Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. Due to technological and market uncertainties, these projects involve the management of a large number of changes. The Callisto project was born out of realization that the classical approaches to project management do not provide sufficient functionally to manage large engineering projects. Callisto was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another. In the first phase of the project, rule-based prototypes were used to build quick prototypes of project management expertise and the project management knowledge required to support expert project managers.
Introduction In the following two subsections, we present a brief discussion of the project management problem and how the Callisto project began. The Project Management Problem Innovation is important to the continued vitality of industry. New products and changes in existing products are occurring at an increasing rate, causing product lives to decrease. In order to maintain market share, companies are forced to reduce product development time and bring their products to the market as early as possible. A major portion of development involves performing and managing many activities. For example, in hightechnology industries such as the computer industry, thousands of activities must be performed to design and build the prototype of a new product. Poor performance or management of an activity can result in critical delays. If product development time is to be reduced, better management and technical support are crucial. The Callisto project was started at the initiative of Digital ...
Stunning images taken by new alien-hunting telescopes in Chile reveal the raw beauty of our skies. The four telescopes are looking for habitable planets around nearby ultra-cool stars from the European Southern Observatory's Paranal Observatory in northern Chile. After finishing this commissioning phase at the end of this month, this impressive array of super-powerful planet-hunting telescopes will begin scientific operations. Stunning images taken by new alien-hunting telescopes in Chile reveal the raw beauty of our skies. The four telescopes are looking for habitable planets around nearby ultra-cool stars from the European Southern Observatory's Paranal Observatory in northern Chile.
How does the typical data science project life-cycle look like? This post looks at practical aspects of implementing data science projects. It also assumes a certain level of maturity in big data (more on big data maturity models in the next post) and data science management within the organization. Therefore the life cycle presented here differs, sometimes significantly from purist definitions of'science' which emphasize the hypothesis-testing approach. In practice, the typical data science project life-cycle resembles more of an engineering view imposed due to constraints of resources (budget, data and skills availability) and time-to-market considerations.