Database machines in support of very large databases

Software database management systems were developed in response to the needs of early data processing applications. Database machine research developed as a result of certain performance deficiencies of these software systems. This thesis discusses the history of database machines designed to improve the performance of database processing and focuses primarily on the Teradata DBC/1012, the only successfully marketed database machine that supports very large databases today. Also reviewed is the response of IBM to the performance needs of its database customers; this response has been in terms of improvements in both software and hardware support for database processing. In conclusion, an analysis is made of the future of database machines, in particular the DBC/1012, in light of recent IBM enhancements and its immense customer base

Towards A Comprehensive Cloud Decision Framework with Financial Viability Assessment

Most organizations moving their legacy systems to the cloud base their decisions on the naïve assumption that the public cloud provides cost savings. However, this is not always true. Sometimes the migration complexity of certain applications outweighs the benefits to be had from a public cloud. Moreover, the total cost of ownership does not necessarily decrease by moving to a public cloud. Therefore, there is a need for a disciplined approach for choosing the right cloud platform for application migration. In this paper, we propose a comprehensive cloud decision framework that includes an extensible decision criteria set, associated usage guidelines, a decision model for cloud platform recommendation, and a cost calculator to compute the total cost of ownership (TCO). The decision process works as follows. It begins with the ordering of relevant criteria, either according to industry best practice or the enterprise’s specific requirements and preferences. A technical recommendation is made on the basis of the criteria classification, which is then assessed for financial viability. By providing traceability of the cost items in the public/private TCO calculators to the decision criteria, the framework enables users to iterate through the decision process, determining and eliminating (if possible) the main cost drivers until a right balance is found between the desirable criteria and the available budget. We illustrate the need, benefits and value of our proposed framework through three different real-world use case scenarios

Business Intelligence in the Cloud?

Business Intelligence (BI) deals with integrated approaches to management support. In many cases, the integrated infrastructures that are subject to BI have become complex, costly, and inflexible. A possible remedy for these issues might arise on the horizon with “Cloud Computing” concepts that promise new options for a net based sourcing of hard- and software. Currently, there is still a dearth of concepts for defining, designing, and structuring a possible adaption of Cloud Computing to the domain of BI. This contribution combines results from the outsourcing and the BI literature and derives a framework for delineating “Cloud BI” approaches. This is the bases for the discussion of six possible scenarios – some of which within immediate reach today

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database management systems and spawned a host of new technologies, approaches, and platforms. The potential value of big data analytics is great and is clearly established by a growing number of studies. The keys to success with big data analytics include a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is used, big data can be considered to be a new, fourth generation of decision support data management. Though the business value from big data is great, especially for online companies like Google and Facebook, how it is being used is raising significant privacy concerns

Capacity requirement planning master data solution procurement at Qimonda Portugal SA

Estágio realizado na Qimonda Portugal S. A. e orientado pelo Eng.º Peter MaderaTese de mestrado integrado. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200

Revisiting Ralph Sprague’s Framework for Developing Decision Support Systems

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring

Digital cockpits and decision support systems : design of technics and tools to extract and process data from heterogeneous databases

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2006-200

A Strategic Roadmap for Maximizing Big Data Return

Big Data has turned out to be one of the popular expressions in IT the last couple of years. In the current digital period, according to the huge improvement occurring in the web and online world innovations, we are facing a gigantic volume of information. The size of data has expanded significantly with the appearance of today's innovation in numerous segments, for example, assembling, business, and science. Types of information have been changed from structured data-driven databases to data including documents, images, audio, video, and social media contents referred to as unstructured data or Big Data. Consequently, most of the organizations try to invest in the big data technology aiming to get value from their investment. However, the organizations face a challenge to determine their requirements and then the technology that suits their businesses. Different technologies are provided by variety of vendors, each of them can be used, and there is no methodology helping them for choosing and making a right decision. Therefore, the objective of this paper is to construct a roadmap for helping the organizations determine their needs and selecting a suitable technology and applying this conducted proposed roadmap practically on two companies

Justice Data Warehouse Assessment, Summary Report, February 13, 1998

This report is based on information provided from discussions with the Division of Criminal and Juvenile Justice Planning, members of the project's "Planning Group," and members of the Iowa Court Information System staff