Symbolic Intersect Detection: A Method for Improving Spatial Intersect Joins

Due to the increasing popularity of spatial databases, researchers have focused their efforts on improving the query processing performance of the most expensive spatial database operation: the spatial join. While most previous work focused on optimizing the filter step, it has been discovered recently that, for typical GIS data sets, the refinement step of spatial join processing actually requires a longer processing time than the filter step. Furthermore, two-thirds of the time in processing the refinement step is devoted to the computation of polygon intersections. To address this issue, we therefore introduce a novel approach to spatial join optimization that drastically reduces the time of the refinement step. We propose a new approach called Symbolic Intersect Detection (SID) for early detection of true hits. Our SID optimization eliminates most of the expensive polygon intersect computations required by a spatial join by exploiting the symbolic topological relationships between the two candidate polygons and their overlapping minimum bounding rectangle. One important feature of our SID optimization is that it is complementary to the state-of-the-art methods in spatial join processing and therefore can be utilized by these techniques to further optimize their performance. In this paper, we also develop an analytical cost model that characterizes SIDs effectiveness under various conditions. Based on real map data, we furthermore conduct an experimental evaluation comparing the performance of the spatial joins with SID against the state-of-the-art approach. Our experimental results show that SID can effectively identify more than 80% of the true hits with negligible overhead. Consequently, with SID, the time needed for resolving polygon intersect in the refinement step is improved by over 50% over known techniques, as predicted by our analytical model.     

Modelling the three-dimensional elastic constants of parallel-fibred and lamellar bone

The complex hierarchical structure of lamellar bone makes understanding structure–mechanical function relations, very difficult. We approach the problem by first using the relatively simple structure of parallel-fibred bone to construct a mathematical model for calculating Young's moduli in three-dimensions. Parallel-fibred bone is composed essentially of arrays of mineralized collagen fibrils, which are also the basic structural motif of the individual lamellae of lamellar bone. Parallel-fibred bone structure has orthotropic symmetry. As the sizes and shapes of crystals in bone are not well known, the model is also used to compare the cases of platelet-, ribbon- and sheet-reinforced composites. The far more complicated rotated plywood structure of lamellar bone results in the loss of the orthotropic symmetry of individual lamellae. The mathematical model used circumvents this problem by sub-dividing the lamellar unit into a thin lamella, thick lamella, transition zone between them, and the recently observed back-flip lamella. Each of these is regarded as having orthotropic symmetry. After the calculation of their Young's moduli they are rotated in space in accordance with the rotated plywood model, and then the segments are combined to present the overall modulus values in three-dimensions. The calculated trends compare well with the trends in microhardness values measured for circumferential lamellar bone. Microhardness values are, as yet, the only measurements available for direct comparison. Although the model is not directly applicable to osteonal bone, which is composed of many hollow cylinders of lamellar bone, the range of calculated modulus values and the trends observed for off-axis calculations, compare well with measured values. © 1998 Chapman & Hall     

Optical computing: introduction by the feature editors

This feature issue of Applied Optics: Information Processing contains 19 papers on Optical Computing. Many of these papers are expanded versions of presentations given at the Optical Society of America's Sixth Topical Meeting on Optical Computing held in Salt Lake City, Utah, in March 1995. This introduction provides a brief historical account of the series of optical computing meetings and a brief review of the papers contained in this special issue.     

Photorefractive processing for large adaptive phased arrays

An adaptive null-steering phased-array optical processor that utilizes a photorefractive crystal to time integrate the adaptive weights and null out correlated jammers is described. This is a beam-steering processor in which the temporal waveform of the desired signal is known but the look direction is not. The processor computes the angle(s) of arrival of the desired signal and steers the array to look in that direction while rotating the nulls of the antenna pattern toward any narrow-band jammers that may be present. We have experimentally demonstrated a simplified version of this adaptive phased-array-radar processor that nulls out the narrow-band jammers by using feedback-correlation detection. In this processor it is assumed that we know a priori only that the signal is broadband and the jammers are narrow band. These are examples of a class of optical processors that use the angular selectivity of volume holograms to form the nulls and look directions in an adaptive phased-array-radar pattern and thereby to harness the computational abilities of three-dimensional parallelism in the volume of photorefractive crystals. The development of this processing in volume holographic system has led to a new algorithm for phased-array-radar processing that uses fewer tapped-delay lines than does the classic time-domain beam former. The optical implementation of the new algorithm has the further advantage of utilization of a single photorefractive crystal to implement as many as a million adaptive weights, allowing the radar system to scale to large size with no increase in processing hardware.     

A Bayesian network approach to assess and predict software quality using activity-based quality models

ContextSoftware quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty.ObjectiveThe qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model.MethodThe four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed.ResultsThe approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions.ConclusionThe approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.     

Skyline and mapping aware join query evaluation

Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on” on top of a traditional Select-Project-Join query plan. In many real-world applications, the skyline dimensions can be anti-correlated such as the attribute pair {price, mileage} for cars and {price, distance} for hotels. Anti-correlated data are particularly challenging for skyline evaluation and therefore have commonly been ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anti-correlated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local within individual data sources and across disparate sources—to significantly reduce the above-mentioned costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions.     

Constant-phase-mode operation of the light-addressable potentiometric sensor

The constant-phase-mode operation of the light-addressable potentiometric sensor (LAPS) is proposed and demonstrated. In this new operation mode, the temporal change and the spatial distribution of the analyte concentration are recorded in the form of the bias voltage applied to the LAPS sensor plate, which is servo-controlled to maintain the phase of the photocurrent at a constant value with respect to the light modulation. The constant-phase-mode LAPS is advantageous for its wider measurement range and reduction of artifacts.     

A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation

Data envelopment analysis (DEA) is a powerful analytical research tool for measuring the relative efficiency of a homogeneous set of decision making units (DMUs) by obtaining empirical estimates of relations between multiple inputs and multiple outputs related to the DMUs. To further embody multilayer hierarchical structures of these inputs and outputs in the DEA framework, which are prevalent in today’s performance evaluation activities, we propose a generalized multiple layer DEA (MLDEA) model. Starting from the input-oriented CCR model, we elaborate the mathematical deduction process of the MLDEA model, formulate the weights in each layer of the hierarchy, and indicate different types of possible weight restrictions. Meanwhile, its linear transformation is realized and further extended to the BCC form. To demonstrate the proposed MLDEA model, a case study in evaluating the road safety performance of a set of 19 European countries is carried out. By using 13 hierarchical safety performance indicators in terms of road user behavior (e.g., inappropriate or excessive speed) as the model’s input and 4 layered road safety final outcomes (e.g., road fatalities) as the output, we compute the most optimal road safety efficiency score for the set of European countries, and further analyze the weights assigned to each layer of the hierarchy. A comparison of the results with the ones from the one layer DEA model clearly indicates the usefulness and effectiveness of this improvement in dealing with a great number of performance evaluation activities with hierarchical structures.     

Prediction of part orientation error tolerance of a robotic gripper

This paper presents a model which predicts the part orientation error tolerance of a three-fingered robotic gripper. The concept of “self-alignment” is introduced, where the gripper uses the grasping process to bring the workpiece into its final state of orientation. The gripper and part are represented mathematically, and initial contact locations upon grasp closure determined. This information is used to solve for the contact forces present, and criteria are developed to determine if beneficial part motion resulting in self-alignment is expected. The results are visualized via a boundary projected on a reference plane below the part. The model is validated experimentally with a number of part configurations with favorable results. This method presents a useful tool by which the mechanical designer can quantitatively predict the performance of an intuitively designed gripping system.