[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121939-en":3,"doc-seo-121939-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121939,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Scaling and Explaining Machine Learning Powered Database Applications - Doctoral Thesis","For decades, database systems have provided reliable foundations for applications across finance, web services, business intelligence, social analysis, and healthcare. Recent large-scale machine learning deployments now reshape these systems by enabling adaptive prediction services, while reducing transparency and straining scalability between growing application loads and legacy databases. This thesis addresses both issues through on-database contextual explanation and runtime contextual/counterfactual explanation aligned with GDPR right-to-explanation, and through transactional caching that scales legacy databases via external look-aside caches while preserving application invariants and correctness under provable guarantees.","This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e. g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use:  \n• This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated.  \n• A copy can be downloaded for personal non-commercial research or study, without prior permission or charge.  \n• This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author.  \n• The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author.  \n• When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.  \nScaling and Explaining Machine Learning Powered Database Applications  \nShuai An  \nU  \nR  \nG  \nH  \nO  \nF  \nE  \nD  \nDoctor of Philosophy  \nLaboratory for Foundations of Computer Science School of Informatics  \nUniversity of Edinburgh  \nAbstract  \nFor decades, database systems have been the backbone of applications in a wide range of domains, e.g., finance, web services, business intelligence, social analysis, healthcare. Meanwhile, the resurgence of machine learning in particular large-scale deep learning services provided by big companies in recent years has been expeditiously reshaping these applications, enabling them to easily take advantage of model prediction services and be significantly more adaptive, intelligent and capable. However, this movement causes database applications to be less transparent, rendering database vendors incapable of offering reliable insights and explanations to their application customers. In addition, the increasing popularity of machine learning features rapidly boosts the scale of applications while the underlying legacy database systems are struggling to scale out and keep up the pace, causing tension between the application load and database system.  \nThis thesis aims to address these two challenges. The first part of the thesis presents anew concept, referred to as on-database contextual explanation, and a suit of associated techniques to empower database applications to explain their learning powered decisions to end customers, even if they are generated by third-party cloud-based prediction services that opt not to offer explainability. The key intuition is that the data exchange between the databases and remote machine learning models already gives the applications a dynamic context that contains vital information to deduce reliable explanations, independent of the explainability of the remote models. To elaborate on and exploit this, we develop algorithms and systems to efficiently compute contextual feature explanation and counterfactual explanation by examining and monitoring databases at runtime, faithfully conforming to the “right-to-explanation” policy requested by GDPR. We also  \nevaluate its effectiveness via extensive experiments and real-world case studies.  \nThe second part of thesis develops a means to scale out legacy databases without migrating them to the cloud or re-deploying with added hardware. Our method is to augment legacy database systems at runtime with external caches, allowing us to offload database load to a look-aside cache on-the-fly. However, a caveat of extending database systems with lightweight caches is that the augmented system as a whole loses correctness guarantees that a typical database system offers especially for transactional workloads, requiring the developers to re-design the applications. To this end, we present transactional caching, a scheme that maintains application invariant over the augmented system. It works with any key-value in-memory caches, e.g., Redisand Memcached, and empowers them to assure that applications always see a monotonically increasing snapshot of the da","cbCaisZ6J83kLr07","https://ap.wps.com/l/cbCaisZ6J83kLr07","pdf",3185414,1,212,"English","en",105,"# Abstract\n# Main Contributions\n## On-Database Contextual Explanation\n## Contextual and Counterfactual Explanation at Runtime\n## Transactional Caching for Legacy Databases\n## Transactional Cache Replacement Policies","[{\"question\":\"What two core challenges does the thesis focus on?\",\"answer\":\"It addresses (1) reduced transparency when machine learning prediction services are integrated into database applications, and (2) scalability tension between expanding application load and legacy database systems.\"},{\"question\":\"How does on-database contextual explanation improve explainability?\",\"answer\":\"It uses the data exchange context between databases and remote machine learning models to compute reliable contextual and counterfactual explanations, even when the remote models do not provide explainability interfaces.\"},{\"question\":\"What is transactional caching and why is it needed?\",\"answer\":\"Transactional caching augments legacy databases with external look-aside caches while redesigning for invariant preservation, maintaining monotonically increasing snapshots so applications keep correctness guarantees for transactional workloads.\"}]","Scaling and Explaining Machine Learning Powered Database Applications - 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