13163414
Process-Embedded Data Integrity
Despite the established theory and the history of the practical use of integrity rules, data quality problems, which should be solvable using data integrity rules, persist in organizations. One effective mechanism to solve this problem is to embed data integrity in a continuous data quality improvement process. The result is an iterative data quality improvement process as data integrity rules are defined, violations of these rules are measured and analyzed, and then the rules are redefined to reflect the dynamic and global context of business process changes. Using action research, we study a global manufacturing company that applied these ideas for improving data quality as it built a global data warehouse. This research merges data integrity theory with management theories about quality improvement using a data quality lens, and it demonstrates the usefulness of the combined theory for data quality improvement.
12254873
Process Knowledge and Data Quality Outcomes
What modes and domains of knowledge about data production processes are most critical for producing high-quality data? This study provides an answer to this question. Data are collected via questionnaire and analyzed using linear regression. The results show some similarities and differences in which knowledge variables are significant for various data quality dimensions. Three results are of particular interest to data quality managers and researchers. The first is the complexity and mix of knowledge associated with producing accurate data. The second is the significant results overall for knowledge about the data collection process as compared to data storage and utilization processes. The third is the negative associations of the knowing-why mode of knowledge as compared to the positive associations for knowing-what and knowing-how. Each of these results has managerial implications and generates avenues for further research.
207563187
Data quality assessment
How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
7927244
GM-WTA: An efficient workflow task allocation method in a distributed execution environment
A workflow is a collection of units of work called workflow tasks which cooperatively realize a business objective by utilizing system resources such as databases. The roles of workflow tasks are performed in the order driven by a computer representation of the workflow logic. During completing each task's role, the remote control transfers and the remote resource accesses may often occur in a distributed workflow system. Hence, the efficient distribution of workflow components, especially workflow tasks, is so effective as to improve the performance of workflow processing. If we can place adjacent workflow tasks as close as possible and locate workflow tasks near to the required resources, we can significantly reduce the overhead of workflow processing.In this paper, we propose an efficient workflow task allocation method in a distributed workflow system, which is based on the locality principle. The method that utilizes the concept of graph partitioning can improve the performanace of workflow processing by minimizing the remote processing costs incurred during workflow execution. In addition, we perform several experiments to evaluate our proposed method.
14456420
A replica control method for improving availability for read-only transactions
Data replication is often considered in distributed database systems to enhance availability and performance. The benefit of data replication, however can only be realized at the cost of maintaining the consistency of data. In particular, network partition failures make it more difficult to achieve high data availability while ensuring strong correctness criteria such as l-copy serializability. We propose a replica control method to improve the availability of data in the presence of network partition failures. Our method extends the traditional primary copy method by using the relaxed correctness criterion called insular consistency for large-scale distributed systems, where partition failures frequently occur. We focus on increasing the availability of data for read-only transactions. We introduce a version vector as a tool for guaranteeing insular consistency and present a mechanism that allows read-only transactions to be executed at any partition as long as the insular consistency is satisfied. An asynchronous update propagation mechanism is also employed to improve the performance of update operations. We also show that the proposed method is correct and give some performance considerations.
7659070
Statistical Properties and Adaptive Tuning of Support Vector Machines
In this paper we consider the statistical aspects of support vector machines (SVMs) in the classification context, and describe an approach to adaptively tuning the smoothing parameter(s) in the SVMs. The relation between the Bayes rule of classification and the SVMs is discussed, shedding light on why the SVMs work well. This relation also reveals that the misclassification rate of the SVMs is closely related to the generalized comparative Kullback-Leibler distance (GCKL) proposed in Wahba (1999, Scholkopf, Burges, & Smola (Eds.), Advances in Kernel Methods—Support Vector Learning. Cambridge, MA: MIT Press). The adaptive tuning is based on the generalized approximate cross validation (GACV), which is an easily computable proxy of the GCKL. The results are generalized to the unbalanced case where the fraction of members of the classes in the training set is different than that in the general population, and the costs of misclassification for the two kinds of errors are different. The main results in this paper have been obtained in several places elsewhere. Here we take the opportunity to organize them in one place and note how they fit together and reinforce one another. Mostly the work of the authors is reviewed.
6621245
Application of Machine Shop Data Model in Manufacturing Simulation
Simulation technology holds tremendous promise for the manufacturing industry. A machine shop data model has been developed at the National Institute of Standards and Technology (NIST) to represent neutral machine shop data for supporting machine shop manufacturing simulation [1]. This paper discusses the data model and its application in manufacturing simulation. The paper also proposes an application framework that integrates the simulation model, machine shop database, and machine shop XML (Extensible Markup Language) data document. A prototype system is developed to present the application of machine shop data model using the proposed framework.
509415
A New Class of Non-stationary Interpolatory Subdivision Schemes Based on Exponential Polynomials
We present a new class of non-stationary, interpolatory subdivision schemes that can exactly reconstruct parametric surfaces including exponential polynomials. The subdivision rules in our scheme are interpolatory and are obtained using the property of reproducing exponential polynomials which constitute a shift-invariant space. It enables our scheme to exactly reproduce rotational features in surfaces which have trigonometric polynomials in their parametric equations. And the mask of our scheme converges to that of the polynomial-based scheme, so that the analytical smoothness of our scheme can be inferred from the smoothness of the polynomial based scheme.