A Survey on Graph Database Management Techniques for Huge Unstructured Data

Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management

SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks

Automatic network management driven by Artificial Intelligent technologies has been heatedly discussed over decades. However, current reports mainly focus on theoretic proposals and architecture designs, works on practical implementations on real-life networks are yet to appear. This paper proposes our effort toward the implementation of knowledge graph driven approach for autonomic network management in software defined networks (SDNs), termed as SeaNet. Driven by the ToCo ontology, SeaNet is reprogrammed based on Mininet (a SDN emulator). It consists three core components, a knowledge graph generator, a SPARQL engine, and a network management API. The knowledge graph generator represents the knowledge in the telecommunication network management tasks into formally represented ontology driven model. Expert experience and network management rules can be formalized into knowledge graph and by automatically inferenced by SPARQL engine, Network management API is able to packet technology-specific details and expose technology-independent interfaces to users. The Experiments are carried out to evaluate proposed work by comparing with a commercial SDN controller Ryu implemented by the same language Python. The evaluation results show that SeaNet is considerably faster in most circumstances than Ryu and the SeaNet code is significantly more compact. Benefit from RDF reasoning, SeaNet is able to achieve O(1) time complexity on different scales of the knowledge graph while the traditional database can achieve O(nlogn) at its best. With the developed network management API, SeaNet enables researchers to develop semantic-intelligent applications on their own SDNs

Diseño e implementación de una herramienta de visualización para análisis en tiempo real de redes SDN/OpenFlow

Las Redes Definidas por Software (Software Defined Networking) permiten la monitorización y el control centralizado de la red, de forma que los administradores pueden tener una visión real y completa de la misma. El análisis y visualización de los diferentes parámetros obtenidos representan la forma más viable y práctica de programar la red en función de las necesidades del usuario. Por este motivo, en este proyecto se desarrolla una arquitectura modular cuyo objetivo es presentar en tiempo real la información que se monitoriza en una red SDN. En primera instancia, las diferentes métricas monitorizadas (error, retardo y tasa de datos) son almacenadas en una base de datos, para que en una etapa posterior se realice el análisis de dichas métricas. Finalmente, los resultados obtenidos, tanto de métricas en tiempo real como de los datos estadísticos, son presentados en una aplicación web. La información es obtenida a través de la interfaz REST que expone el controlador Floodlight y para el análisis de la información se plantea una comparación entre los valores medios y máximos del conjunto de datos. Los resultados obtenidos muestran gráficamente de forma clara y precisa las diferentes métricas de monitorización. Además, debido al carácter modular de la arquitectura, se ofrece un valor añadido a los sistemas actuales de monitorización SDN