An Efficient Built-in Temporal Support in MVCC-based Graph Databases

Real-world graphs are often dynamic and evolve over time. To trace the evolving properties of graphs, it is necessary to maintain every change of both vertices and edges in graph databases with the support of temporal features. Existing works either maintain all changes in a single graph or periodically materialize snapshots to maintain the historical states of each vertex and edge and process queries over proper snapshots. The former approach presents poor query performance due to the ever-growing graph size as time goes by, while the latter one suffers from prohibitively high storage overheads due to large redundant copies of graph data across different snapshots. In this paper, we propose a hybrid data storage engine, which is based on the MVCC mechanism, to separately manage current and historical data, which keeps the current graph as small as possible. In our design, changes in each vertex or edge are stored once. To further reduce the storage overhead, we simply store the changes as opposed to storing the complete snapshot. To boost the query performance, we place a few anchors as snapshots to avoid deep historical version traversals. Based on the storage engine, a temporal query engine is proposed to reconstruct subgraphs as needed on the fly. Therefore, our alternative approach can provide fast querying capabilities over subgraphs at a past time point or range with small storage overheads. To provide native support of temporal features, we integrate our approach into Memgraph, and call the extended database system TGDB(Temporal Graph Database). Extensive experiments are conducted on four real and synthetic datasets. The results show TGDB performs better in terms of both storage and performance against state-of-the-art methods and has almost no performance overheads by introducing the temporal features

A data transformation model for relational and non-relational data

The information systems that support small, medium, and large organisations need data transformation solutions from multiple data sources to fulfill the requirements of new applications and decision-making to stay competitive. Relational data is the foundation for the majority of applications programme, whereas non-relational data is the foundation for the majority of newly produced applications. The relational model is the most elegant one; nonetheless, this kind of database has a drawback when it comes to managing very large volumes of data. Because they can handle massive volumes of data, non-relational databases have evolved into relational database substitutes. The key issue is that rules for data transformation processes across various data types are becoming less well-defined, leading to a steady decline in data quality. Therefore, to handle relational and non-relational data and satisfy the requirements for data quality, an empirical model in this domain knowledge is required. This study seeks to develop a data transformation model used for different data sources while satisfying data quality requirements, especially the transformation processes in relational and non-relational model, named Data Transformation with Two ETL Phases and Central-Library (DTTEPC). The different stages and methods in the developed model are used to transform the metadata information and stored data from relational to non-relational systems, and vice versa. The model is developed and validated through expert review, and the prototype based on the final version is employed in two case studies: education and healthcare. The results of the usability test demonstrate that the developed model is capable of transforming metadata data and stored data across systems. So enhancing the information systems in various organizations through data transformation solutions. The DTTEPC model improved the integrity and completeness of the data transformation processes. Moreover, supports decision-makers by utilizing information from various sources and systems in real-time demands

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio