16958826
Recent Advances and Research Problems in Data Warehousing
In the recent years, the database community has witnessed the emergence of a new technology, namely data warehousing. A data warehouse is a global repository that stores pre-processed queries on data which resides in multiple, possibly heterogeneous, operational or legacy sources. The information stored in the data warehouse can be easily and efficiently accessed for making effective decisions. The On-Line Analytical Processing (OLAP) tools access data from the data warehouse for complex data analysis, such as multidimensional data analysis, and decision support activities. Current research has lead to new developments in all aspects of data warehousing, however, there are still a number of problems that need to be solved for making data warehousing effective. In this paper, we discuss recent developments in data warehouse modelling, view maintenance, and parallel query processing. A number of technical issues for exploratory research are presented and possible solutions are discussed.
29393244
Semantic Caching and Query Processing
Semantic caching is very attractive for use in distributed systems due to the reduced network traffic and the improved response time. It is particularly efficient for a mobile computing environment, where the bandwidth of wireless links is a major performance bottleneck. Previous work either does not provide a formal semantic caching model, or lacks efficient query processing strategies. This paper extends the existing research in three ways: formal definitions associated with semantic caching are presented, query processing strategies are investigated and, finally, the performance of the semantic cache model is examined through a detailed simulation study.
39362526
Main memory database recovery algorithms and their performance
In main memory systems recovery becomes more complex mainly due to the volatility of main memory and the elimination of the separation of data storage (disk) and data processing locations (main memory). In a disk based database system, it is the disk copy that represents the stable database restored after a system failure; but in MDBSs, the main memory copy of the database must be recovered. The paper discusses the recovery issues in MDBSs and presents a somewhat detailed performance study of two recovery algorithms, one using a shadow approach and the other update-in-place.<<ETX>>
35078097
An adaptive location management algorithm for mobile computing
In mobile computing mobile hosts or mobile units move randomly from one place to another within a well-defined geographical area. This requires some mechanism for identifying the exact location of a mobile host. Such mechanisms are usually referred to as location management and is one of the essential components for supporting mobility. In the past a number of efficient location management schemes have been suggested but they lack adaptability. We believe that an adaptable location management scheme is more desirable because of frequent change in the location of mobile hosts. In this paper, we report a location management algorithm, which adapts to the constant changing scenario. Our algorithm makes use of some of the concepts of location management schemes proposed in the past.
34436430
An Integrated Data Structure with Multiple Access Paths for Database Systems and its Performance
Abstract In the past a number of file organizations have been proposed for processing different types of queries efficiently. To our knowledge none of the existing file organizations is capable of supporting all types of accesses equally efficiently. In this paper we have taken a different approach for designing an integated data structure which offers multiple access paths for processing different types of queries efficiently. The data structure reported here can be implemented on disk based as well as main memory database systems, however, in this paper we report its behavior mainly in main memory database environment. Our approach is to fuse those data structures which offer an efficient access paths for a particular type. To show the feasibility of our scheme we fused the B+-tree, the grid file and extendible hashing structures, using a proper interface. We implemented and measured its performance through simulation modeling. Our results show that the data structure does improve concurrency and offers a higher throughput for a variety of transaction processing workloads. We argue that our scheme is different than creating secondary indexes for improving concurrency. In the absence of a data strucure which can provide all types of access equally efficiently, an integrated data structure is an acceptable solution which offers an efficient way for increasing the performance of database management systems.
42935672
Performance Measurement of Main Memory Database Recovery Algorithms Based on Update-in-Place and Shadow Approaches
The authors present a performance study of two main memory database recovery algorithms, one using a shadow approach and the other update-in-place. The results show that in main memory databases, shadow approach performs better than update-in-place. The authors introduce minor improvements to the shadow scheme and show that the modified algorithm does show further performance improvement. >
5947607
Enhancing data analysis with noise removal
Removing objects that are noisy is an important goal of data cleaning as noise hinders most types of data analysis. Most existing data cleaning methods focus on removing noise that is the product of low-level data errors that result from an imperfect data collection process, but data objects that are irrelevant or only weakly relevant can also significantly hinder data analysis. Thus, if the goal is to enhance the data analysis as much as possible, these objects should also be considered as noise, at least with respect to the underlying analysis. Consequently, there is a need for data cleaning techniques that remove both types of noise. Because data sets can contain large amounts of noise, these techniques also need to be able to discard a potentially large fraction of the data. This paper explores four techniques intended for noise removal to enhance data analysis in the presence of high noise levels. Three of these methods are based on traditional outlier detection techniques: distance-based, clustering-based, and an approach based on the local outlier factor (LOF) of an object. The other technique, which is a new method that we are proposing, is a hyperclique-based data cleaner (HCleaner). These techniques are evaluated in terms of their impact on the subsequent data analysis, specifically, clustering and association analysis. Our experimental results show that all of these methods can provide better clustering performance and higher quality association patterns as the amount of noise being removed increases, although HCleaner generally leads to better clustering performance and higher quality associations than the other three methods for binary data.
16866072
Metrics and Connections for Rigid-Body Kinematics
The set of rigid-body motions forms a Lie group called SE(3), the special Euclidean group in three dimensions. In this paper, we investigate possilble choices of Riemannian metrics and affine connections on SE(3) for applications to kinematic analysis and robot-trajectory planning. In the first part of the paper, we study metrics whose geodesics are screw motions. We prove that no Riemannian metrics call have such geodesics, and we show that the metrics whose geodesics are screw motions form a two-parameter family of semi-Riemannan metrics. In the second part of the paper, we investigate affine connections which through the covariant derivative give the correct expression for the acceleration of a rigid body. We prove that there is a unique symmetric connection with this property. Furthermore, we show that there is a family of Riemannian metrics that are compatible with such a connection. These metrics are products of the bi-invariant metric on the group of rotations and a positive-definite constant metric on the group of translations.
41766531
High Performance Data Mining
The current decade has seen an explosive growth in database technology and the amount of data collected. This has created an unprecedented opportunity for ”data mining,” which is a process of efficient supervised or unsupervised discovery of interesting information hidden in the data. The focus of this symposium is to bring together experts from the academia and the industry, including reseachers and practitioners, to discuss issues in this important area. Owing to the huge size of data and computation involved in data mining algorithms, parallel processing is often considered an essential component for a successful data mining solution - which will be a special emphasis of the symposium. Talks in this mini-symposium will cover both the current state-of the-practice as well as state-of-the-research in data mining. A highlight of the symposium is a panel discussion on the theory and practice of data mining.
206537669
Optimal control of systems with unilateral constraints
Problems in robotics and biomechanics such as trajectory planning or resolution of redundancy can be effectively solved using optimal control. Such systems are often subject to unilateral constraints. Examples include tasks involving contacts (e.g., walking, running, multifingered or multiarm manipulation), and other tasks that may not involve contacts but in which the system state or the inputs must satisfy inequality conditions (e.g., limits on actuator forces). This paper shows how problems of optimal control in robotics that involve unilateral constraints can be efficiently solved by first formulating the constrained optimal control problem as an unconstrained problem of the calculus of variations and then solving it using an integral formulation. This method has several advantages over the Pontryagin minimum principle which is traditionally employed to solve such problems. An example of two-arm manipulation with inequality constraints due to Coulomb friction is used to demonstrate the formulation of the problem and the algorithms.
10015251
Repartitioning of Adaptive Meshes: Experiments with Multilevel Diffusion
For a large class of irregular grid applications, the structure of the mesh changes from one phase of the computation to the next. Eventually, as the graph evolves, the adapted mesh has to be repartitioned to ensure good load balance. If this new graph is partitioned from scratch, it will lead to an excessive migration of data among processors. In this paper, we present a new scheme for computing repartitionings of adaptively refined meshes. This scheme performs diffusion of vertices in a multilevel framework and minimizes vertex movement without significantly compromising the edge-cut.