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|Citation||PhD Thesis, Stanford University, June 2005
Massive-scale self-administered networks like peer-to-peer and sensor networks have data distributed across thousands of participant hosts. End-users derive benefit by querying data in these networks in various ways (e.g. keyword searches, aggregate queries, and select-project-joins). These networks are however highly dynamic with short-lived hosts being the norm rather than an exception. In the face of such dynamisn, traditional query processing algorithms either abort the query or return best-effort results with ill-defined semantics. In this dissertation, we present motivation, methodology and performance results on designing efficient and scalable systems that ensure valid semantics for a variety of queries: (a) coverage and freshness for keyward queries on text corpus, (b) privacy for access-controlled content under keyword queries, (c) validity for aggregate queries on shared numerical attributes and (d) atomicity, isolation and durability for queries and updates on mutable content.
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