591216
The HS File: A New Dynamic Signature File Method for Efficient Information Retrieval
Many works on the signature file methods have been made in the past, but they are mainly for static environments. However, many recent applications in practice require a dynamic information storage structure that effectively supports insertions, deletions and updates. Though there are a few signature file techiniques for dynamic environments, they suffer from serious performance degradation when query signature weights are light. In this paper, we propose a new dynamic signature file organization, called the hierarchical signature(HS) file, that solves the problem of light query signature weights. We perform simulation experiments by using wide range of parameter values. We show through performance comparison based on experiments that the HS file improves performance significantly in both the retrieval time and the storage over-head over the other dynamic signature file methods proposed earlier.
37385528
A framework for handling dependencies among web services transactions
This paper proposes an effective Web services (WS) transaction management framework to automatically manage inconsistencies occurred by relaxing isolation of WS transactions.
850911
Usability-based caching of query results in OLAP systems
In this paper we propose a new cache management scheme for online analytical processing (OLAP) systems based on the usability of query results in rewriting and processing other queries. For effective admission and replacement of OLAP query results, we consider the benefit of query results not only for recently issued queries but for the expected future queries of a current query. We exploit semantic relationships between successive queries in an OLAP session, which are derived from the interactive and navigational nature of OLAP query workloads, in order to classify and predict subsequent future queries. We present a method for estimating the usability of query results for the representative future queries using a probability model for them. Experimental evaluation shows that our caching scheme using the past and future usability of query results can reduce the cost of processing OLAP query workloads effectively only with a small cache size and outperforms the previous caching strategies for OLAP systems.
8831551
A per-object based hybrid concurrency control
Existing concurrency control algorithms do not conform well to various environments in the performance perspective. Each algorithm has some assumption on the conflict characteristic of its execution environment, and its performance degrades when the assumption fails. A per-object hybrid scheme integrating a two phase locking algorithm with a parallel validation technique is presented to solve this problem. Read and write accesses on each database object are controlled by one algorithm at a time. This controlling algorithm can alter while transactions are running, in order to enhance the overall system performance. In the simulation study, the proposed scheme with the proposed algorithm alteration strategy is shown to conform well to various execution environments, better than both the two phase locking algorithm and the parallel validation technique.
10552627
An efficient parallel join algorithm based on hypercube-partitioning
Many parallel join algorithms have been proposed so far but most of which are developed focused on minimizing the disk I/O and CPU costs. The communication cost, however, is also an important factor that can significantly affect the join processing performance in multiprocessor systems. We propose an efficient parallel join algorithm, called Cube-Robust, for hypercube multicomputers. The proposed algorithm is developed based on the observation that the size ratio of two relations to be joined is the dominant factor in the communication cost. We develop the analytic cost model for the proposed join algorithm. The performance comparisons show that the Cube-Robust join algorithm works better than others proposed earlier in a wide range of size ratios.<<ETX>>
13362734
FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting
Abstract The aim of this paper is to present a novel subspace clustering method named FINDIT. Clustering is the process of finding interesting patterns residing in the dataset by grouping similar data objects from dissimilar ones based on their dimensional values. Subspace clustering is a new area of clustering which achieves the clustering goal in high dimension by allowing clusters to be formed with their own correlated dimensions. In subspace clustering, selecting correct dimensions is very important because the distance between points is easily changed according to the selected dimensions. However, to select dimensions correctly is difficult, because data grouping and dimension selecting should be performed simultaneously. FINDIT determines the correlated dimensions for each cluster based on two key ideas: dimension-oriented distance measure which fully utilizes dimensional difference information, and dimension voting policy which determines important dimensions in a probabilistic way based on V nearest neighbors' information. Through various experiments on synthetic data, FINDIT is shown to be very successful in the high dimensional clustering problem. FINDIT satisfies most requirements for good clustering methods such as accuracy of results, robustness to the noise and the cluster density, and scalability to the dataset size and the dimensionality. Moreover, it is gracefully scalable to full dimension without any modification to algorithm.
2543653
Extracting and Learning an Unknown Grammar with Recurrent Neural Networks
Simple second-order recurrent networks are shown to readily learn small known regular grammars when trained with positive and negative strings examples. We show that similar methods are appropriate for learning unknown grammars from examples of their strings. The training algorithm is an incremental real-time, recurrent learning (RTRL) method that computes the complete gradient and updates the weights at the end of each string. After or during training, a dynamic clustering algorithm extracts the production rules that the neural network has learned. The methods are illustrated by extracting rules from unknown deterministic regular grammars. For many cases the extracted grammar outperforms the neural net from which it was extracted in correctly classifying unseen strings.
39095893
Comparison of hierarchical and marginal likelihood estimators for binary outcomes
Abstract Likelihood estimation in random-effect models is often complicated because the marginal likelihood involves an analytically intractable integral. Numerical integration such as Gauss–Hermite quadrature is an option, but is generally not recommended when the dimensionality of the integral is high. An alternative is the use of hierarchical likelihood, which avoids such burdensome numerical integration. These two approaches for fitting binary data are compared and the advantages of using the hierarchical likelihood are discussed. Random-effect models for binary outcomes and for bivariate binary-continuous outcomes are considered.
10452928
Detecting truncation point in steady-state simulation using chaos theory
In the steady-state simulation, it is important to identify initialization bias for the correct estimates of the simulation model under study. In this paper, the methods from chaos theory are applied to the determination of truncation points in the simulation data for controlling the initial bias. Two methods are proposed and evaluated based on their effectiveness for estimating the average waiting time in M/M/1(/spl infin/) queueing model.
8642618
Stationary subdivision schemes reproducing polynomials
A new class of subdivision schemes is presented. Each scheme in this class is a quasi-interpolatory scheme with a tension parameter, which reproduces polynomials up to a certain degree. We find that these schemes extend and unify not only the well-known Deslauriers-Dubuc interpolatory schemes but the quadratic and cubic B-spline schemes. This paper analyzes their convergence, smoothness and accuracy. It is proved that the proposed schemes provide at least the same or better smoothness and accuracy than the aforementioned schemes, when all the schemes are based on the same polynomial space. We also observe with some numerical examples that, by choosing an appropriate tension parameter, our new scheme can remove undesirable artifacts which usually appear in interpolatory schemes with irregularly distributed control points.
10094158
System and software designs for transaction processing in asymmetric communication environments
Observing that it is impractical to use traditional methods to control concurrency in broadcast-based asymmetric communication environments, we introduce a concurrency control protocol designed for broadcast-based transaction processing called Server Timestamp and Update Broadcast Supported Concurrency (STUBcast). STUBcast supports two new correctness criteria proposed in this paper—Single Serializability and Local Serializability. These criteria are weaker than global serializability, but are practical and easier to achieve in broadcast environments. This article also discusses the software design for the simulation program and the protocol implementation. Simulation results collected using our simulation program suggest that STUBcast could be very efficient in realistic application environments. Copyright © 2003 John Wiley & Sons, Ltd.