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

Haeusler, Martin; Trojer, Thomas; Kessler, Johannes; Farwick, Matthias; Nowakowski, Emmanuel; Breu, Ruth

doi:10.1007/s10270-019-00725-0

Cited by 13 publications

(10 citation statements)

References 54 publications

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“…A further benefit of a dynamic index is the ability to express different aspects of the modelled data and, therefore, to rely on already established methods for its modelling, such as having a registry for models and metamodels [6], graphs [7], and query languages [8] for efficient model traversal.…”

Section: Approach: Index For Data Dropsmentioning

confidence: 99%

Data Drops for Tunnel Information Modelling

Paskaleva

Niedermoser²,

Vierhauser

et al. 2022

Geomechanics and Tunnelling

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In the Architecture, Engineering and Construction (AEC) industry, as well as in the tunnelling domain, inter‐company processes between partners in different roles in large‐scale construction projects still exhibit great potential towards digitalisation. Thereby, information should be seamlessly shared between partners according to the Building Information Modelling (BIM) paradigm. Today, different types of artefacts (e.g., models, plans, documents, etc.) are shared at different points in time, which differ in terms of requirements, information content, as well as data formats. In this article, we extend and prototypically implement the concept of Data Drops to provide those artefacts in a digitalised form via a shared Data Drop management platform. For this purpose, we have developed a formal, well‐defined indexed data structure on a metadata level. This not only facilitates traceability, but also enables searching for specific meta‐information and provides a common view on Data Drops. In addition, a networked view between different drops can be provided. The approach is being evaluated on the use case of a real tunnel construction project.

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Section: Approach: Index For Data Dropsmentioning

confidence: 99%

Data Drops for Tunnel Information Modelling

Paskaleva

Niedermoser²,

Vierhauser

et al. 2022

Geomechanics and Tunnelling

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“…Different extensions to graph databases exist to introduce the time axis: these temporal graph (TG) databases record how nodes and edges appear, disappear and change their key/value pairs over time. Some of these proposals include Greycat [16] from Hartmann et al and Chronograph [15] from Haeusler et al In particular, Greycat is an open-source solution which reuses several existing database engines (e.g. the LevelDB key/value store) to implement a TG data model.…”

Section: Temporal Graphsmentioning

confidence: 99%

“…This approach was a single application, rather than a distributed architecture in this paper, and it did not integrate the rapid response to events offered by CEP. Haeusler et al [15] present ChronoSphere (CS), an alternative versioned graph technology, applying it to an IT landscape case study. Their system read data from an existing Configuration Management Databases (CMDBs), and created a versioned graph that can be accessed over time.…”

Section: Graphs For Runtime Monitoringmentioning

confidence: 99%

Towards an architecture integrating complex event processing and temporal graphs for service monitoring

Parra-Ullauri

García-Domínguez

Boubeta-Puig

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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Software is becoming more complex as it needs to deal with an increasing number of aspects in volatile environments. This complexity may cause behaviors that violate the imposed constraints. A goal of runtime service monitoring is to determine whether the service behaves as intended to potentially allow the correction of the behavior. It may be set up in advance the infrastructure to allow the detections of suspicious situations. However, there may also be unexpected situations to look for as they only become evident during data stream monitoring at runtime produced by te system. The access to historic data may be key to detect relevant situations in the monitoring infrastructure. Available technologies used for monitoring offer different trade-offs, e.g. in cost and flexibility to store historic information. For instance, Temporal Graphs (TGs) can store the long-term history of an evolving system for future querying, at the expense of disk space and processing time. In contrast, Complex Event Processing (CEP) can quickly react to incoming situations efficiently, as long as the appropriate event patterns have been set up in advance. This paper presents an architecture that integrates CEP and TGs for service monitoring through the data stream produced at runtime by a system. The pros and cons of the proposed architecture for extracting and treating the monitored data are analyzed. The approach is applied on the monitoring of Quality of Service (QoS) of a data-management network case study. It is demonstrated how the architecture provides rapid detection of issues, as well as the ability to access to historical data about the state of the system to allow for a comprehensive monitoring solution. CCS CONCEPTS• Software and its engineering → Maintaining software.

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“…Querying History in Model-driven Engineering The history of an RTM may be encoded via model versioning based on a (general-purpose) model repository, e.g., the solution by Haeusler et al [56]. There is however a considerable difference between the objectives of InTempo and those of model repositories.…”

Section: Related Workmentioning

confidence: 99%

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

Cited by 13 publications

References 54 publications

Data Drops for Tunnel Information Modelling

Data Drops for Tunnel Information Modelling

Towards an architecture integrating complex event processing and temporal graphs for service monitoring

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

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