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

Supporting sense-making and decision-making through time evolution analysis of open sources

Modern societies produce a huge amount of open source information that is often published on the Web in a natural language form. The impossibility of reading all these documents is paving the way to semantic-based technologies that are able to extract from unstructured documents relevant information for analysts. Most solutions extract uncorrelated pieces of information from individual documents; few of them create links among related documents and, to the best of our knowledge, no technology focuses on the time evolution of relations among entities. We propose a novel approach for managing, querying and visualizing temporal knowledge extracted from unstructured documents that can open the way to novel forms of sense-making and decision-making processes. We leverage state-of-the-art natural language processing engines for the semantic analysis of textual data sources to build a temporal graph database that highlights relationships among entities belonging to different documents and time frames. Moreover, we introduce the concept of temporal graph query that analysts can use to identify all the relationships of an entity and to visualize their evolution over time. This process enables the application of statistical algorithms that can be oriented to the automatic analysis of anomalies, state change detection, forecasting. Preliminary results demonstrate that the representation of the evolution of entities and relationships allows an analyst to highlight relevant events among the large amount of open source documents