Dr. Neeraj Dahiya et al. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 8, Issue 1 March 2021, pp. 4-13 © 2021 IJRAA All Rights Reserved page - 4- Artificial Intelligence of Things: Purpose, Techniques and Practical Implications Dr. Neeraj Dahiya 1 , Dr. Uma Rani 2 1 Department of CSE, SRM University, Delhi-NCR, Sonipat, Haryana. 2 Department of CSE, DPG Institute of Technology & Management, Haryana. Abstract: The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. AI can be used to transform IoT data into useful information for improved decision making processes, thus creating a foundation for newer technology such as IoT Data as a Service (IoTDaaS). AIoT is transformational and mutually beneficial for both types of technology as AI adds value to IoT through machine learning capabilities and IoT adds value to AI through connectivity, signaling and data exchange. As IoT networks spread throughout major industries, there will be an increasingly large amount of human-oriented and machine-generated unstructured data. AIoT can provide support for data analytics solutions that can create value out of this IoT-generated data. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.. Keywords: Data Mining, Big Data, Distributed computing, Hadoop I. INTRODUCTION Mining of Data includes successful information assortment and warehousing just as PC handling. Information digging is utilized for analyzing crude information, including deals numbers, costs, and clients, to grow better showcasing techniques, improve the presentation or decline the expenses of maintaining the business. Additionally, Data mining serves to find new examples of conduct among purchasers [1]. Fig. 1 Data mining techniques Comprehensively, there are seven primary Data Mining methods. 1. Statistics: It is a part of science which identifies with the assortment and portrayal of information. A measurable strategy isn't considered as a Data Mining procedure by numerous investigators. Notwithstanding, it assists with finding the examples and construct prescient models [2]. 2. Clustering: Grouping is probably the most seasoned strategy utilized in Data Mining. It is the way toward recognizing comparable information that are like one another. 3. Visualization: Perception is utilized toward the start of the Data Mining measure [3]. 4. Decision Tree: This method can be utilized for investigation examination, information pre- preparing and expectation work [4]. 5. Association Rules: Affiliation Rules help to discover the relationship between at least two things. It assists with knowing the relations between the various factors in information bases. Affiliation rules find the shrouded designs in the informational collections [5]. 6. Neural Networks: Neural Network is another significant procedure utilized by individuals nowadays. This method is frequently utilized in the beginning phases of the Data Mining innovation. Neural systems are anything but difficult to use as they are robotized to a specific degree and in light of this the client isn't relied upon to have a lot of information about the work or information base [6]. 7. Classification: This strategy helps in inferring