1 The linked data generation process is a complex process which involves multiple stakeholders in the definition of mapping artefacts representation. The creation of these mapping artefacts is error prone, and the quality of the artefacts should be assessed prior to the generation of the linked data. Producing high quality mappings will result in high quality linked data and provide higher confidence to linked data consumers. Furthermore, the source data of these mappings should be regularly monitored to detect data changes which could impact the quality of the resulting linked data dataset. A process designed to offer fresh and high-quality data will benefit both data consumers and producers. Applying the process to a real-world use case demonstrates the feasibility and effectiveness of the process. In this paper we describe a process designed to improve quality within the linked data generation process. Furthermore, we describe the application of the process to a real-world cloud monitoring use case CEUR Workshop Proceedings (CEUR-WS.org)
Cloud monitoring data typically contains time series information with the metric information stored
with a corresponding timestamp. However, commonly used cloud monitoring platforms such as
Prometheus [
Transforming the time series data collected from a system such as Prometheus [
2022 Copyright for this paper by its authors.
Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 3 W3C linked data representation at https://www.w3.org/standards/semanticweb/data
In this paper we provide a discussion of the evolution of the MQV framework and the application to a real-world cloud monitoring use case. The paper is structured as follows: Section 2 describes the background & motivation of this work; Section 3 discusses the application of the framework to the cloud monitoring use case; Section 4 describes related work and Section 5 concludes the paper and describes future work.
Prometheus [
In our research we propose to transform the Prometheus time series data into a more analysis and linkable friendly format, namely linked data. The benefits of transformation include easier data processing and interlinking with semantically similar data. Linking the data with other monitoring data could be used to drive an intent-based system, which can define and accomplish required goals automatically. Within the state-of-the-art limited work could be found which tackles the challenge of transforming Prometheus time series data into a linkable graph format. It is hoped the approach will provide greater insight and flexibility when performing cloud native monitoring.
A framework named the Prometheus RDF Generator has been designed to transform statistical data
from Prometheus [
The MQV framework [4,5] is designed to assess and refine the quality of mappings and its main functionality is described in a paper in the research and innovation track of the SEMANTiCS 2022 conference. The paper describes the framework design and a user evaluation which was conducted with a sample size of 58 participants. Improvements have been made to the framework based on the evaluation such as improved interface aesthetics. Furthermore, in the time since the submission of the SEMANTiCS paper, the framework has been extended to include a change detection component and applied as part of an internship with Ericsson Software Technology.
The component is designed to ensure the freshness of the linked data by detecting and analyzing the changes which occur in the source data. The component regularly monitors the source data to detect new changes and uplifts them into linked data, represented using the Change Detection Ontology (CDO)4. The ontology has been specifically designed by us to represent source data changes. Moreover, the component allows users to define a notification policy, which defines when users will be notified of changes. The policy is defined by users entering thresholds into the user interface. These thresholds define how many changes or types of changes should be detected before a notification is sent. The user interface input is uplifted into linked data and represented using Rei policy ontology [7]. Mappings associated with the source data can be uploaded to the component, which can be analyzed to detect the impact of these changes on them and the resulting linked data. For example, if data has been added to the source data of the mapping, the dataset should be regenerated to capture the new information and ensure the freshness of the data. Figure 2 shows a screenshot of the newer version of the framework.
The framework allows users to select two modes of operation. These modes include i) Mapping Assessment and ii) Change Detection. These two modes have been used during the design of the Prometheus RDF Generator system and their usage is outlined below.
Mapping Assessment. The mapping assessment component was used to assess the quality of mappings5 which were used to uplift the monitoring information. The local ontology functionality which was previously described in [4,5] was utilized as the ontology used to represent the information is still under development. The mappings were uploaded to the framework and a validation report6 was generated, which contains quality assessment and refinement information. The report generated shows that no quality issues have been detected within the mappings. Therefore, no quality refinement information is contained in the report.
Change Detection. The component is currently being used to consistently analyze the monitoring
data which is being collected from the Prometheus server [
The state of the art in mapping quality frameworks and dataset change frameworks has been reviewed.
Mapping Quality Frameworks. Resglass [8] is a rule driven approach which targets RML [9] mappings to detect inconsistencies using rules used to generate linked data datasets. The approach [3] provides a test driven approach which extends an existing RDF test case based architecture to assess and refine the quality of RML mappings. The approach [2] extends an existing quality assessment framework to target R2RML [6] mappings using metrics commonly used to assess the quality of the resulting dataset. Furthermore, the framework is extensible allowing additional metrics to be added.
Dataset Change Frameworks. DSNotify [10] is an approach designed to address change detection and link maintenance in linked data datasets. The approach includes a monitor component which regularly analyses the dataset for resource changes. sparqlPuSH [11] is a framework which enables proactive notification of updates within linked datasets. Users can subscribe to a subset of the dataset using a SPARQL query and will be notified when content within the subset changes. DELTA-LD [12] is designed to address broken links and synchronization of a linked dataset by identifying the delta between two versions of dataset. The approach detects changes by generating features from the properties and objects within the dataset which can be used for comparison.
Future work includes the completion of the development and deployment of the Prometheus RDF Generator. Furthermore, the ontology used to model the time series data will be evaluated using methods such as ontology competency questions [13]. Therefore, ensuring the ontology conforms to the design requirements. A second user evaluation will be completed on the MQV framework which will evaluate the additional change detection component functionality. The evaluation will involve users interacting with the system using predefined tasks and standardized methods such as the Post-Study System Usability Questionnaire [14]. Furthermore, the ontology used within the change detection component to represent change information will be evaluated in a similar manner to the aforementioned methods.
Deploying the framework into a real-world use case is beneficial to demonstrate the usability and effectiveness of the design. The framework ensures that the mappings used within the Prometheus RDF Generator design are of the good quality, thus ensuring high quality data to the consumers. Furthermore, the change detection component allows changes within the source data to be detected and analyzed allowing the impact of changes on the mappings and resulting dataset to be assessed, therefore, promoting fresh linked data. It is hoped the combination of these two components within the use case will aid the data transformation process while demonstrating the feasibility of the design for others.
This research was conducted with the financial support of the SFI AI Centre for Research Training under Grant Agreement No. 18/CRT/6223 at the ADAPT SFI Research Centre at Trinity College Dublin. The ADAPT SFI Centre for Digital Media Technology is funded by Science Foundation Ireland through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant # 13/RC/2106. Application of the MQV framework in the Prometheus use case has been undertaken as part of an internship in Ericsson Software Technology. 7. References