A ubiquitous computing environment consists of many resources that need to be identified by users and applications. Users and developers require some way to identify resources by human readable names. In addition, ubiquitous computing environments impose additional requirements such as the ability to work well with ad hoc situations and the provision of names that depend on context. The Non-anchored Unified Naming (NUN) system was designed to satisfy these requirements. It is based on relative naming among resources and provides the ability to name arbitrary types of resources. By having resources themselves take part in naming, resources are able to able contribute their specialized knowledge into the name resolution process, making context-dependent mapping of names to resources possible. The ease of which new resource types can be added makes it simple to incorporate new types of contextual information within names. In this paper, we describe the naming system and evaluate its use.
There have been naming systems not targeted for ubiquitous computing environments that also use relative naming. Tilde @cite_2 and Prospero @cite_0 are file systems based on relative naming. Prospero is also able to support a limited form of location-aware computing by creating symbolic links according to the login terminal of a user @cite_7 .
@cite_0 @cite_7 @cite_2
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This thesis describes a novel statistical named-entity (i.e. “proper name”) recognition system known as “MENE” (Maximum Entropy Named Entity). Named entity (N.E.) recognition is a form of information extraction in which we seek to classify every word in a document as being a person-name, organization, location, date, time, monetary value, percentage, or “none of the above”. The task has particular significance for Internet search engines, machine translation, the automatic indexing of documents, and as a foundation for work on more complex information extraction tasks. Two of the most significant problems facing the constructor of a named entity system are the questions of portability and system performance. A practical N.E. system will need to be ported frequently to new bodies of text and even to new languages. The challenge is to build a system which can be ported with minimal expense (in particular minimal programming by a computational linguist) while maintaining a high degree of accuracy in the new domains or languages. MENE attempts to address these issues through the use of maximum entropy probabilistic modeling. It utilizes a very flexible object-based architecture which allows it to make use of a broad range of knowledge sources in making its tagging decisions. In the DARPA-sponsored MUC-7 named entity evaluation, the system displayed an accuracy rate which was well-above the median, demonstrating that it can achieve the performance goal. In addition, we demonstrate that the system can be used as a post-processing tool to enhance the output of a hand-coded named entity recognizer through experiments in which MENE improved on the performance of N.E. systems from three different sites. Furthermore, when all three external recognizers are combined under MENE, we are able to achieve very strong results which, in some cases, appear to be competitive with human performance. Finally, we demonstrate the trans-lingual portability of the system. We ported the system to two Japanese-language named entity tasks, one of which involved a new named entity category, “artifact”. Our results on these tasks were competitive with the best systems built by native Japanese speakers despite the fact that the author speaks no Japanese. Recent growth of the Internet has greatly increased the amount of information that is accessible and the number of resources that are available to users. To exploit this growth, it must be possible for users to find the information and resources they need. Existing techniques for organizing systems have evolved from those used on centralized systems, but these techniques are inadequate for organizing information on a global scale. This article describes Prospero, a distributed file system based on the Virtual System Model. Prospero provides tools to help users organize Internet resources. These tools allow users to construct customized views of available resources, while taking advantage of the structure imposed by others. Prospero provides a framework that can tie together various indexing services producing the fabric on which resource discovery techniques can be applied. As computers become pervasive, users will access processing, storage, and communication resources from locations that have not been practical in the past. Such users will demand support for location-independent computing. While the basic system components used might change as the user moves from place to place, the appearance of the system should remain constant. In this paper we discuss the role of, and requirements for, directory services in support of integrated, location-independent computing. We focus on two specific problems: the server selection problem and the user location problem. We present solutions to these problems based on the Prospero Directory Service. The solutions demonstrate several unique features of Prospero that make it particularly suited for support of location-independent computing. The nearestor near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the of dimensionality." That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points Supported by NAVY N00014-96-1-1221 grant and NSF Grant IIS-9811904. Supported by Stanford Graduate Fellowship and NSF NYI Award CCR-9357849. Supported by ARO MURI Grant DAAH04-96-1-0007, NSF Grant IIS-9811904, and NSF Young Investigator Award CCR9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions (more than 50).