Doctoral thesis defence: 'Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy'
- Date: 7 February 2025, 10:15–14:00
- Location: Ångström Laboratory, Häggsalen, room 10132
- Type: Academic ceremony, Thesis defence
- Lecturer: Tianru Zhang
- Organiser: Department of Information Technology; Division of Scientific Computing,
- Contact person: Salman Toor
Welcome to the defence of Tianru Zhang's doctoral thesis. The defence will be conducted in English.
Date for the nailing of the thesis: Friday 17 January at 14.45 in building 1A, floor 0, Ångström Laboratory.
Supervisiors: Salman Toor and Andreas Hellander
Faculty examiner: Dean Sasu Tarkoma (University of Helsinki).
Abstract: The rise of Big Data has catalyzed numerous advanced data-driven methods, while simultaneously posing significant challenges in data management. This thesis aims to address two fundamental aspects of data management–storage management and information extraction–by leveraging machine learning (ML) techniques. In particular, we focus on two research topics: Storage Hierarchy, which explores hierarchical storage management (HSM) in multitiered storage systems; and Information Hierarchy, which targets the extraction of intrinsic data hierarchies from raw data.
We begin by introducing the key stages of data life cycle and their associated challenges in the Big Data era, alongside a review of machine learning foundations and their potentials for addressing these challenges. Subsequently, we present the Storage Hierarchy project, which is detailed across Paper I, II, and III. In these works, we develop automated, adaptive, and efficient HSM approaches using reinforcement learning (RL). In Paper I we introduce the HSM-RL framework for managing file-level data migration in hierarchical storage system (HSS). It leverages RL to optimize file placement and temporal difference learning for realtime adaptability. Paper II extends this work to complex real–world scenarios using scientific datasets, exploring the framework’s flexibility, scalability, and effectiveness. Moving to finer granularity, Paper III presents ReStore, an RL-based page-level data migration approach that incorporates the unique characteristics of modern Solid-State Drives (SSDs), such as read/write asymmetry and parallelism.
The Information Hierarchy project focuses on autonomous extraction of implicit data hierarchies from raw, unlabeled data. Presented in Paper IV, we propose InfoHier, a framework that integrates self-supervised learning (SSL) with hierarchical clustering (HC) to uncover latent data representations and hierarchical structures. By jointly training SSL and HC through a dynamic balancing loss, InfoHier ensure that the HC results align with the intrinsic data hierarchy. This method facilitates meaningful and structured information extraction andretrieval.
Collectively, the Storage Hierarchy and Information Hierarchy projects advance intelligent data management by enabling efficient storage solutions and autonomous information extraction. These contributions pave the foundation for next generation data management systems, addressing the challenges of Big Data with adaptive and scalable solutions.