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With the development of blockchain, blockchain has a broad prospect as a new type of data management system. However, limited to the data modeling method of blockchain, the usability of blockchain is restricted; In addition, every blockchain system has its own native but naive interfaces, when developing based on the different blockchain systems, which will leads to low development efficiency and high development costs. In this study, we construct a SQL-Middleware for blockchain system to solve these problems. The SQL-Middleware first performs relational modeling of blockchain data, mapping the blockchain data into a relational table; On the basis of modeling the blockchain data, SQL-Middleware encapsulates a set of SQL interfaces for blockchain system, thus realizing the unification of interface access methods of different blockchain systems. At last, we implement the SQL-Middleware based on the open source blockchain system CITA. Demonstration shows that the SQL-Middleware greatly improves the data management capabilities of blockchain and simplifies the blockchain access steps.

The traditional data of satisfaction evaluation comes from questionnaire survey, however, the accuracy and objectivity need to be strengthened, the practical application value is limited. The output of the research is usually the result of satisfaction evaluation, but the deep reasons of low customer satisfaction are not deeply explored. The purpose of this study is to propose a new customer satisfaction evaluation method system based on big data. According to different data collection methods, three evaluation methods are proposed, namely business big data evaluation, crawler big data evaluation, hardware big data evaluation and so on, to test and evaluate the real customer satisfaction level. These methods are helpful for enterprises to strengthen innovation, so as to improve customer satisfaction and loyalty.

Service quality is increasingly recognized as being of key strategic value by organizations in both the wearable devices manufacturing and service sector. With the development of wearable devices technology, how to improve service quality becomes hot topic for wearable devices providers. This paper studies on the service quality of wearable devices based on the SERVQUAL model and establishes the assessment system to explore the factors influencing wearable devices service quality.

In this demo paper, we present the Product Knowledge Retrieve System (PKRS), which can retrieve the large-scale product knowledge efficiently. The PKRS has three features. Firstly, PKRS can retrieve not only the objective knowledge (e.g. categories) but also the subjective knowledge (e.g. users’ opinion). Secondly, a learned mapping dictionary (LMD) is devised to accelerate the query parsing. Thirdly, PKRS adopts optimized join strategy to improve the retrieval effectiveness. For demonstration, we compare the performance of our PKRS with a state-of-the-art knowledge management system. The experimental results show that the PKRS can process the queries on product knowledge more effectively.