(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 2, 2020 430 | Page www.ijacsa.thesai.org Semantic Architecture for Modelling and Reasoning IoT Data Resources based on SPARK Ahmed Salama 1 , Masoud E. Shaheen 2 , Haytham Al-Feel 3 Information Systems Department Faculty of Computers and Information Fayoum University, Fayoum, Egypt Abstract—Electronic Internet-of-Things is one of the foremost valuable techniques today. Through it, everything everywhere the globe became connected and intelligent, eliminating the wants to human-to-human interaction to perform tasks. This by changing all of those objects like humans, machines, devices and something around to be simply an internet Protocol (IP) to be expressed within the network environment through completely different sensors and actuators devices which might facilitate the interaction between all of them. These different types of sensors generate a large volume of various information and data. This type of sensor information created it generally useless because of the heterogeneity and lack of interoperability of it that represents it in unstructured form. So, investing from semantic internet techniques might handle these main challenges that face the IoT applications. Hence, the main contribution behind this research aims to boost the performance and quality of sensors information retrieved from IoT resources and applications by using semantic web technologies to resolve the matter of heterogeneity and interoperability and then convert the unstructured sensor data to structured form to realize the next level of investing of sensors employed in IoT applications. Also, the aim through this research to improve the performance of the tremendous amount of information that represents the demonstrated IoT information utilizing Big Data techniques such as Spark and its query language that's named SPARK-SQL as a streaming inquiry language for a colossal amount of information. The proposed architecture demonstrated that utilizing the semantic techniques to model the streaming sensors data improve the value of information and permit us to gather unused information. Moreover, the improvement by using SPARK leads to extend the performance of utilizing this sensor information in terms of the time retrieval of running queries, particularly when running the same queries utilizing the conventional SPARQL inquiry language. Keywords—Big Data; Internet of Things; Semantic Modelling; Semantic_Reasonin; Semantic_Rules; Sensors; Apache SPARK; SPARK_SQL I. INTRODUCTION Internet-of-Things is considered one of the hottest trends that formulate the progress of information technology development sector. Connecting every object via Internet Protocol (IP) facilitates the intercommunication between human users and machines in different aspects. In this context, there were various researches that focused on the physical side of the IoT applications without representing the importance of the information that is gathered from the resources of Internet of Things devices On this context, there have been various researches that centered at the physical aspect of the IoT packages without representing the importance of the information which might be collected from the sources of internet of things devices. IoT is divided into four architectural layers which started with the specified networked things, consisting of wireless sensors and actuators as layer 1, and layer 2 represents each structure of sensors data aggregation and virtual data conversion. Additionally, layer three overviews the role of IT structures in appearing preprocessing of information earlier than it saved into the storage repository. Finally, the extracted information is analyzed, controlled, and loaded directly to the conventional lower back-give up storage systems as shown in Fig. 1. Hence, the aim of this research is to:  Build a semantic modeled architecture. This proposed architecture could model the different information fetched from the IoT sensors and actuators such as humidity, temperature, and pressure. This could enrich the meaning of this data and solve the main issue of heterogeneity.  Build a reasoner tool based on the Description Logic (DL) as one of the Artificial Intelligence languages that depend on semantic web technologies to infer a set of new rules based on a set of existing concepts and individuals after modeling this fetched information.  Providing the proposed model with the SPARK ecosystem as a big data platform based on Hadoop. This enhancement will increase the performance of the queries performed semantically against the SPARQL query language. This enhancement will illustrate the strength factor that advantages the contribution to others. The rest of the research is organized as follows: Section 2 presents the literature review that relates to the proposed works. Also, the background technologies which are used through the work are explained in Section 3. In addition to that, the proposed architecture is discussed in Section 4. On the other hand, the implementation processes of the work is presented through Section 5. Also, the results and the comparative study are presented in Section 6. In addition, evaluating the proposed architecture is explained through Section 7. Eventually, Section 8 concludes the paper and discusses the possible directions for future work.