5688593
A Cluster-Based TMO-Structured Scalable Approach for Location Information Systems
Location-based service (LBS) systems have recently become a subject of active research and development. A key issue in such systems is efficient management of current and past location information of moving items. In the past two years, we have been establishing a new LBS system architecture, named the GALIS (Gracefully Aging Location Information System), which is based on the new approach of using a cluster of database server nodes and effecting efficient distributed and parallel computing in both real-time update of location records of moving items and processing of location-related queries. Each server node is dedicated to keeping records relevant to a different geographical zone and a different temporal zone. Each subsystem in GALIS is structured as a TMO network for the sake of easy analysis and expansion. A prototype implementation of GALIS has been realized only partially, and its full implementation is under way. In this paper, major features of GALIS are presented together with discussions on trade-off considerations which led to the adoption of those features. Issues remaining for further research are also briefly discussed.
206985371
Framework and network based multimedia object management environment
Because multimedia objects are becoming more prevalent, on ever increasing volume, inventing an efficient multimedia object management environment is a matter of increasing urgency. In recognition of this crisis, we developed the multimedia object management environment (MOME) that includes a suite of tools such as the Vortex framework, and network file indexer (NFI). MOME also includes a fully featured graphical user interface for maximum user control and flexibility. With meta data, it automatically generates indexes and paths for different types of multimedia objects and allows users to quickly find what they are looking for. In this paper, we address the background, architecture, and performance of MOME in detail.
32386430
An Object-Oriented BSP Tree Algorithm for Hidden Surface Removal
Since the BSP tree algorithm was introduced, many extended or applied algorithms for the original BSP tree algorithm have been reported. Most of the previous work dealt with polygons that are components of polyhedra. If a scene consists of many polyhedra and they are allowed to move, the management cost of a BSP tree becomes expensive and the size of the tree becomes large. This paper presents an object-oriented BSP tree algorithm that can more efficiently deal with a large number of moving polyhedra.
28214269
Active rule processing in the BioCompose database
BioCompose is an object-relational database query tool that runs on top of any relational database and provides an intelligent and complete object-relational interface to the user. Unlike the traditional approach, which is completely table driven, queries in BioCompose are structured along the lines of natural language and sentences. In BioCompose, the database server continuously monitors the triggers and integrity constraints which are expressed as logical rules and are evaluated based on incoming events which include real time events, value events, transaction events, abstract events, and message events. The paper presents a novel approach that compiles triggers and integrity constraints into an asynchronous network to minimize the rule evaluation effort under different kinds of events.
5669766
Automatic Estimation of 3D Transformations using Skeletons for Object Alignment
An algorithm for automatic estimation of 3D transformations between two objects is presented in this paper. Skeletons of the 3D objects are created using a fully parallel thinning technique, feature point pairs (land markers) are automatically extracted from skeletons, and a least squares method is applied to solve an over determined linear system to estimate the 3D transformation matrix. Experiments show that this method is quite accurate when the translations and rotation angles are small, even when there is some noise in the data. The estimation process requires about 2 seconds on an Intel Centrino Laptop with 512 MB memory, for a complex model with about 37,000 object points and 500 object points for its skeletons
10441616
Automatic Gait Optimization with Gaussian Process Regression
Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.
566237
Regularized discriminant analysis for high dimensional, low sample size data
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear and Quadratic Discriminant Analysis, called Regularized Discriminant Analysis (RDA), which has been shown to be more flexible in dealing with various class distributions. RDA applies the regularization techniques by employing two regularization parameters, which are chosen to jointly maximize the classification performance. The optimal pair of parameters is commonly estimated via cross-validation from a set of candidate pairs. It is computationally prohibitive for high dimensional data, especially when the candidate set is large, which limits the applications of RDA to low dimensional data.In this paper, a novel algorithm for RDA is presented for high dimensional data. It can estimate the optimal regularization parameters from a large set of parameter candidates efficiently. Experiments on a variety of datasets confirm the claimed theoretical estimate of the efficiency, and also show that, for a properly chosen pair of regularization parameters, RDA performs favorably in classification, in comparison with other existing classification methods.
33246096
Optimal net assignment
We study in this paper the net assignment problem subject to the capacity constraint, selection constraint and routing constraint. Given two adjacent channels separated by a cell row, and a set of nets in each of the two channels, this problem is to assign to the cell row a subset of nets in each channel such that without violating any given constraint, the sum of the remaining densities of the two channels is minimized. The capacity constraint requires the density caused by the nets, which are assigned to the cell row, to be no more than a user-specified number k, where k is no more than the number of tracks available for routing over that cell row. The selection constraint specifies in each channel the subset of nets which are candidates to be assigned to the cell row. The routing constraint requires each net to be either completely assigned to the cell row or to stay in its channel. This problem can find its application in modeling a practical over-the-cell routing problem in which the whole region over the cell row is two-layer routable for the nets in the two adjacent channels. We present an optimal algorithm to solve this problem and provide experimental results to support our algorithm. >
6023729
Accurately Choosing Execution Runs for Software Fault Localization
Software fault localization involves locating the exact cause of error for a “failing” execution run – a run which exhibits an unexpected behavior. Given such a failing run, fault localization often proceeds by comparing the failing run with a “successful” run, that is, a run which does not exhibit the unexpected behavior. One important issue here is the choice of the successful run for such a comparison. In this paper, we propose a control flow based difference metric for this purpose. The difference metric takes into account the sequence of statement instances (and not just the set of these instances) executed in the two runs, by locating branch instances with similar contexts but different outcomes in the failing and the successful runs. Given a failing run πf and a pool of successful runs S, we choose the successful run πs from S whose execution trace is closest to πf in terms of the difference metric. A bug report is then generated by returning the difference between πf and πs. We conduct detailed experiments to compare our approach with previously proposed difference metrics. In particular, we evaluate our approach in terms of (a) effectiveness of bug report for locating the bug, (b) size of bug report and (c) size of successful run pool required to make a decent choice of successful run.