EDBT 2026 Demo / reviewers in the wild / expert
Divya Natolana Ganapathy
dblp:340/0433
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-2352-3037ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Services computing and microservices · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs
knowledge graph construction |
0.7 | 1 | 2023 | A Semantically Rich Framework to Automate Cloud Service Level Agreements · IEEE Trans. Serv. Comput. 2023 |
Services computing and microservices
service level agreement |
0.7 | 1 | 2023 | A Semantically Rich Framework to Automate Cloud Service Level Agreements · IEEE Trans. Serv. Comput. 2023 |
Cloud and datacenter computing › cloud service management
cloud service selection |
0.2 | 1 | 2023 | A Semantically Rich Framework to Automate Cloud Service Level Agreements · IEEE Trans. Serv. Comput. 2023 |
Methods — techniques the papers use, named apart from their topics
semantic knowledge graph · 2.0deontic rule extraction · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A Semantically Rich Framework to Automate Cloud Service Level AgreementsabstractConsumers evaluate and choose cloud-based services based on the Service Level Agreements (SLA). These agreements list the service terms and metrics to be agreed upon by the service providers and the customers. Current cloud SLAs are text documents that require significant manual effort to parse and determine if providers meet the SLAs. Moreover, due to the lack of standardization, providers differ in the way they define the terms and metrics, making it more difficult to compare different provider SLAs. We have developed a novel framework to significantly automate the process of extracting knowledge embedded in cloud SLAs and representing it in a semantically rich knowledge graph helping the user to make a calculated decision in choosing a provider. Our framework captures the key terms, measures, and deontic rules, in the form of obligations and permissions present in the cloud SLAs. In this paper, we discuss our framework, technique, and challenges in automating the cloud services agreement. We also describe our results and their validation against well-established standards. Divya Natolana Ganapathy, Karuna P. Joshi |
IEEE Trans. Serv. Comput. | 1 |