Impliance: A Next Generation Information Management Appliance

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today's requirements and hardware capabilities, would it look anything like today's database systems?" In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data - unstructured as well as structured - in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Strategic Predictors of Successful Enterprise Systems Deployment

Purpose The delivered wisdom to date has enterprise system purchase and implementation as one of the most hazardous projects any organization can undertake. The aim was to reduce this risk by both theoretically and empirically finding those key predictors of a successful enterprise system deployment. Design/methodology/approach A representative sample of 60 firms drawn from the Fortune 1000 that had recently (1999-2000) adopted enterprise resource planning (ERP) systems was used to test a model of adoption performance with significant results. Findings Leadership (social learning theory), business process re-engineering (change the company not the technology) and acquisition strategy (buy, do not make) were found to be significant predictors of adoption performance (final model R 2=43 percent, F=5.5, pp Originality/value The “four factor” model we validate is a robust predictor of ERP adoption success and can be used by any organization to audit plans and progress for this undertaking

Strategic Predictors of Successful Enterprise Systems Deployment

Purpose The delivered wisdom to date has enterprise system purchase and implementation as one of the most hazardous projects any organization can undertake. The aim was to reduce this risk by both theoretically and empirically finding those key predictors of a successful enterprise system deployment. Design/methodology/approach A representative sample of 60 firms drawn from the Fortune 1000 that had recently (1999-2000) adopted enterprise resource planning (ERP) systems was used to test a model of adoption performance with significant results. Findings Leadership (social learning theory), business process re-engineering (change the company not the technology) and acquisition strategy (buy, do not make) were found to be significant predictors of adoption performance (final model R 2=43 percent, F=5.5, pp Originality/value The “four factor” model we validate is a robust predictor of ERP adoption success and can be used by any organization to audit plans and progress for this undertaking

High Growth Firms in Scotland

High growth firms (HGFs) are widely thought to be a key force driving economic growth in modern advanced economies (Acs et al, 2008; BERR, 2008; Henrekson and Johansson, 2010). One of the central aims of the current economic strategy of the Scottish Government is to provide responsive and focused enterprise support to increase the number of highly successful, competitive businesses (Scottish Government, 2007). Hence, for the past decade there have been a number of policy initiatives designed to stimulate high growth entrepreneurship in Scotland. Many of these policies have had a strong technology focus. Given the importance these firms have for a region’s economic growth potential and the policy attention they are beginning to receive, it was felt to be important that Scottish Enterprise develops a deeper understanding of these important generators of wealth creation in the Scottish economy. This report examines HGFs in Scotland from both quantitative and qualitative perspectives

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page