NOSQL systems are suitable solutions for big data projects and offer a high level of flexibility in design. A good and efficient schema design for NOSQL wide column stores is not only based on the application’s conceptual data model but also on the queries defined in an application’s workload. In these databases, a manual schema design relies on rules of thumb to choose a good schema. Utility these rules without practices is a big challenge in this area. Because these rules are vague and generic, and must be adapted to each application. One of the ways that researchers use to overcome this challenge is presenting automated schema design. The main contribution of this paper is automated schema design for NOSQL wide column stores. This research proposes a workload-driven approach for the mapping from the application conceptual data model to a database schema with a goal of optimizing query performance. This approach uses workload information to achieve a good workload performance, which result in an optimized schema by minimizing the number of requests to the wide column stores. The experimental results show that automated schema generated by the proposed approach leads to a good workload performance.

Today, due to massive amount of information, NOSQL databases are used to compute complex data. These databases are used to store semi-structured and unstructured data for big data management. In this paper, fuzzy queries are executed by users on the information stored in the Neo4j graph database, which show results in defuzzy manner. To evaluate the efficiency of the proposed method, we considered a database of manufacturing company. Price and quality fuzzy variables were considered and used to define and execute the fuzzy queries. The results can verify the performance of the proposed method. Additionally, a counselor (who has information about the data) defines fuzzy terms, membership functions, and fuzzy rules table. The most important process in this method is to find the center of gravity in order to defuzzify the final result. So, an algorithm has been implemented for this purpose by C # programming language. The greatest query time is due to find center of gravity. The evaluation results show that the increase in query time by using the proposed approach in comparison to query time by using Cypher language is acceptable due to the complexity of fuzzy concepts. Therefore, the proposed solution will be suitable for using ambiguous and fuzzy queries in large databases.

Database management systems are the main part of information system that the size and complexity of these systems significantly have been increased in recent years. With the growing and being more complicated database management systems, database Administrators face more problems and challenges, so management of these systems is Time-consuming and costly. More over the main part of the total cost of ownership includes the cost of expert database administrator (DBA) who can manage these large and complicated systems. Autonomic databases by providing self-management functionality are caused to reduce the total cost of ownership for a database system. The self-management decisions as the automated schema database tuning depend on the database workload. One of the important issues in realizing the database automated tuning is workload monitoring and analysis for changes detection and schema re-tuning with this changes. In this paper is presented the feedback control loop for continuous monitoring and light-weight analysis of workload in NOSQL column-oriented database. This loop describes a design pattern for self-tuning feature and uses for workload change detection which require automated schema database re-tuning. The experimental results exhibit the effectiveness of the proposed solution for workload change detection.

The use of NOSQL data and its storage in the Cloud is growing rapidly. Due to the accumulation of data in the Cloud, data security against untrusted service providers as well as external attackers becomes a more serious problem. Over the past few years, there are some efforts to secure the outsourcing of NOSQL data, especially column-based and document-based models. However, practical solutions for secure outsourcing of key-value databases have not been identified. This paper attempts to introduce SecureKV as a secure method for outsourcing key-value databases. This method employs a multi-Cloud storage scenario to preserve outsourced data confidentiality. Besides security issues, the proposed method supports executing major key-value queries directly on outsourced data. A prototype of the Redis database management system has been implemented to show the efficiency and effectiveness of the proposed method. The results imply that, besides security issues, it is efficient and scalable enough in executing key-value-specific queries.