Web applications called mash-ups combine information of varying granularity from different, possibly disparate, sources. We describe Mash-o-matic, a utility that can extract, clean, and combine disparate information fragments, and automatically generate data for mash-ups and the mash-ups themselves. As an illustration, we generate a mash-up that displays a map of a university campus, and outline the potential benefits of using Mash-o-matic.Mash-o-matic exploits superimposed information (SI), which is new information and structure created in reference to fragments of existing information. Masho-matic is implemented using middleware called the Superimposed Pluggable Architecture for Contexts and Excerpts (SPARCE), and a query processor for SI and referenced information, both parts of our infrastructure to support SI management. We present a high-level description of the mash-up production process and discuss in detail how Mash-o-matic accelerates that process.

In our research on superimposed information management, we have developed applications where information elements in the superimposed layer serve to annotate, comment, restructure, and combine selections from one or more existing documents in the base layer. Base documents tend to be unstructured or semi-structured (HTML pages, Excel spreadsheets, and so on) with marks delimiting selections. Selections in the base layer can be programmatically accessed via marks to retrieve content and context. The applications we have built to date allow creation of new marks and new superimposed elements (that use marks), but they have been browse-oriented and tend to expose the line between superimposed and base layers. Here, we present a new access capability, called bilevel queries, that allows an application or user to query over both layers as a whole. Bi-level queries provide an alternative style of data integration where only relevant portions of a base document are mediated (not the whole document) and the superimposed layer can add information not present in the base layer. We discuss our framework for superimposed information management, an initial implementation of a bi-level query system with an XML Query interface, and suggest mechanisms to improve scalability and performance.

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IStatistical quality control techniques are useful in monitoring the process behaviour. Attribute tontrol charts are widely psed in process control. The selection of sample size, sampling interval and control ~idth of the control qhart is import~t in minimising the quality costs'. Control chart parameters, Ilike 30" cpntrollilnits and fixed fraction sampling at conveniently selected sampling intervals result in deplomble cost penaltie4 in quality' control. The best selection of tl¥:se pammeters OOpends on seveml pi"OCess pnrnmclers,! like frequency of occupancy of a shift in tOO process, cost of swnpling, c~t of investigation for tiOOfng assignable cause, probability of false alarms, penalty cost of OOfecti ves and pro~ss correction costs. ' \ I A general model has been developed to determine the'total quality cost as a function of these 1 parameters. Probability of not identifying a process shift (J3-risk) and probflbility of wrongly concludinlg thai the process got shifted (a-risk) are considered in OOveloping 100 model. This cost equation is op~mised to determine optimum values cl control chart parameters. Fibonacci sea~h is I used to quicken 100 analytical method for detennining optimum sampling size and control wid~ The li'roposals made by Duncan, Montgomery , Gibra and Chiu for determining the optimum control chart parameters are critically examined and compared widt 100 present model. Case studies were conducted in two foun~s.Optimum control chart parameters in casting of cylinOOr liners and cast plates are determined. It has been found dtat quaqty costs are considerably reduced by using optimally designed con~1 chart parafeters with'proposed method.

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