Efficient Snapshot Retrieval over Historical Graph Data (1207.5777v1)

Abstract: We address the problem of managing historical data for large evolving information networks like social networks or citation networks, with the goal to enable temporal and evolutionary queries and analysis. We present the design and architecture of a distributed graph database system that stores the entire history of a network and provides support for efficient retrieval of multiple graphs from arbitrary time points in the past, in addition to maintaining the current state for ongoing updates. Our system exposes a general programmatic API to process and analyze the retrieved snapshots. We introduce DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compactly recording the historical information, and that supports efficient retrieval of historical graph snapshots for single-site or parallel processing. Along with the original graph data, DeltaGraph can also maintain and index auxiliary information; this functionality can be used to extend the structure to efficiently execute queries like subgraph pattern matching over historical data. We develop analytical models for both the storage space needed and the snapshot retrieval times to aid in choosing the right parameters for a specific scenario. In addition, we present strategies for materializing portions of the historical graph state in memory to further speed up the retrieval process. Secondly, we present an in-memory graph data structure called GraphPool that can maintain hundreds of historical graph instances in main memory in a non-redundant manner. We present a comprehensive experimental evaluation that illustrates the effectiveness of our proposed techniques at managing historical graph information.

• The paper introduces DeltaGraph, a distributed database system with a novel index structure for efficient snapshot retrieval of historical graph data.
• DeltaGraph uses a hierarchical structure with differential functions and column-oriented storage to efficiently store and retrieve historical graph snapshots.
• Experimental results show DeltaGraph significantly outperforms traditional methods, offering superior speed and scalability for large dynamic networks through parallel processing.

Efficient Snapshot Retrieval over Historical Graph Data

The paper addresses the management of historical data for dynamic information networks and introduces a system for efficient snapshot retrieval and temporal analysis. With the advent of large volumes of temporal data from networks such as social networks, citation databases, and other interconnected systems, there is a crucial need for a robust management solution that supports both the storage and retrieval of historical graph data. Traditional graph management systems struggle with temporal dimensions, thus necessitating the innovative approach presented in this work.

Overview of the Proposed System

The authors present a distributed graph database system incorporating a novel index structure named DeltaGraph. This structure is designed to store the entire history of a network, allowing for the efficient retrieval of graph snapshots from any time point. DeltaGraph supports single-site and parallel processing, leveraging a distributed architecture to partition and process large-scale data effectively.

The core functionality of DeltaGraph revolves around enabling snapshot queries, which can fetch one or more historical snapshots of a graph. This capability is critical for supporting temporal analysis tasks that require insights into network evolution. The snapshot retrieval process is optimized for interactivity, ensuring prompt responses crucial for applications like visualization tools.

DeltaGraph: Structure and Functionality

DeltaGraph is a hierarchical, directed graph structure composed of nodes corresponding to historical graph snapshots and interior nodes constructed from differential functions over its children. This organization allows for the compact recording of historical information, with a focus on balancing storage requirements against retrieval efficiency. The structure supports various differential functions, such as the Balanced and Intersection functions, providing flexibility in tuning performance characteristics like query latency and storage utilization.

The proposed system employs column-oriented storage and multi-query optimization techniques to further enhance retrieval speeds and reduce storage costs. These strategic choices ensure that DeltaGraph remains a versatile and extensible framework capable of handling the demands of modern dynamic network analysis.

Performance and Evaluation

The authors provide extensive experimental evidence of DeltaGraph's effectiveness, showcasing significant improvements over traditional methods such as interval trees and the Copy+Log approach. Notably, DeltaGraph demonstrates superior scalability and speed, achieving rapid snapshot retrieval even for large datasets with millions of nodes and edges.

By utilizing a partitioned setup across multiple machines, the system illustrates its capability to handle large-scale networks through parallelized processing, further highlighting its advantages in a distributed computing environment. Diverse configurations of the DeltaGraph, such as varying differential functions and event list sizes, are explored, demonstrating the system's adaptability to different network dynamics and user requirements.

Implications and Future Directions

The implications of this research are multifaceted, impacting both theoretical and practical aspects of historical graph data management. By providing a scalable, efficient system for snapshot retrieval, the work paves the way for improved network analysis and understanding of temporal phenomena across various domains.

Future developments could focus on enhancing the adaptive capabilities of DeltaGraph, potentially through the integration of machine learning techniques to predict and preemptively adjust system parameters based on observed query patterns. Additional explorations into automated differential function selection and optimization could further streamline configuration processes, delivering even greater efficiency.

In conclusion, the paper presents a comprehensive solution to the challenges of managing and analyzing historical graph data, offering a finely-tuned system to meet the demands of modern network analysis. With its innovative architecture and performance enhancements, DeltaGraph stands as a significant contribution to the field of temporal database systems.