74793
Towards Optimal Utilization of Main Memory for Moving Object Indexing
In moving object databases, existing disk-based indexes are unable to keep up with the high update rate while providing speedy retrieval at the same time. However, efficient management of moving-object database can be achieved through aggressive use of main memory. In this paper, we propose an Integrated Memory Partitioning and Activity Conscious Twin-index (IMPACT) framework where the moving object database is indexed by a pair of indexes based on the properties of the objects' movement – a main-memory structure manages active objects while a disk-based index handles inactive objects. As objects become active (or inactive), they dynamically migrate from one structure to the other. Moreover, the main memory is also organized into two partitions – one for the main memory index, and the other as buffers for the frequently accessed nodes of the disk-based index. Our experimental study shows that the IMPACT framework provides superior performance.
35430187
ITQS: an integrated transport query system
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SIGMOD 2004 June 13-18, 2004, Paris, France. Copyright 2004 ACM 1-58113-859-8/04/06 . . . <5.00. '( )%* & 5 6 '( & . & = = & 5
16280855
Indexing Fast Moving Objects for kNN Queries Based on Nearest Landmarks
With the rapid advancements in positioning technologies such as the Global Positioning System (GPS) and wireless communications, the tracking of continuously moving objects has become more convenient. However, this development poses new challenges to database technology since maintaining up-to-date information regarding the location of moving objects incurs an enormous amount of updates. Existing indexes can no longer keep up with the high update rate while providing speedy retrieval at the same time. This study aims to improve k nearest neighbor (kNN) query performance while reducing update costs. Our approach is based on an important observation that queries usually occur around certain places or spatial landmarks of interest, called reference points. We propose the Reference-Point-based tree (RP-tree), which is a two-layer index structure that indexes moving objects according to reference points. Experimental results show that the RP-tree achieves significant improvement over the TPR-tree.
16556774
Optimizing Moving Queries over Moving Object Data Streams
With the increasing in demand on location-based aware services and RFIDs, efficient processing of continuous queries over moving object streams becomes important. In this paper, we propose an efficient in-memory processing of continuous queries on themoving object streams.We model moving objects using function of time and use it in the prediction of usefulness of objects with respect to the continuous queries. To effectively utilize the limited memory, we derive several replacement policies to discard objects that are of no potential interest to the queries and design efficient algorithms with light data structures. Experimental studies are conducted and the results show that our proposed method is both memory and query efficient.
2261558
XACML Function Annotations
XACML is being increasingly adopted in large enterprise systems for specifying access control policies. However, the efficient analysis and integration of multiple policies in such large distributed systems still remains a difficult task. In this paper, we propose an annotation technique which is a simple extension to XACML, and may greatly benefit the policy analysis process. We also discuss an important consistency problem during XACML policy translation and point out a few possible research directions.
5467372
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge
Due to the inherent difficulty of processing noisy text, the potential of the Web as a decentralized repository of human knowledge remains largely untapped during Web search. The access to billions of binary relations among named entities would enable new search paradigms and alternative methods for presenting the search results. A first concrete step towards building large searchable repositories of factual knowledge is to derive such knowledge automatically at large scale from textual documents. Generalized contextual extraction patterns allow for fast iterative progression towards extracting one million facts of a given type (e.g., Person-BornIn-Year) from 100 million Web documents of arbitrary quality. The extraction starts from as few as 10 seed facts, requires no additional input knowledge or annotated text, and emphasizes scale and coverage by avoiding the use of syntactic parsers, named entity recognizers, gazetteers, and similar text processing tools and resources.
12904695
Computer-aided kidney segmentation on abdominal CT images
In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation
6491908
A Dependency-based Method for Evaluating Broad-Coverage Parsers
With the emergence of broad-coverage parsers, quantitative evaluation of parsers becomes increasingly more important. We propose a dependency-based method for evaluating broad-coverage parsers. The method offers several advantages over previous methods that are based on phrase boundaries The error count score. We propose here is not only more intuitively meaningful than other scores, but also more relevant to semantic interpretation. We will also present an algorithm for transforming constituency trees into dependency trees so that the evaluation method is applicable to both dependency and constituency grammars. Finally, we discuss a set of operations for modifying dependency trees that can be used lo eliminate inconsequential differences among different parse trees and allow us to selectively evaluate different aspects of a parser.
22202534
Subspace-based active user identification for a collision-free slotted ad hoc network
We propose a novel spreading code scheme, transmitter-receiver-based code, for wireless ad hoc networks. The design facilitates collision resolution using multiuser detection at each node, and is more bandwidth efficient than creating orthogonal channels in time or frequency. A subspace-based receiver structure is introduced, which identifies users of interest, or "active" users, with minimal prior information on the spreading code ensemble. A subspace-based blind multiuser detector can then be implemented to suppress multiaccess interference. The performance of the proposed active user identifier is studied by investigating its false alarm rate P/sub f/ and miss rate P/sub m/. Tradeoffs between P/sub f/ and P/sub m/ are discussed, and a graphical method to determine the threshold value d/sub th/ of the decision statistic used in discriminating between active and inactive channels is introduced.
15110225
Efficient Parsing for Korean and English: A Parameterized Message- Passing Approach
This article presents an efficient, implemented approach to cross-linguistic parsing based on Government Binding (GB) Theory (Chomsky 1986) and followers. One of the drawbacks to alternative GB-based parsing approaches is that they generally adopt a filter-based paradigm. These approaches typically generate all possible candidate structures of the sentence that satisfy X theory, and then subsequently apply filters in order to eliminate those structures that violate GB principles. (See, for example, Abney 1989; Correa 1991; Dorr 1993; Fong 1991.) The current approach provides an alternative to filter-based designs that avoids these difficulties by applying principles to descriptions of structures without actually building the structures themselves. Our approach is similar to that of Lin (1993) in that structure-building is deferred until the descriptions satisfy all principles; however, the current approach differs in that it provides a parameterization mechanism along the lines of Dorr (1994) that allows the system to be ported to languages other than English. We focus particularly on the problem of processing head-final languages such as Korean. We are currently incorporating the parser into a machine translation (MT) system called PRINCITRAN. l In general, parsers of existing principle-based interlingual MT systems are exceedingly inefficient, since they tend to adopt the filter-based paradigm. We combine the benefits of the message-passing paradigm with the benefits of the parameterized approach to build a more efficient, but easily extensible system, that will ultimately be used for MT. The algorithm has been implemented in C++ and successfully tested on well-known, translationally divergent sentences. We present a general framework for parsing by message passing and describe our implementation of GB principles as attribute-value constraints. We then present the parameterization framework, demonstrating the feasibility of handling cross-linguistic variation within the message-passing framework. A technique for automatic precompilation of parameter settings is described. Finally, we compare the efficiency of the