With the explosion in the amount of semistructured data users access and store in personal information management systems, there is a critical need for powerful search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically support some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multidimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in noncontent dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.

With the explosion in the amount of semi-structured data users access and store in personal information management systems, there is a need for complex search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools usually index text content, allowing for some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multi-dimensional approach to semi-structured data searches in personal information management systems by allowing users to provide fuzzy structure and metadata conditions in addition to keyword conditions. Our techniques provide a complex query interface that is more comprehensive than content-only searches as it considers three query dimensions (content, structure, metadata) in the search. We propose techniques to individually score each dimension, as well as a framework to integrate the three dimension scores into a meaningful unified score. Our work is integrated in Wayfinder, an existing fully-functioning file system. We perform a thorough experimental evaluation of our techniques to show the effect of approximating individual dimensions on the overall scores and ranks of files, as well as on query performance. Our experiments show that our scoring strategy adequately takes into account the approximation in each dimension to efficiently evaluate fuzzy multi-dimensional queries. In addition, fuzzy query conditions in non-content dimensions can significantly improve scoring (and thus ranking) accuracy.

With the explosion in the amount of semi-structured data users access and store, there is a need for complex search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools usually index text content, allowing for some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional querying approach to semi-structured data searches in personal information systems by allowing users to provide fuzzy structure and metadata conditions in addition to traditional keyword conditions. The provided query interface is more comprehensive than content-only searches as it considers three query dimensions (content, structure, metadata) in the search. We have implemented our proposed approach in the Wayfinder file system. In this demo, we will use this implementation to both present an overview of the unified scoring framework underlying the fuzzy multi-dimensional querying approach and demonstrate its potential in improving search results.

We consider the problem of ensuring high data availability in federated content sharing systems. Ideally, such a system would provide high data availability in a device transparent manner so that users are not faced with the time-consuming and error-prone task of managing data replicas across the constituent devices of the system. We propose a novel unified availability model and a decentralized replication algorithm to approximate this ideal. Our availability model addresses three different concerns: availability during connected operation (online), availability during disconnected operation (offline), and availability after permanent disconnection from the federated system (ownership). Our replication algorithm centers around the intuition that devices should selfishly use their local storage to ensure offline and ownership availability for their individual owners. Excess storage, however, is used communally to ensure high online availability for all shared content. Evaluation of an implementation shows that our algorithm rapidly reaches stable and communally desirable configurations when there is sufficient space. Consistent with the fact that devices in a federated system are owned by different users, however, as space becomes highly constrained, the system approaches a non-cooperative configuration where devices only hoard content to serve their individual owners' needs

We introduce PlanetP, content addressable publish/subscribe service for unstructured peer-to-peer (P2P) communities. PlanetP supports content addressing by providing: (1) a gossiping layer used to globally replicate a membership directory and an extremely compact content index; and (2) a completely distributed content search and ranking algorithm that help users find the most relevant information. PlanetP is a simple, yet powerful system for sharing information. PlanetP is simple because each peer must only perform a periodic, randomized, point-to-point message exchange with other peers. PlanetP is powerful because it maintains a globally content-ranked view of the shared data. Using simulation and a prototype implementation, we show that PlanetP achieves ranking accuracy that is comparable to a centralized solution and scales easily to several thousand peers while remaining resilient to rapid membership changes.

We describe DDDDRRaW, a prototype toolkit for distributed real-time rendering on commodity clusters. In constrast to most work on cluster computing, DDDDRRaW supports a repeated, low-latency computation, the drawing of frames which must take place on a time scale of 30-100 ms. DDDDRRaW employs image layer decomposition, a rendering-specific work partitioning algorithm described and evaluated using simulation. In this paper we address implementation issues. In particular, one important issue we explore is how to exploit the potential parallelism afforded by the multiple hardware resources of each node: the CPU, the network adapter and the video card. We evaluate DDDDRRaW's live performance on two small workstation clusters representing different points in the technology spectrum. Our results show that DDDDRRaW effectively exploits cluster resources to improve real-time rendering performance and should scale well to moderately sized clusters.