## 2 Background and Other Works
### Background
#### 2.1.1 Data Availability and Representation  
We first review the literature relating to the availability and capture of data and then the literature relating to data representations. * EXTRACTION OF NEW KNOWLEDG FROM LOGS  
Any new scientific contribution must be retrieved from a dedicated \"conceptual fingerprint\" [20] that must be precisely defined, which is an uneasy task in any innovative context and depends on the network-based learning of cognitive communities, which allows for the identification of possible undiscovered interactions [21]. This raises difficult questions of adversarial recommendations, not only in the categorization of scientific contributions [22]. The extraction of new knowledge can also proceed from the structural components of meaning with related metrics (e.g., Altmetrics: a manifesto [[http://altmetrics.org/manifesto/](http://altmetrics.org/manifesto/)]([http://altmetrics.org/manifesto/](http://altmetrics.org/manifesto/)) accessed on 11 March 2023) in systems in which the interactions of the users and the search engine make sense [23]. 'Searching as learning' thus becomes a tandem of interactions between the exploration of search behaviors and outcomes in learning-related tasks [24]. This trend raises questions on the fairness, security and trustworthiness of those models within this type of deep learning model [25] and opens avenues toward meta-heuristics. * DATA CAPTURE AND EXPLOITATION  
Availability  
The methodology of usage patterns from the analysis of web-based library catalog logs has been developed for almost two decades [26]. Librarians and publishers of scientific materials, notably with Elastic Stack suite ([[https://www.elastic.co/elastic-stack/](https://www.elastic.co/elastic-stack/)]([https://www.elastic.co/elastic-stack/](https://www.elastic.co/elastic-stack/)), accessed on 23 February 2023) and Kibana for the management of logs of big data contents, have long been exploiting (more than 10 years) logs of documentary tracks of research activities ([[https://www.ezpaarse.org/](https://www.ezpaarse.org/)]([https://www.ezpaarse.org/](https://www.ezpaarse.org/))) on a global scale for multipurpose data management operations. Data availability is generally limited by rules of integrity and privacy. Citation counts and usage analytics from logs are two alternative ways to document the quality of the same'seed article' in scholarly digital libraries; both methodologies are supposed to present a complementarity [27]. Tests of data availability and first-degree analytics are given on a large scale in WorldWideScience ([[http://worldwidescience.org/](http://worldwidescience.org/)]([http://worldwidescience.org/](http://worldwidescience.org/))), including experimentations on reusing log data to analyze the semantics of queries in science, with a comparative approach [28] and a large number of participating databases worldwide. Exploitations in large scientific infrastructure: A few examples  
Data capture and re-use from logs to improve services to users/researchers is at work in a growing body of very large infrastructure; we select here a few significant practices in techniques and results. The OakRidge Leadership Computing Facility, which exploits logs in petascale file systems, has been developing since 2017, profiling usage behavior trends and the sharing trends of users, and it invites other HPC centers to develop a similar approach [29] with new methodologies [30] for efficient web mining taxonomy. We already mentioned Bioportal, at the NCBO, for similar log management in the workflow of scientific research, at which they identify and profile browsing behavior types and \"compare exploration strategies across ontologies\" [13]. * DATA REPRESENTATIONS  
Turning web usage data into knowledge involves a variety of operations, techniques and architecture that we examine in accordance with the related phases in GRAPHYP's results on assessor shifts (see Section 3).