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.
26557344
A Knowledge-Based Approach to Rank Documents for Boolean Queries
17546848
Analysis of Fuzzy Operators for High Quality Information Retrieval
Kim, M.H., J.H. Lee and Y.J. Lee, Analysis of fuzzy operators for high quality information retrieval. Information Processing Letters 46 (1993) 251-256. It has been argued that the conventional fuzzy set model based on the MIN and MAX operators is not appropriate for a model of information retrieval systems due to the properties of the MIN and MAX operators adverse to retrieval effectiveness. In this paper we investigate the behavioral aspects of various fuzzy operators and address the properties which are important in achieving high quality document rankings. We then present the fuzzy set model based on positively compensatory operators, which provides higher retrieval effectiveness than the conventional fuzzy set model. It is also described that the behavior of the proposed fuzzy set model almost coincides with that of the extended boolean model.
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.
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.
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.
33130698
Voltage-clock scaling for low energy consumption in real-time embedded systems
Low power and energy consumption will always be an essential requirement in many real-time embedded applications. Voltage scaling is a relatively novel approach to reducing energy consumption. The idea is that a processor can be run either at high or at low voltage: at high voltage, the clock rate is high but so is the power consumption; at low voltage the clock rate is lower, but the power consumption drops by a greater factor. This immediately suggests a powerful approach to lowering energy consumption in real-time systems. In this paper we introduce static and dynamic algorithms to control processor voltage to reduce energy consumption. We demonstrate by simulation that our algorithms can significantly reduce total energy consumption.
8597522
Optimal resource control in periodic real-time environments
Three factors determine the optimum configuration of a multiprocessor at any epoch: the workload, the reward structure, and the state of the computer system. An algorithm is presented for the optimal (more realistically, quasi-optimal) configuration of such systems used in real-time applications with periodic reward rates and workloads. The algorithm is based on Markov decision theory. It is suggested that a change in the workload or the reward structure should be as powerful a motivation for reconfiguration as component failure. Such changes occur naturally over the course of operation: an example of an online transaction processing system with a workload and reward structure that has a period of a day is given.<<ETX>>
9193373
Pfair scheduling of periodic tasks with allocation constraints on multiple processors
Summary form only given. Pfair scheduling for periodic tasks on multiple processors in a time slotted environment can not only guarantee task deadlines to be respected, but also make tasks execute at steady progressive rates. We consider pfair schedulability of periodic tasks with allocation constraints in the sense that some tasks can only be assigned to one or more specified processors. The contributions of the paper lie in the following four aspects. Firstly, we prove that there exists pfair schedule in the case that fixed tasks are assigned to disjoined processor subsets. Secondly, we give a sufficient and necessary condition of the existence of pfair schedule for periodic tasks with arbitrary allocation constraints. Thirdly, we show that pfair schedulability test of periodic tasks with arbitrary allocation constraints can be done in polynomial time. Finally, an online approximate pfair scheduling algorithm called HPA (hierarchical pfair algorithm) is proposed for scheduling fixed and migrating tasks. With HPA although idea a fixed task is not guaranteed to respect its deadline completely, the amount of time by which it misses its deadline is bounded.
5961676
Total Data Quality Management: The Case of IRI
Abstract Implementing a Total Data Quality Management (TDQM) program is not a trivial undertaking. Two key steps are (1) to clearly define what an organization means by data quality and (2) to develop metrics that measure data quality dimensions and that are linked to the organization’s goals and objectives. This paper presents the case of Information Resources, Inc., which exemplifies how a company can develop a viable TDQM program.
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.