## 1 Introduction  
Semantic interoperability: Bridging data discovery and data re-use in strategic decision making. Semantic interoperability requires integrated solutions for all steps where data need to be processed. In this context, innovative use of the web draws attention to levels where strategic decisions may require timely and relevant responses. However, since web usage data contain all the knowledge from ancestors, only comprehensive, efficient analytical modeling could delineate new knowledge from experienced decisions, data reuse, and data discovery, thereby directing the pace of browsing activity toward meaningful assessments. As we would like to emphasize below, a systemic valorization of web usage in the detection of innovative features is an important factor for efficient complementarity of Elastic Stack (ELK) and the GRAPHYP Knowledge Graph (GR) in efficiently processing semantic interoperability Big Data contexts. The respective features of ELK and GR could be appreciated as follows. A recent review of various databases suggests that ELK meets the key requirements where resiliency needs to be demonstrated and tested, as ELK is an optimal solution for data management in any context while \"allowing users to easily monitor their blockchain systems\" [1]. However, in current strategic decision making, ELK needs to be connected to a system that prepares data for contextualized sensory tasks: Knowledge Graphs (KGs) seem to be the best solution. A large consensus in the literature states that \"the accurate derivation and justification of KG plays an important role in controlling the operation and decision-making of energy plants\" [2]. Nevertheless, research is still open to new solutions on the expressiveness and efficiency of KG; the important question of knowledge justification in this context remains a critical and challenging issue [3]. This edition recently benefited from the new results of KG GRAPHYP. This novel framework--a modeling web usage model for creating web graphs ([[https://webgraph.di.unimi.it/](https://webgraph.di.unimi.it/)]([https://webgraph.di.unimi.it/](https://webgraph.di.unimi.it/)) (accessed on 15 May 2023)) from log files--develops a novel additional browsing function as a \"scout\" for discoveries, highlighting the challenges of decision making, with an analytical typology of recordings and a compass for explorable paths to discovery [4]. ELK and GR could thus appear as systems that benefit from synergies and possible functional complementarities in meeting strategic requirements in data discovery and reuse. In the following, we deal with identifying an optimal articulation of ELK and GR at the levels of three research questions; in the meantime, we outline the conditions of other real decision applications. Surprisingly, attention to the science appears to be scant: an up-to-date overview of current and future research on semantic interoperability in meaning-making activities hardly accounts for new features of the emerging workflow of current scientific knowledge assembly. Meanwhile, research in the areas of informed decision making and meaning-finding technologies seems to point to the same behaviors [5]. For our side, we underline the new venues of the late birth diagnosis of \"multimodal knowledge acquisition\" [6]. At the same time, research on digital twins shows the need for further monitoring and control requirements for their real industrial applications from a communication and computing perspective [7]. While research diagnostics are still not very oriented, the area of Big Data has also grown more and more in recent years [8]. Therefore, the question has evolved as to how to collect and store these large volumes and derive valuable insights from these unstructured data. In the following, our answer depends on three research questions:  
* RQ1: Is ELK a suitable solution for decision makers when analyzing and visualizing data? The ELK can provide an open-source answer to the question of gaining insights from big datasets [9].