Design Big Data Research Articles

AN EXPLORATION OF BIG DATA AS THE BASIS FOR DEVELOPING A FLUID AND ADAPTABLE ARCHITECTURE PROGRAMMING

Many discussions have been devoted to the use of big data in architecture, particularly as atool for data predictive analysis. Interpretation of big data rules, however, is rarely discussed. This paper explores the use of big data as a basis for programming architectures. It emphasizes the use of big data in design beyond predicting the future. Instead, it is a descriptive resource that transcends rigid data dependencies. This allows architectural program development to become more flexible and adaptable. This study was conducted using exploratory qualitative methodology. A variety of sources in Google were used to collect the data. By using the tracing method, a rule inquiry is conducted. To gain a better understanding of how big data can be applied in architectural programming, a rules-based design exploration was conducted. Based on the results of this study, the following methods may be used to determine the rules: (1) data collection for reading patterns, (2) trace lines as a basis for discovering rules, and (3) exploration of rules as a basis for programming design. The concept of architecture can be considered asa component that incorporates digital technology, which enhances efficiency and adaptability

Big Data in Designing Clinical Trials: Opportunities and Challenges.

Emergence of big data analytics resource systems (BDARSs) as a part of routine practice in Radiation Oncology is on the horizon. Gradually, individual researchers, vendors, and professional societies are leading initiatives to create and demonstrate use of automated systems. What are the implications for design of clinical trials, as these systems emerge? Gold standard, randomized controlled trials (RCTs) have high internal validity for the patients and settings fitting constraints of the trial, but also have limitations including: reproducibility, generalizability to routine practice, infrequent external validation, selection bias, characterization of confounding factors, ethics, and use for rare events. BDARS present opportunities to augment and extend RCTs. Preliminary modeling using single- and muti-institutional BDARS may lead to better design and less cost. Standardizations in data elements, clinical processes, and nomenclatures used to decrease variability and increase veracity needed for automation and multi-institutional data pooling in BDARS also support ability to add clinical validation phases to clinical trial design and increase participation. However, volume and variety in BDARS present other technical, policy, and conceptual challenges including applicable statistical concepts, cloud-based technologies. In this summary, we will examine both the opportunities and the challenges for use of big data in design of clinical trials.

Big Data in Design and Manufacturing Engineering

Big Data helps facilitate information visibility and process automation in design and manufacturing engineering. It also helps analyze trends through analytics and predict inventory, manufacturing output and equipment lifespan and cycles, etc. This paper introduces Big Data, its characteristics and a number of issues of Big Data in design and manufacturing engineering. These issues include design and manufacturing data, Big Data benefits and impacts and its applications and opportunities. Methods, technologies and some technology progress around Big Data are presented in this study. General challenges of Big Data and Big Data challenges in design and manufacturing engineering are also discussed.