Perceptive type components had the greater relative weight to determine the level of patient satisfaction in this survey.

Abstract:The Internet of things (IoT) is potentially interconnecting unprecedented amounts of raw data, opening countless possibilities by two main logical layers: become data into information, then turn information into knowledge. The former is about filtering the significance in the appropriate format, while the latter provides emerging categories of the whole domain. This path of the data is a bottom-up flow. In the other hand, the path of the process is a top-down flow, starting at the strategic level of business and scientific institutions. Today, the path of the process treasures a sizeable amount of well-known methods, architectures and technologies: the so called Big Data. On the top, Big Data analytics aims variable association (e-commerce), data mining (predictive behaviour) or clustering (marketing segmentation). Digging the Big Data architecture there are a myriad of enabling technologies for data taking, storage and management. However the strategic aim is to enhance knowledge with the appropriate information, which does need of data, but not vice versa. In the way, the magnitude of upcoming data from the IoT will disrupt the data centres. To cope with the extreme scale is a matter of moving the computing services towards the data sources. This paper explores the possibilities of providing many of the IoT services which are currently hosted in monolithic cloud centres, moving these computing services into nano data centres (NaDa). Particularly, datainformation processes, which usually are performing at sub-problem domains. NaDa distributes computing power over the already present machines of the IP provides, like gateways or wireless routers to overcome latency, storage cost and alleviate transmissions. Large scale questionnaires have been taken for 300 IT professionals to validate the points of view for IoT adoption. Considering IoT is by definition connected to the Internet, NaDa may be used to implement the logical low layer architecture of the services. Obviously, such distributed NaDa send results on a logical high layer in charge of the information-knowledge turn. This layer requires the whole picture of the domain to enable those processes of Big Data analytics on the top.

Today, Big Data, IoT and Analytics are driving and making the differences in key performing top organizations. The interplay of these three areas can be instrumental for the future development of research, complex systems and enterprises. IoT will be estimated to rise to billions of devices connected by 2020 [1]. This has huge implications for research, businesses and future activities for mankind. The development of this vision is pivotal to support and foster IoTBDS (iotbds.org). Sensors are used to extract an unprecedented amount of data, which can be filtered and processed in the IoT by machine networks, automated analysis, tools and systems. This can become a Big Data layer abstraction from the captured data if the process is well-organized. Hereby, complex information systems can obtain the benefits of collecting, processing and analyzing highly valuable data. This is one of the most important scopes of COM-PLEXIS (complexis.org). Therefore, starting from the pillars of IoT, Analytics and Big Data, we can build complex information architectures by using tools like social media, predictive modeling, insight analysis and sentiment analysis. Eventually, we can build three additional layers: complex data, information and knowledge, and offer service related to these three valued-added layers. The application fields are widespread in fields like social networking, financial services, or biological research.This special issue (SI) call is focused on contributions that explore and demonstrate related areas, approaches and recommendations for both IoTBDS and COMPLEXIS. In particular, research work and projects that offer solutions to the underlying prob-B Victor Chang

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