## 1 Introduction
In terms of present web services that are particularly designed for applications and due to the increasing demand for communication between applications, the formulation of uniform standards and protocols for information communication has become more important. The current standards for information communication include HP's e-Speak, Microsoft's.Net strategy, IBM's WSTK (Web Service Toolkit) and WSDE (Web Service Development Environment), and Oracle's Dynamic Service. Sun Microsystems also announced its web services framework, which can be adopted for the J2 EE operating platform. The World Wide Web Consortium (W3C) even unified the various standards for web services, including HTTP (HyperText Transfer Protocol), XML (Extensible Makeup Language), SOAP (Simple Object Access Protocol), WSDL (Web Service Description Language), and UDDI (Universal Description, Discovery and Integration). For example, HTTP is a communication protocol widely used on the Internet; XML is a tag language used to describe the interaction between users and service providers; SOAP is a communication protocol based on XML written for the access to web services; WSDL uses XML to describe the details of web services so that applications can easily interact with each other; and UDDI is an XML-based distributed registry of web services of which the goal is to enable web services providers to quickly tell users their available web services. These standards allow web services to have a broader operating base and platform, and that is why this study utilizes web services technology as an important basis to solve the above problems. Big data means that the amount of data involved is too enormous to be easily retrieved, managed, processed, and organized as information that could be interpreted by humans in a reasonable time period. Hence, big data cannot be processed by most traditional database management systems; rather, it must be performed under software that can run simultaneously on tens, hundreds, or even thousands of servers. Currently, the unit size of a dataset that can be analyzed within a reasonable time is ExaByte (EB), in which 1 EB = 10\({}^{18}\) = 1000 PB (PetaBytes), where PB = 1000 TB (TeraBytes). [PERSON] [5] mentioned that the size of the dataset does not have to be very large, such as TB, PB, or EB, but it must be a \"complete\" and \"relative\" dataset. In other words, the dataset must be able to become a series of useful information after processing and analyzing, in order for an analyst to find out the relationship between information sectors. For this reason, this study adopted a time-series analysis [6] as the basis for retrieving relevant cases. How does one carry out unimaginable and incomprehensible big data analytics? The fundamental solution is to \"make it simple.\" People can summarize the results of big data analytics and take corresponding actions or make optimal decisions. The biggest challenge of this study is to explore a back-end system based on big data analytics. Some domestic and foreign studies in the references have used both case-based reasoning and learning to develop relevant information systems. For example, [PERSON] [7] used the case-based reasoning technique to explore implementation methods for an optimal milling machine system that meets the processing needs. [PERSON] [8] combined a case-based reasoning module with a practical database to find the related technologies of knowledge management and development framework for financing, credit, and default cases in the banking industry. [PERSON] [9] noted the key factors for sales personnel to sell sports shoes and established the weights of these factors according to case-based reasoning, so as to get the relevant technology for sales forecasts of new products before these products are launched. [PERSON] and [PERSON] [10] applied case-based reasoning to neural networks to estimate the unique product development costs of a manufacturer. [PERSON] et al.