Distributed Window Concurrency Control for Distributed Database Systems

In recent years research in the area of distributed transaction management has been growing rapidly. While designing concurrency control components for such systems, the most common techniques have involved locking and timestamping and variations of these. In spite of their heavy use in the designing of distributed systems, each of the above techniques has its own specific disadvantages and overhead. Our primary goal is to describe an approach that has the advantages of each of these techniques and none of their disadvantages. In this paper, we describe the Distributed Window Request Order Linked List method and show how this technique embodies most of the advantages of the standard techniques yet at the same time does not introduce serious difficulties or extra overhead of its own. We include an experimental comparison to compare transaction response time for this new method to those of locking and timpstamping methods. Keywords: distributed, database, concurrency, ROLL. SECTION 1. In..

Rose Perrizo & Anne Hughes Interview, 1977

In this interview, Anne Hughes and Rose Perrizo discusses Irish Amerian family life in the West Central Minnesota. Mrs. Perrizo was born in 1903, her great-grandparents immigrated from Ireland. Both Anne and Rose were raised in the Irish American community of Clontarf, MN.https://digitalcommons.morris.umn.edu/irishamericans/1002/thumbnail.jp

A kernel-based semi-naïve Bayesian classifier using p-trees

Abstract 1 A novel semi-naive Bayesian classifier is introduced that is particularly suitable to data with many attributes. The naive Bayesian classifier is taken as a starting point and correlations are reduced through joining of highly correlated attributes. Our technique differs from related work in its use of kernel-functions that systematically include continuous attributes rather than relying on discretization as a preprocessing step. This retains distance information within the attribute domains and ensures that attributes are joined based on their correlation for the particular values of the test sample. We implement a kernel-based semi-naive Bayesian classifier using P-Trees and demonstrate that it generally outperforms the naive Bayesian classifier as well as a discrete semi-naïve Bayesian classifier