E-health systems have witnessed widespread usage in the last few years, mainly due to advancements in health monitoring hardware and the availability of remote diagnostic services. Given the numerous benefits, there are two main challenges: management of the e-health ecosystem and security and privacy of sensitive data. The former results from nonunified, redundant, and often replicated information across numerous independent e-health service providers. While the later stems from sensitive information stored in centralized systems that can be compromised. Blockchain has emerged as a promising technology, which can be used to secure access and privacy of data and provide an umbrella management solution to large-scale distributed and decentralized enterprise systems. In this article, we present a unified system for migrating independent conventional e-health systems to a single blockchain-based ecosystem. More specifically, we address the issues of difference in data structures for conventional relational databases and blockchain file databases. The solution describes the conversion process and synchronization of information in a unified system for large-scale e-health data. The implementation and analysis show that significant improvements in data storage, access control, and seamless migration can be achieved.

Interoperability and Synchronization Management of Blockchain-Based Decentralized e-Health Systems

A Schema-less NoSQL system refers to solutions where users do not declare a database schema and, in fact, its management is moved to the application code. This paper presents a study that allows us to evaluate, to some extent, the data structuring impact. The decision of how to structure data in semi-structured databases has an enormous impact on data size, query performance and readability of the code, which influences software debugging and maintainability. This paper presents an experiment performed using MongoDB along with several alternatives of data structuring and a set of queries having increasing complexity. This paper introduces an analysis regarding the findings of such an experiment.

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Data schema does matter, even in NoSQL systems!

With the data growing dramatically and the structure of data becoming increasingly flexible, MongoDB has replaced the relational database in many applications. However, not much work has been done to effectively transform the schema and migrate the data from relational database to MongoDB. In this paper, we propose an approach of model transformation and data migration from relational database to MongoDB. Our work i) considers the query characteristics and data characteristics of the relational database, ii) designs a model transformation algorithm based on description tags and action tags, iii) automatically migrates the data into MongoDB based on the result of model transformation, and iv) develops an useful tool. We have designed experiments to prove that using our approach can achieve a better read performance.

Model Transformation and Data Migration from Relational Database to MongoDB

Content management systems (CMSs) are able to let people, who have no technical skill to manage websites, rapidly create, edit, organize and publish online contents. For example, CMSs are often used to establish e-commerce online shops, community portals, personal blogs, or organization websites. Thus, CMSs play the role of collecting data and then can be easily extended to become intelligent Internet systems. Most popular CMSs, e.g., Word Press and Joomla, rely on relational databases or SQL databases, e.g., MySQL, PostgreSQL, etc. However, due to the explosive growth of huge data, the well-structured characteristic of SQL databases may limit the scalability to handle horizontal scaling. Therefore, regarding the flexibility and the feasibility of parallel processing, cloud computing takes Not Only SQL (NoSQL) databases into consideration. How to migrate the original CMS software from SQL databases to NoSQL databases becomes one emerging and critical research issue. This paper is motivated to propose an autonomous SQL-to-NoSQL schema denormalization and migration. At the same time, this paper also evaluates the proposed mechanism on the realistic CMS software. Based on our experimental results, our mechanism not only helps the current CMS software migrate into the cloud platform without re-designing their database schemas, but also improves at least 45% access performance after schema migration.

SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems

In an era of super computing, data is increasing exponentially requiring more proficiency from the available technologies of data storage, data processing, and analysis. Such continuous massive growth of structured and unstructured data is referred to as a “Big data”. The processing and storage of big data through a conventional technique is not possible. Due to improved proficiency of Big Data solution in handling data, such as NoSQL caused the developers in the previous decade to start preferring big data databases, such as Apache Cassandra, Oracle, and NoSQL. NoSQL is a modern database technology that is designed to provide scalability to support voluminous data, leading to the rise of NoSQL as the most viable database solution. These modern databases aim to overcome the limitations of relational databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. These days, the larger enterprises need to shift NoSQL databases due to their more flexible models. It is a great challenge for business organizations and enterprises to transform their existing databases to NoSQL databases considering heterogeneity and complexity in relational data. In addition, with the emergence of big data, data cleansing has become a great challenge. In this paper, we proposed an approach that has two modules: data transformation and data cleansing module. The first phase is the transformation of a relational database to Oracle NoSQL database through model transformation. The second phase provides data cleansing ability to improve data quality and prepare it for big data analytics. The experiments show the proposed approach successfully transforms the relational database to a big data database and improve data quality.

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Intelligent Data Engineering for Migration to NoSQL Based Secure Environments

With the increasing maturity of NoSQL databases as well as the situation of reading more than writing on large volumes of data, many applications turn to NoSQL and pick it as data storage system. Migrating from SQL database to NoSQL and providing efficient query become growing imperative. However, join operation is not supported in NoSQL database and read separately for multiple times which brings poor performance is unavoidable. This paper proposes a schema conversion model for transforming SQL database to NoSQL which can provide high performance of join query with nesting relevant tables, and a graph transforming algorithm for containing all required content of join query in a table by offering correctly nested sequence. A rigorous proof about the transform algorithm has been conducted and the experiment has verified the correctness of the conversion model and the high performance of join operation.

Schema Conversion Model of SQL Database to NoSQL

With the development of web application, the demand of query performance and expansion becomes increasingly necessary. Relational database seems hardly handling with this, more and more applications choose migrating to NoSQL database such as HBase. The features of high reliability, high performance, column-oriented storage, high scalability, and real-time read and write, won favor from public. In this paper, we use two concepts Nested and Multiple Nested to describe join keys reference relationship between tables in relational database. Because HBase only support the column family nest the column qualifier, it is hard to deal with the situation of Multiple Nested after data migration from relational database meanwhile ensure high query performance in transformed HBase. We propose a novel HBase schema for migrating from relational database to HBase, which supports multiple nested. With this schema in HBase, query only once on one table for any query is needed to get the data, and query efficiency has been greatly improved, especially for join query. Additionally, index migration is available in this scheme, which supports faster query. Finally, this paper proposes a mapping algorithm to find column name corresponding to attribute in relational database.

Multiple Nested Schema of HBase for Migration from SQL

Game theory has been widely used in multiple fields such as economics, computer science, and political science to study the rational behavior of multiple decision makers in different scenarios. In recent years, with the rapid development of artificial intelligence, AI trained using game theory has shown outstanding performance in board games such as Go, chess and backgammon. This study explores the application of game theory optimization in the board game "Go-Moku" using a learning algorithm combining Monte Carlo tree search algorithm and reinforcement learning. The paper discusses recent developments in game theory in Go-Moku and explains the Monte Carlo tree search algorithm in detail. The performance of the algorithm is evaluated through experimental results of the application of Alpha Zero in the Go-Moku domain, demonstrating its effectiveness in improving the gaming capabilities of artificial intelligence. According to the analysis, after 1,100 training sessions, the algorithm combining reinforcement learning and MCTS had a 9:1 win rate compared to a pure MCTS approach with 2,000 self-simulations per step. And playing against the 800-times pure MCTS method, it reached a 10:0 win rate for the first time in the 300th game. In addition, the paper discusses potential applications of game-theoretic optimization in other dynamic games. Overall, these results shed light on further exploration of game theory in the area of complete information games and reinforcement learning.

Game Theory Optimization Based on Python: Evidence from Board Game “Go-Moku”

The invention discloses a database incremental data transformation and migration method and system The system comprises a first transformation and migration unit, an assembling and generating unit and a second transformation and migration unit The method comprises the following steps: carrying out incremental data transformation and migration treatment on isolated nodes and/or leaf node in an incremental graph; adding nodes of which the in-degree is not zero but the out-degree is zero in the incremental graph in a first node assemble; and carrying out incremental data transformation and migration treatment on the rest nodes in the incremental graph by using the first node assemble By the database incremental data transformation and migration method and system, data consistency between a relational database and a NoSQL database can be maintained, in an incremental data transformation and migration process, repeated migration of whole quantity data can be avoided, and therefore, processing efficiency is improved greatly The database incremental data transformation and migration method and system can be widely used in the technical field of database transformation and migration

Database incremental data transformation and migration method and system

The authors mainly discuss finite-time tracking consensus and fixed-time tracking consensus of multi-agent systems for second-order with inherent nonlinear function and disturbance under a directed interaction graph in this work. By sliding mode control theory, consensus tracking controllers are proposed to ensure that sliding mode can be reached and maintain on it in finite time and fixed time. By using some theoretical knowledge of control, such as graph theory and Lyapunov theory, we make the system to reach consensus in finite time and fixed time by choosing the right parameters. In addition, the calculation leads to the conclusion that settling time of finite-time consensus is associated to initial state, while settling time of fixed-time consensus is interrelated to parameters but not to the initial state. The feasibility of the theory is proved by simulation experiments.

Zijing Li

Papers

Schema Conversion Model of SQL Database to NoSQL

Multiple Nested Schema of HBase for Migration from SQL

Game Theory Optimization Based on Python: Evidence from Board Game “Go-Moku”

Database incremental data transformation and migration method and system

Fixed-time consensus of second-order multi-agent systems with nonlinear dynamics