Durable graph pattern queries on historical graphs

Semertzidis, Konstantinos; Pitoura, Evaggelia

doi:10.1109/icde.2016.7498269

Cited by 45 publications

(27 citation statements)

References 22 publications

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“…However, existing graph databases on the market did not offer built-in versioning capabilities [R2]. Using a general-purpose graph database (e.g., Neo4j 2 or Titan 3 ) and managing the versioning process entirely on the application side has already been proven by various authors to be possible [8,66,67,71]. However, such approaches greatly increase the complexity of the resulting graph structure as well as the complexity of the queries that operate on it.…”

Section: Solution Overviewmentioning

confidence: 99%

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ChronoSphere: a graph-based EMF model repository for IT landscape models

et al. 2019

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IT Landscape models are representing the real-world IT infrastructure of a company. They include hardware assets such as physical servers and storage media, as well as virtual components like clusters, virtual machines and applications. These models are a critical source of information in numerous tasks, including planning, error detection and impact analysis. The responsible stakeholders often struggle to keep such a large and densely connected model up-to-date due to its inherent size and complexity, as well as due to the lack of proper tool support. Even though modeling techniques are very suitable for this domain, existing tools do not offer the required features, scalability or flexibility. In order to solve these challenges and meet the requirements that arise from this application domain, we combine domain-driven modeling concepts with scalable graphbased repository technology and a custom language for model-level queries. We analyze in detail how we synthesized these requirements from the application domain and how they relate to the features of our repository. We discuss the architecture of our solution which comprises the entire data management stack, including transactions, queries, versioned persistence and metamodel evolution. Finally, we evaluate our approach in a case study where our open-source repository implementation is employed in a production environment in an industrial context, as well as in a comparative benchmark with an existing state-of-the-art solution. Keywords Model-driven engineering • Model repositories • Versioning • Graph database • IT landscape 1 Introduction Model-driven engineering (MDE) is a discipline that aims at improving the processes, workflows and products of exist-Communicated by Dr. Ana Moreira.

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Section: Solution Overviewmentioning

confidence: 99%

“…Other related approaches, e.g., by Semertzidis and Pitoura [66,67] or by Han et al [29], assume the existence of a series of graph snapshots as input to their solutions. These approaches do not aim for online transaction processing (OLTP) capabilities and focus on the analysis of a series of static graphs.…”

Section: Related Graph Versioning Solutionsmentioning

confidence: 99%

ChronoSphere: a graph-based EMF model repository for IT landscape models

et al. 2019

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“…[10], [27] perform subgraph matching, but with the additional support for wildcards and/or approximate matches, and Lin et al [28] address the problem of generalized subgraph query processing. Finally, Semertzidis et al [29] considered pattern queries over time-evolving graphs.…”

Section: Background a Related Workmentioning

confidence: 99%

Hybrid algorithms for subgraph pattern queries in graph databases

Katsarou

Ntarmos

Triantafillou

2017

2017 IEEE International Conference on Big Data (Big Data)

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“…The system can real-time detect suspicious pattern graphs and early warn high-risk data transfer modes on constantly updated network graphs. Semertzidis and Pitoura proposed an approach to find the most durable matches of an input graph pattern on graphs that evolve over time [12]. In [13], an incremental graph matching algorithm was proposed for updates of pattern graphs.…”

Section: Related Workmentioning

confidence: 99%

“…At present, the study of incremental graph pattern matching is still in its infancy; existing work [6][7][8][9][10][11][12] mainly focuses on the updates of data graphs. Fan et al proposed the incremental graph simulation algorithm IncMatch [6,7].…”

Section: Related Workmentioning

confidence: 99%

Incremental Graph Pattern Matching Algorithm for Big Graph Data

Zhang

Gao

2018

Scientific Programming

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Graph pattern matching is widely used in big data applications. However, real-world graphs are usually huge and dynamic. A small change in the data graph or pattern graph could cause serious computing cost. Incremental graph matching algorithms can avoid recomputing on the whole graph and reduce the computing cost when the data graph or the pattern graph is updated. The existing incremental algorithm PGC IncGPM can effectively reduce matching time when no more than half edges of the pattern graph are updated. However, as the number of changed edges increases, the improvement of PGC IncGPM gradually decreases. To solve this problem, an improved algorithm iDeltaP IncGPM is developed in this paper. For multiple insertions (resp., deletions) on pattern graphs, iDeltaP IncGPM determines the nodes' matching state detection sequence and processes them together. Experimental results show that iDeltaP IncGPM has higher efficiency and wider application range than PGC IncGPM.

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Durable graph pattern queries on historical graphs

Cited by 45 publications

References 22 publications

ChronoSphere: a graph-based EMF model repository for IT landscape models

ChronoSphere: a graph-based EMF model repository for IT landscape models

Hybrid algorithms for subgraph pattern queries in graph databases

Incremental Graph Pattern Matching Algorithm for Big Graph Data

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