One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. #Option deltagraph software#Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. These implementations allow us to carry out a comprehensive study of the feasibility and usability (through business analyses), the efficiency (saving up to 99% query execution times comparing to classic approaches) and the scalability of our solution. Based on the translation rules, we implement several temporal graphs according to benchmark and real-world datasets in the Neo4j data store. We define a set of translation rules to convert our conceptual model into the logical property graph. It has the advantage of being generic as it captures the different kinds of changes that may occur in interconnected data. To do so, we propose a new conceptual model of temporal graphs. The objective of this paper is to propose a complete solution to manage temporal interconnected data. For decision makers, these data changes provide additional insights to explain the underlying behaviour of a business domain. However, most of the existing work on the topic does not take into account the temporal dimension of such data, even though they may change over time: new interconnections, new internal characteristics of data (etc.). Graph data management systems are designed for managing highly interconnected data. This helps us attain better control for the size of our representation and reap further memory savings. Finally, our framework is the first effort we are aware of, that considers actual time instead of time steps. Moreover, our memory-efficient representation yields more than 70% faster graph compression and orders of magnitude quicker retrieval of graphs' elements, especially when it comes to large-scale networks. Our experimental evaluation demonstrates that our approach for compressing temporal graphs readily outperforms competing techniques, attaining compression ratios that are on average around 60% of the space required by state-of-the-art techniques. We empirically establish properties exhibited by community-networks regarding their time aspect(s) and harness these features in our proposed representation. In this paper we propose a framework for compressing emerging temporal graphs based on a dual-representation which articulates both network structure and corresponding temporal information. In reality however, networks change over time and, in many instances, we are interested in capturing and studying this evolution. Despite their success, such methods mostly focus on static graphs, and predominantly offer access to either a snapshot or an aggregated view of a network. Respective approaches offer succinct mappings for social, biological, and information networks while allowing for the efficient access of sought graph elements. Graph compression techniques have managed to reduce memory requirements and allow for representing such networks using a few bits-per-edge. This hinders the timely analysis of the formed networks due to existing physical memory limitations and significant I/O overheads. During the last decade, the volume growth of such systems has been unprecedented. Contemporary data-systems empowering the daily human activity are routinely represented with graphs.
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