The Cognitive Internet of Things and Big Data

In this section, we solve open research issues that are cited in third section and we compare our architecture with others. Accordingly, In addition, our architecture is suitable for all projects that deal with the large quantity of data such as the architecture of Reality mining in sensor-based mobile-driven environments. In addition, users can configure the tool without any technical assistance. Hence, all users are able to configure dynamic intelligent environment without technical assistance like in Security and privacy in the Internet of Things: Current status and open issues. The tool has to contain friendly interfaces and straightforward services to make all the user feeling comfortable. In addition, we propose automated functions such as sensors to locate and track the user movements. Moreover, we propose simple methods such as disabling the unneeded functions of the service in order to improve energy efficiency. Our mechanism is independent. Thus, Its components do not depend on each other. For example, if the user does not ingest the file into the platform, the system will show empty results. If an error occurs while the system is working, it will ask to reload the last process and not the whole data flow process. Furthermore, the tool can collect all type of data and adds values and knowledge to raw of collected sensor data. Our architecture solves the real-time consideration by sending the collected data to be ingested in the platform. Hence, the user can get the data required by sending requests for data feeds. Likewise in Fog computing: A platform for internet of things and analytics, we solve the speed of data flow in the architecture by choosing some existing technologies such as ELT to ingest, extract, load, and transform data in high speed. In addition, we use a programmable interface to ease seamless resource. In addition, it can support a huge number of simultaneous and paralleled events such as generating data from location sensor while generating data from a record. It is capable of collecting all the varieties of data from different sources such as Fog computing: A platform for internet of things and analytics and Four-Layer Architecture for Product Traceability in Logistic Applications Big Data and the Internet of Things: Enterprise Information Architecture for a New. In addition, we tried to find another solution without moving the data ingestion tool as illustrated in A survey on internet of things architecture, protocols, possible applications, security, privacy, realworld implementation, and future trends. Thus, we combine both of DL and DWH to improve the complex data transformations and standardized analysis capabilities. In DL platforms such as Kylo and Zaloni that are built on Apache Hadoop and Spark, the stored data sych as in Comma-Separated Values (CSV) file loaded into a Hive table which makes data processing on Hadoop easier by providing a database query interface to Hadoop. Furthermove, we use the CIoT and big data architecture as a high level solution for computer-aided software engineering tool in order to deal several constraints such as configuration, dynamicity, context-aware computing, type of data, object capabilities, real-time consideration, fault tolerance, energy management, self-system, and business models interaction.