18594508
An index structure for efficient reverse nearest neighbor queries
The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new index structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single index structure is employed for a dynamic database, in contrast to the use of multiple indexes in previous work. This leads to significant savings in dynamically maintaining the index structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our index structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our index structure extremely preferable in both static and dynamic cases.
5363634
Applying bulk insertion techniques for dynamic reverse nearest neighbor problems
Reverse nearest neighbors queries have emerged as an important class of queries for spatial and other types of databases. The Rdnn-tree is an R-tree based structure that has been shown to perform outstandingly for such kind of queries. However, one practical problem facing it (as well as other type of indexes) is how to effectively construct the index from stretch. In this case, the cost of constructing and maintaining a Rdnn-Tree is about twice the cost of an R-Tree. Normal insertion into a Rdnn-Tree is performed one point at a time, known as single point insertion. The question arises, can insertion be improved there by reducing the construction and maintenance cost. In this paper we propose a bulk-loading technique, which is capable of significantly, improve the performance of constructing the index from stretch, as well as insert a large amount of data. Experiments show that our method outperforms the single point insertion significantly.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
11135944
Fuzzy Clustering of Web Logs for User Classification
40285763
Critical Points for Interactive Schema Matching
Experience suggests that fully automated schema matching is infeasible, especially for n-to-m matches involving semantic functions. It is therefore advisable for a matching algorithm not only to do as much as possible automatically, but also to accurately identify the critical points where user input is maximally useful. Our matching algorithm combines several existing approaches, with a new emphasis on using the context provided by the way elements are embedded in paths. A prototype tested on biological data (gene sequence, DNA, RNA, etc.) and on bibliographic data, shows significant performance improvements from user feedback and context checking. In non-interactive mode on the purchase order schemas, it compares favorably with COMA, the most mature schema matching system in literature, and also correctly identifies critical points for user input.
20361854
Protein Metal Binding Residue Prediction Based on Neural Networks
Over one-third of protein structures contain metal ions, which are the necessary elements in life systems. Traditionally, structural biologists were used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpreting the function formation and reaction mechanism of enzyme by their structures and observations from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict the protein metal-binding amino acid residues from its sequence information only by neural networks with sliding window-based feature extraction and biological feature encoding techniques. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium), the metal-binding residues are identified by the proposed method with higher than 90% sensitivity and very good accuracy under 5-fold cross validation. With such promising results, it can be extended and used as a powerful methodology for metal-binding characterization from rapidly increasing protein sequences in the future.
27737931
Designing multimedia applications on real-time systems with SMP architecture
Large real-time multimedia systems such as flight simulators have adopted multiprocessor architecture to handle the large amount of events and computations. Existing realtime scheduling algorithms designed for single-processor systems such as Rate Monotonic (RM) and Earliest Deadline First Algorithm (EDF) have very poor performance on multiprocessor systems. On the other hand, most online scheduling algorithms for multiprocessor systems have poor schedulability conditions for real-time applications. In this paper, we study different ways to design real-time applications on SMP machines.
14056151
Distributed pinwheel scheduling with end-to-end timing constraints
Algorithms for allocating resources and scheduling tasks are important to the success of many real-time systems with end-to-end performance requirements. In this paper, an end-to-end scheduling model based on the pinwheel scheduling algorithms is presented for distributed real-time systems. We discuss how tasks on different nodes may be transformed to have harmonic periods. We also present algorithms to adjust the phases between schedules on neighboring nodes so that the overall end-to-end delay is reduced. Using the pinwheel approach, schedules on different nodes are closely synchronized and more static. However, for many real-time systems, this practical approach may provide a more predictable performance and a shorter end-to-end delay.
42000677
Real-Time Database Benchmark Design for Avionics Systems
Traditionally, database systems have been developed for data processing applications. With the development of CAD/CAM applications, object-oriented databases have been shown to be more flexible and effective in providing the kind of performance more adequate for CAD users. Recently, as a result of the rapid growth in embedded and autonomous systems applications, many database applications are moving toward the real-time domain that is defined by high data access and/or transaction execution rates as well as stringent timing constraints. These real-time characteristics require databases to have special capabilities to handle time-critical transactions. Some examples are databases for safety-critical applications like air traffic control, avionics systems, process control, and medical service systems.