Indaleko: The Unified Personal Index
William Anthony Mason

TL;DR
Indaleko introduces a memory-aligned personal index that enables natural language, context-aware retrieval across large, multi-platform datasets, outperforming existing keyword-based systems in precision and speed.
Contribution
This work presents the Unified Personal Index (UPI), a novel architecture integrating temporal, spatial, and activity metadata for memory-based personal information retrieval.
Findings
Successfully processes multi-dimensional, memory-based queries
Achieves sub-second response times with memory anchor indexing
Outperforms commercial systems on context-aware retrieval
Abstract
Personal information retrieval fails when systems ignore how human memory works. While existing platforms force keyword searches across isolated silos, humans naturally recall through episodic cues like when, where, and in what context information was encountered. This dissertation presents the Unified Personal Index (UPI), a memory-aligned architecture that bridges this fundamental gap. The Indaleko prototype demonstrates the UPI's feasibility on a 31-million file dataset spanning 160TB across eight storage platforms. By integrating temporal, spatial, and activity metadata into a unified graph database, Indaleko enables natural language queries like "photos near the conference venue last spring" that existing systems cannot process. The implementation achieves sub-second query responses through memory anchor indexing, eliminates cross-platform search fragmentation, and maintains…
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Taxonomy
TopicsPersonal Information Management and User Behavior · Information Retrieval and Search Behavior · Data Quality and Management
