BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:THE APPLICATION OF REINFORCED MACHINE LEARNING FOR CACHE MANAGEMEN T IN HIGH-LOAD INFORMATION SYSTEMS DTSTART;VALUE=DATE-TIME:20260910T074000Z DTEND;VALUE=DATE-TIME:20260910T081000Z DTSTAMP;VALUE=DATE-TIME:20260922T191404Z UID:indico-contribution-2036@events.ncbj.gov.pl DESCRIPTION:Speakers: Bohdan Volokh (Kyiv National University of Construct ion and Architecture)\nThe performance of modern\, high-load information s ystems significantly depends on the efficiency of cache management. Meanwh ile\, the traditional Least Recently Used and Least Frequently Used polici es are based on fixed heuristics\, which limits their ability to adapt to changes in query structure and intensity.\n\nTo overcome these constraints \, a decision-making approach for adding new objects to the cache is propo sed\, which is based on the tabular Q-Learning method with a deferred asyn chronous update mechanism. It takes into account the key characteristics o f objects\, the context of their use and long-term consequences. The propo sed approach involves training the agent through the accumulation of exper ience and the consideration of deferred rewards\, which ensures an adaptiv e caching policy in real time and improves its efficiency\, in particular by increasing the cache hit ratio.\n\nThe practical value of the results l ies in the ability to improve the efficiency of Clinical Decision Support System users when working with current data. These systems process large\, rapidly updated sets of medical data\, and the developed approach will re duce data access latency and improve the information support provided to d octors when forming clinical recommendations. The proposed approach can al so be applied to other electronic health systems for making decisions rega rding the caching of frequently used data.\n\nhttps://events.ncbj.gov.pl/e vent/468/contributions/2036/ LOCATION: URL:https://events.ncbj.gov.pl/event/468/contributions/2036/ END:VEVENT END:VCALENDAR