![]() ![]() However, these graphs are extremely sparse throughout their lifetimes. Due to their popularity, they have become increasingly massive in terms of their number of nodes, arcs, and lifetimes. Time-evolving web and social network graphs are modeled as a set of pages/individuals (nodes) and their arcs (links/relationships) that change over 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. Our experimental results demonstrate that the δ-Copy+Log presents an overall better performance as compared to traditional storage methods in terms of space usage and query evaluation time. This technique targets the mitigation of the apparent trade-off between the conflicting goals of the reduction of space usage and acceleration of query execution time. It differentiates from existing temporal graph management systems by adopting the δ-Copy+Log technique. Clock-G is designed by the developers of the Thing'in platform and is currently being deployed into production. In this paper, we discuss the design of a temporal graph management system Clock-G and introduce a new space-efficient storage technique δ-Copy+Log. However, existing commercial graph databases are not designed with native temporal support which limits their usability in such use cases. Analyzing the history of these connections paves the way to new promising applications such as object tracking, anomaly detection, and forecasting the future behavior of devices. ![]() The graph of Thing'in is dynamic because IoT devices create temporary connections between each other. The platform manages a graph of millions of connected and non-connected objects using a commercial graph database. Thing'in 1 is a platform, initiated by Orange 2. IoT applications can be naturally modeled as a graph where the edges represent the interactions between devices, sensors, and their environment. We present a comprehensive experimentalĮvaluation that illustrates the effectiveness of our proposed techniques at Secondly, we present an in-memory graph data structureĬalled GraphPool that can maintain hundreds of historical graph instances in Portions of the historical graph state in memory to further speed up the In addition, we present strategies for materializing We develop analytical models for both the storage space neededĪnd the snapshot retrieval times to aid in choosing the right parameters for a Structure to efficiently execute queries like subgraph pattern matching over Along with the original graph data, DeltaGraph can also maintainĪnd index auxiliary information this functionality can be used to extend the That enables compactly recording the historical information, and that supportsĮfficient retrieval of historical graph snapshots for single-site or parallel Novel, extensible, highly tunable, and distributed hierarchical index structure Our system exposes a general programmaticĪPI to process and analyze the retrieved snapshots. Graphs from arbitrary time points in the past, in addition to maintaining theĬurrent state for ongoing updates. History of a network and provides support for efficient retrieval of multiple We present the designĪnd architecture of a distributed graph database system that stores the entire To enable temporal and evolutionary queries and analysis. Information networks like social networks or citation networks, with the goal We address the problem of managing historical data for large evolving ![]()
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