@article {846,
	title = {Can ChatGPT Predict Stock Market Price Movements?},
	journal = {Journal of Digital Information Management},
	volume = {22},
	year = {2024},
	month = {09/2024},
	pages = {91-98},
	type = {Research},
	chapter = {91},
	abstract = {<p><span style="color: rgb(17, 17, 17); font-family: Raleway, Arial, Tahoma; font-size: 15.2015px; text-align: justify;">Accelerated advances in network speed, re- liability, and security have increased the demand for soft- ware and services to move from being stored and pro- cessed locally on users{\textquoteright} devices to being managed by third parties that can be accessed through the network. This has led to the need to develop new software archi- tectures and software architectures that meet these new requirements. One example of software architecture is the recent emergence of a microservices architecture to meet online service providers{\textquoteright} maintenance and scalability requirements. Microservices is an architecture style that focuses on breaking down a system into small, lightweight services that are intentionally created to perform a highly cohesive business function, and it is an evolution of the traditional service-oriented architecture style. The practi- cal projection of microservices architecture in informa- tion engineering is several small applications that work together to provide an integrated service that appears to the external medium as a single application. Each of these applications has its own independent source code and deals with data sources that are different from the rest. As a result, it has an independent development team. Partial applications depend on each other to accomplish their functions, and therefore, any modification to one of the services will affect the work of the rest of the ser- vices; in large applications that require keeping the sys- tem always available, we must perform the process of publishing updates for parts of the system without stop- ping it completely, so we must monitor the impact of modi- fications and forecast system status before new updates are deployed. In achieving this prediction, we must know the dependencies between different services to arrive at a cognitive model representing the dependencies between services or applications.</span></p>
},
	doi = {https://10.0.23.137/jdim/2024/22/3/91-98},
	url = {http://www.dline.info/jdim/wp-content/uploads/2.pdf},
	author = {Ammar Esrawi and Bassel Alkhatib}
}
