueness solution are guaranteed by governing the infinite amount of solutions and limited to the implementation of a small-scale simulation on a single desktop CPU. Accuracy, consistency and stability were easily controlled by a small data scale. However, the fourth industrial can be described the mathematical process as the advent of cyber-physical systems involving entirely new capabilities for researcher and machines (Xing, 2017). In numerical perspective, the fourth industrial revolution (4iR) required the transition from a uncomplex model and small scale simulation to complex model and big data for visualizing the real-world application in digital dialectical and exciting opportunity. Thus, a big data analytics and its classification are a problem solving for these limitations. Some applications of 4iR will highlight the extension version in terms of models, derivative and discretization, dimension of space and time, behavior of initial and boundary conditions, grid generation, data extraction, numerical method and image processing with high resolution feature in numerical perspective. In statistics, a big data depends on data growth however, from numerical perspective, a few classification strategies will be investigated deals with the specific classifier tool. This paper will investigate the conceptual framework for a big data classification, governing the mathematical modeling, selecting the superior numerical method, handling the large sparse simulation and investigating the parallel computing on high performance computing (HPC) platform. The conceptual framework will benefit to the big data provider, algorithm provider and system analyzer to classify and recommend the specific strategy for generating, handling and analyzing the big data. All the perspectives take a holistic view of technology. Current research, the particular conceptual framework will be described in holistic terms. 4iR has ability to take a holistic approach to explain an important of big data, complex modeling, large sparse simulation and high performance computing platform. Numerical analysis and parallel performance evaluation are the indicators for performance investigation of the classification strategy. This research will benefit to obtain an accurate decision, predictions and trending practice on how to obtain the approximation solution for science and engineering applications. As a conclusion, classification strategies for generating a fine granular mesh, identifying the root causes of failures and issues in real time solution. Furthermore, the big data-driven and data transfer evolution towards high speed of technology transfer to boost the economic and social development for the 4iR (Xing, 2017; Marwala et al., 2017)

CLOUD-BASED DATA ANALYTICS FRAMEWORK FOR MOBILE APP EVENT ANALYSIS

Mobile analytics studies the behavior of end users of mobile applications and the mobile application itself. These mobile applications, being an important part of the various businesses products, need to be monitored and the usage patterns are to be analyzed. The data collected from these apps can help to drive important business strategies by identifying the usage patterns. Enriching the data with information available from other sources, like sales/service information, provides holistic view about the solution. Thus, here we aim at exploring some set of tools that give capabilities as event trailing with higher extraction of its linguistics. If the application is used worldwide, the data generated out of it is Big Data, which traditional systems cannot handle. We therefore propose a special framework for efficient data collection, storage and processing at Big Data scale on cloud platform. Â

Blockchain: A Graph Primer

Bitcoin and its underlying technology Blockchain have become popular in recent years. Designed to facilitate a secure distributed platform without central authorities, Blockchain is heralded as a paradigm that will be as powerful as Big Data, Cloud Computing and Machine learning. Blockchain incorporates novel ideas from various fields such as public key encryption and distributed systems. As such, a reader often comes across resources that explain the Blockchain technology from a certain perspective only, leaving the reader with more questions than before. We will offer a holistic view on Blockchain. Starting with a brief history, we will give the building blocks of Blockchain, and explain their interactions. As graph mining has become a major part its analysis, we will elaborate on graph theoretical aspects of the Blockchain technology. We also devote a section to the future of Blockchain and explain how extensions like Smart Contracts and De-centralized Autonomous Organizations will function. Without assuming any reader expertise, our aim is to provide a concise but complete description of the Blockchain technology.Comment: 16 pages, 8 figure

Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

Big Data Integration (BDI) process integrates the big data arising from many diverse data sources, data formats presents a unified, valuable, customized, holistic view of data. BDI process is essential to build confidence, facilitate high-quality insights and trends for intelligent decision making in organizations. Integration of big data is a very complex process with many challenges. The data sources for BDI are traditional data warehouses, social networks, Internet of Things (IoT) and online transactions. BDI solutions are deployed on Master Data Management (MDM) systems to support collecting, aggregating and delivering reliable information across the organization. This chapter has conducted an exhaustive review of BDI literature and classified BDI applications based on their domain. The methods, applications, advantages and disadvantage of the research in each paper are tabulated. Taxonomy of concepts, table of acronyms and the organization of the chapter are presented. The number of papers reviewed industry-wise is depicted as a pie chart. A comparative analysis of curated survey papers with specific parameters to discover the research gaps were also tabulated. The research issues, implementation challenges and future trends are highlighted. A case study of BDI solutions implemented in various organizations was also discussed. This chapter concludes with a holistic view of BDI concepts and solutions implemented in organizations

Reassessing values for emerging big data technologies: integrating design-based and application-based approaches

Through the exponential growth in digital devices and computational capabilities, big data technologies are putting pressure upon the boundaries of what can or cannot be considered acceptable from an ethical perspective. Much of the literature on ethical issues related to big data and big data technologies focuses on separate values such as privacy, human dignity, justice or autonomy. More holistic approaches, allowing a more comprehensive view and better balancing of values, usually focus on either a design-based approach, in which it is tried to implement values into the design of new technologies, or an application-based approach, in which it is tried to address the ways in which new technologies are used. Some integrated approaches do exist, but typically are more general in nature. This offers a broad scope of application, but may not always be tailored to the specific nature of big data related ethical issues. In this paper we distil a comprehensive set of ethical values from existing design-based and application-based ethical approaches for new technologies and further focus these values to the context of emerging big data technologies. A total of four value lists (from techno-moral values, value-sensitive design, anticipatory emerging technology ethics and biomedical ethics) were selected for this. The integrated list consists of a total of ten values: human welfare, autonomy, non-maleficence, justice, accountability, trustworthiness, privacy, dignity, solidarity and environmental welfare. Together, this set of values provides a comprehensive and in-depth overview of the values that are to be taken into account for emerging big data technologies.Horizon 2020(H2020)No 731873 (e-SIDES)Article / Letter to editorInstituut voor Metajuridic

Reassessing values for emerging big data technologies: integrating design-based and application-based approaches

Through the exponential growth in digital devices and computational capabilities, big data technologies are putting pressure upon the boundaries of what can or cannot be considered acceptable from an ethical perspective. Much of the literature on ethical issues related to big data and big data technologies focuses on separate values such as privacy, human dignity, justice or autonomy. More holistic approaches, allowing a more comprehensive view and better balancing of values, usually focus on either a design-based approach, in which it is tried to implement values into the design of new technologies, or an application-based approach, in which it is tried to address the ways in which new technologies are used. Some integrated approaches do exist, but typically are more general in nature. This offers a broad scope of application, but may not always be tailored to the specific nature of big data related ethical issues. In this paper we distil a comprehensive set of ethical values from existing design-based and application-based ethical approaches for new technologies and further focus these values to the context of emerging big data technologies. A total of four value lists (from techno-moral values, value-sensitive design, anticipatory emerging technology ethics and biomedical ethics) were selected for this. The integrated list consists of a total of ten values: human welfare, autonomy, non-maleficence, justice, accountability, trustworthiness, privacy, dignity, solidarity and environmental welfare. Together, this set of values provides a comprehensive and in-depth overview of the values that are to be taken into account for emerging big data technologies.Horizon 2020(H2020)No 731873 (e-SIDES)Article / Letter to editorInstituut voor Metajuridic

Citizen-Centric Data Services for Smarter Cities

Smart Cities use Information and Communication Technologies (ICT) to manage more efficiently the resources and services offered by a city and to make them more approachable to all its stakeholders (citizens, companies and public administration). In contrast to the view of big corporations promoting holistic “smart city in a box” solutions, this work proposes that smarter cities can be achieved by combining already available infrastructure, i.e., Open Government Data and sensor networks deployed in cities, with the citizens’ active contributions towards city knowledge by means of their smartphones and the apps executed in them. In addition, this work introduces the main characteristics of the IES Cities platform, whose goal is to ease the generation of citizen-centric apps that exploit urban data in different domains. The proposed vision is achieved by providing a common access mechanism to the heterogeneous data sources offered by the city, which reduces the complexity of accessing the city’s data whilst bringing citizens closely to a prosumer (double consumer and producer) role and allowing to integrate legacy data into the cities’ data ecosystem.The European Union’s Competitiveness and Innovation Framework Programme has supported this work under grant agreement No. 325097