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.
6230479
Efficient indexing of the historical, present, and future positions of moving objects
Although significant effort has been put into the development of efficient spatio-temporal indexing techniques for moving objects, little attention has been given to the development of techniques that efficiently support queries about the past, present, and future positions of objects. The provisioning of such techniques is challenging, both because of the nature of the data, which reflects continuous movement, and because of the types of queries to be supported. This paper proposes the BBx -index structure, which indexes the positions of moving objects, given as linear functions of time, at any time. The index stores linearized moving-object locations in a forest of B+ -trees. The index supports queries that select objects based on temporal and spatial constraints, such as queries that retrieve all objects whose positions fall within a spatial range during a set of time intervals. Empirical experiments are reported that offer insight into the query and update performance of the proposed technique.
12969098
An approach to evaluate policy similarity
Recent collaborative applications and enterprises very often need to efficiently integrate their access control policies. An important step in policy integration is to analyze the similarity of policies. Existing approaches to policy similarity analysis are mainly based on logical reasoning and boolean function comparison. Such approaches are computationally expensive and do not scale well for large heterogeneous distributed environments (like Grid computing systems). In this paper, we propose a policy similarity measure as a filter phase for policy similarity analysis. This measure provides a lightweight approach to pre-compile a large amount of policies and only return the most similar policies for further evaluation. In the paper we formally define the measure, by taking into account both the case of categorical attributes and numeric attributes. Detailed algorithms are presented for the similarly computation. Results of our case study demonstrates the efficiency and practical value of our approach.
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.
8110502
Data Management in RFID Applications
Nowadays, RFID applications have attracted a great deal of interest due to their increasing adoptions in supply chain management, logistics and security. They have posed many new challenges to existing underlying database technologies, such as the requirements of supporting big volume data, preserving data transition path and handling new types of queries. In this paper, we propose an efficient method to manage RFID data.We explore and take advantage of the containment relationships in the relational tables in order to support special queries in the RFID applications. The experimental evaluation conducted on an existing RDBMS demonstrates the efficiency of our method.
1394805
Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity
Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts.
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
5659557
An Information-Theoretic Definition of Similarity
Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate how our definition can be used to measure the similarity in a number of different domains.
32808871
Trajectory production with the adaptive time-delay neural network
Abstract The adaptive time-delay neural network (ATNN), a paradigm for training a nonlinear neural network with adaptive time delays, has a rich repertoire of capabilities that are characterized in this paper. This network, in which both time delays and weights are adapted, is used to generate circular and figure-eight trajectories, to perform signal production, and to learn repetitive spatial motions. Spatiotemporal signal production has the property that initial segments of signals that contain large amounts of noise can be “cleaned up” to result in trained trajectory motions. Spatiotemporal features are learned as opposed to a point-by-point memorization of the trajectory. Closed-loop, repetitive trajectories that are learned result from training the network to produce attractors consistent with those trajectories. Widely varying starting motions will result in the network being attracted to the repetitive attractor for which it is trained. The network also displays training position invariance, and noise removal when only part of the pattern is trained. Sampling rate effects versus training speed and performance were measured. A comparison between the ATNN and related networks (BP and the TDNN) is reviewed in an example with successful time series prediction of a chaotic series.
1196662
A Nearest-Neighbor Method for Resolving PP-Attachment Ambiguity
We present a nearest-neighbor algorithm for resolving prepositional phrase attachment ambiguities. Its performance is significantly higher than previous corpus-based methods for PP-attachment that do not rely on manually constructed knowledge bases. We will also show that the PP-attachment task provides a way to evaluate methods for computing distributional word similarities. Our experiments indicate that the cosine of pointwise mutual information vector is a significantly better similarity measure than several other commonly used similarity measures.