Hybrid search plan generation for generalized graph pattern matching

Barkowsky, Matthias; Giese, Holger

doi:10.1016/j.jlamp.2020.100563

Cited by 5 publications

(6 citation statements)

References 5 publications

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“…Section 3) is based on the Eclipse Modeling Framework (EMF) [25,54], which is a widespread MDE technology for creating metamodels of architectures. For pattern matching, we employ the Story Diagram Interpreter (SD) [30] optimized according to [2]. SD uses local search to start the search from a specific element of the graph and thus reduces the pattern matching effort [38].…”

Section: Discussionmentioning

confidence: 99%

“…As an example, assume the following graph query q(n 1 , θ ) where θ n 1 , (n 1.1 ∧ ¬n 1.2 ), which captures the following (structural) requirement: all matches of n 1 can be extended to a pattern n 1.1 but not to a pattern n 1. 2 The patterns are based on the TG of system Σ (introduced in Section 2.1) and are shown in Fig. 1 (bottom).…”

Section: Efficient Pattern Matching For Queriesmentioning

confidence: 99%

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A Scalable Querying Scheme for Memory-efficient Runtime Models with History

Sakizloglou,

Ghahremani,

Barkowsky

et al. 2020

Preprint

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Runtime models provide a snapshot of a system at runtime at a desired level of abstraction. Via a causal connection to the modeled system and by employing model-driven engineering techniques, runtime models support schemes for (runtime) adaptation where data from previous snapshots facilitate more informed decisions. Nevertheless, although runtime models and model-based adaptation techniques have been the focus of extensive research, schemes that treat the evolution of the model over time as a first-class citizen have only lately received attention. Consequently, there is a lack of sophisticated technology for such runtime models with history.We present a querying scheme where the integration of temporal requirements with incremental model queries enables scalable querying for runtime models with history. Moreover, our scheme provides for a memory-efficient storage of such models. By integrating these two features into an adaptation loop, we enable efficient history-aware self-adaptation via runtime models, of which we present an implementation.

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Section: Discussionmentioning

confidence: 99%

Section: Efficient Pattern Matching For Queriesmentioning

confidence: 99%

A Scalable Querying Scheme for Memory-efficient Runtime Models with History

Sakizloglou,

Ghahremani,

Barkowsky

et al. 2020

Preprint

Self Cite

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“…Our implementation of InTempo is based on the Eclipse Modeling Framework (EMF) [36,101], which is a widespread MDE technology for creating software systems. For pattern matching, we use the Story Pattern Matcher [48] using the search plan generation strategy presented in [3]. The Matcher uses local search to start the search from a specific element of the graph and thus reduces the pattern matching effort [64].…”

Section: Methodsmentioning

confidence: 99%

“…Invocations are triggered by effectors (Effector) and invocation results are tracked via Fig. 3 Rudimentary example of an RTM monitoring probes (Probe) that are attached to Services. Probes are generated periodically or upon events in the real world.…”

Section: Smart Healthcare Systemmentioning

confidence: 99%

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Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. 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. 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. 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. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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Keeping Pace with the History of Evolving Runtime Models

Sakizloglou

Barkowsky

Giese

2021

Fundamental Approaches to Software Engineering

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Structural runtime models provide a snapshot of the constituents of a system and their state. Capturing the history of runtime models, i.e., previous snapshots, has been shown to be useful for a number of aims. Handling, however, history at runtime poses important challenges to tool support. We present the InTempo tool which is based on the Eclipse Modeling Framework and encodes runtime models as graphs. Key features of InTempo, such as, the integration of temporal requirements into graph queries, the in-memory storage of the model, and a systematic method to contain the model’s memory consumption, intend to address issues which seemingly place limitations on the available tool support. InTempo offers two operation modes which support both runtime and postmortem application scenarios.

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Hybrid search plan generation for generalized graph pattern matching

Cited by 5 publications

References 5 publications

A Scalable Querying Scheme for Memory-efficient Runtime Models with History

A Scalable Querying Scheme for Memory-efficient Runtime Models with History

Incremental execution of temporal graph queries over runtime models with history and its applications

Keeping Pace with the History of Evolving Runtime Models

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