High-end solid state disks (SSDs) provide much faster access to data compared to conventional hard disk drives. We present a technique for using solid-state storage as a caching layer between RAM and hard disks in database management systems. By caching data that is accessed frequently, disk I/O is reduced. For random I/O, the potential performance gains are particularly significant. Our system continuously monitors the disk access patterns to identify hot regions of the disk. Temperature statistics are maintained at the granularity of an extent, i.e., 32 pages, and are kept current through an aging mechanism. Unlike prior caching methods, once the SSD is populated with pages from warm regions cold pages are not admitted into the cache, leading to low levels of cache pollution. Simulations based on DB2 I/O traces, and a prototype implementation within DB2 both show substantial performance improvements.
SummaryThis paper deals with the analysis of spatial images taken from microscopically heterogeneous but macroscopically homogeneous microstructures. A new method is presented, which is strictly based on integral-geometric formulae such as Crofton's intersection formulae and Hadwiger's recursive definition of the Euler number. By means of this approach the quermassdensities can be expressed as the inner products of two vectors where the first vector carries thè integrated local knowledge' about the microstructure and the second vector depends on the lateral resolution of the image as well as the quadrature rules used in the discretization of the integral-geometric formulae. As an example of application we consider the analysis of spatial microtomographic images obtained from natural sandstones.
Solid state disks (SSDs) provide much faster random access to data compared to conventional hard disk drives. Therefore, the response time of a database engine could be improved by moving the objects that are frequently accessed in a random fashion to the SSD. Considering the price and limited storage capacity of solid state disks, the database administrator needs to determine which objects (tables, indexes, materialized views, etc.), if placed on the SSD, would most improve the performance of the system. In this paper we propose a tool called "Object Placement Advisor" for making a wise decision for the object placement problem. By collecting profile inputs from workload runs, the advisor utility provides a list of objects to be placed on the SSD by applying heuristics like the greedy knapsack technique or dynamic programming. To show that the proposed approach is effective in conventional database management systems, we have conducted experiments on IBM DB2 with queries and schemas based on the TPC-H and TPC-C benchmarks. The results indicate that using a relatively small amount of SSD storage, the response time of the system can be reduced significantly by considering the recommendation of the advisor.
Business process integration and monitoring provides an invaluable means for an enterprise to adapt to changing conditions. However, developing such applications using traditional methods is challenging because of the intrinsic complexity of integrating large-scale business processes and existing applications. Model Driven Developmente (MDDe) is an approach to developing applications-from domainspecific models to platform-sensitive models-that bridges the gap between business processes and information technology. We describe the MDD framework and methodology used to create the IBM Business Performance Management (BPM) solution. We describe how we apply model-driven techniques to BPM and present a scenario from a pilot project in which these techniques were applied. Technical details on models and transformation are presented. Our framework uses and extends the IBM business observation metamodel and introduces a data warehouse metamodel and other platform-specific and transformational models. We discuss our lessons learned and present the general guidelines for using MDD to develop enterprise-scale applications.
It is commonly agreed that accounts receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. Experience across multiple industries shows that effective management of AR and overall financial performance of firms are positively correlated. In this paper we address the problem of reducing outstanding receivables through improvements in the collections strategy. Specifically, we demonstrate how supervised learning can be used to build models for predicting the payment outcomes of newlycreated invoices, thus enabling customized collection actions tailored for each invoice or customer. Our models can predict with high accuracy if an invoice will be paid on time or not and can provide estimates of the magnitude of the delay. We illustrate our techniques in the context of real-world transaction data from multiple firms. Finally, simulation results show that our approach can reduce collection time up to a factor of four compared to a baseline that is not model-driven.
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