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

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

SuMGra: Querying Multigraphs via Efficient Indexing

International audienceMany real world datasets can be represented by a network with a set of nodes interconnected with each other by multiple relations. Such a rich graph is called a multigraph. Unfortunately, all the existing algorithms for subgraph query matching are not able to adequately leverage multiple relationships that exist between the nodes. In this paper we propose an efficient indexing schema for querying single large multi-graphs, where the indexing schema aptly captures the neighbourhood structure in the data graph. Our proposal SuMGra couples this novel indexing schema with a subgraph search algorithm to quickly traverse though the solution space to enumerate all the matchings. Extensive experiments conducted on real benchmarks prove the time efficiency as well as the scalability of SuMGra

Predictive analysis of real-time strategy games using graph mining

Machine learning and computational intelligence have facilitated the development of recommendation systems for a broad range of domains. Such recommendations are based on contextual information that is explicitly provided or pervasively collected. Recommendation systems often improve decision-making or increase the efficacy of a task. Real-Time Strategy (RTS) video games are not only a popular entertainment medium, they also are an abstraction of many real-world applications where the aim is to increase your resources and decrease those of your opponent. Using predictive analytics, which examines past examples of success and failure, we can learn how to predict positive outcomes for such scenarios. To do this, one way to represent this type of data in order to model relationships between entities is by using graphs. The vast amount of data has resulting in complex and large graphs that are difficult to process. Hence, researchers frequently employ parallelized or distributed processing. But first, the graph data must be partitioned and assigned to multiple processors in such a way that the workload will be balanced, and inter-processor communication will be minimized. The latter problem may be complicated by the existence of edges between vertices in a graph that have been assigned to different processors. One objective of this research is to develop an accurate predictive recommendation system for multiplayer strategic games to determine recommendations for moves that a player should, and should not, make which can provide a competitive advantage. Another objective is to determine how to partition a single undirected graph in order to optimize multiprocessor load balancing and reduce the number of edges between split subgraphs --Abstract, page iv

Distributed Graph Storage And Querying System

Graph databases offer an efficient way to store and access inter-connected data. However, to query large graphs that no longer fit in memory, it becomes necessary to make multiple trips to the storage device to filter and gather data based on the query. But I/O accesses are expensive operations and immensely slow down query response time and prevent us from fully exploiting the graph specific benefits that graph databases offer. The storage models of most existing graph database systems view graphs as indivisible structures and hence do not allow a hierarchical layering of the graph. This adversely affects query performance for large graphs as there is no way to filter the graph on a higher level without actually accessing the entire information from the disk. Distributing the storage and processing is one way to extract better performance. But current distributed solutions to this problem are not entirely effective, again due to the indivisible representation of graphs adopted in the storage format. This causes unnecessary latency due to increased inter-processor communication. In this dissertation, we propose an optimized distributed graph storage system for scalable and faster querying of big graph data. We start with our unique physical storage model, in which the graph is decomposed into three different levels of abstraction, each with a different storage hierarchy. We use a hybrid storage model to store the most critical component and restrict the I/O trips to only when absolutely necessary. This lets us actively make use of multi-level filters while querying, without the need of comprehensive indexes. Our results show that our system outperforms established graph databases for several class of queries. We show that this separation also eases the difficulties in distributing graph data and go on propose a more efficient distributed model for querying general purpose graph data using the Spark framework