Interactive specification of data displays

On-line graphical language for computer data displa

Programming language trends : an empirical study

Predicting the evolution of software engineering technology trends is a dubious proposition. The recent evolution of software technology is a prime example; it is fast paced and affected by many factors, which are themselves driven by a wide range of sources. This dissertation is part of a long term project intended to analyze software engineering technology trends and how they evolve. Basically, the following questions will be answered: How to watch, predict, adapt to, and affect software engineering trends? In this dissertation, one field of software engineering, programming languages, will be discussed. After reviewing the history of a group of programming languages, it shows that two kinds of factors, intrinsic factors and extrinsic factors, could affect the evolution of a programming language. Intrinsic factors are the factors that can be used to describe the general desigu criteria of programming languages. Extrinsic factors are the factors that are not directly related to the general attributes of programming languages, but still can affect their evolution. In order to describe the relationship of these factors and how they affect programming language trends, these factors need to be quantified. A score has been assigued to each factor for every programming language. By collecting historical data, a data warehouse has been established, which stores the value of each factor for every programming language. The programming language trends are described and evaluated by using these data. Empirical research attempts to capture observed behaviors by empirical laws. In this dissertation, statistical methods are used to describe historical programming language trends and predict the evolution of the future trends. Several statistics models are constructed to describe the relationships among these factors. Canonical correlation is used to do the factor analysis. Multivariate multiple regression method has been used to construct the statistics models for programming language trends. After statistics models are constructed to describe the historical programming language trends, they are extended to do tentative prediction for future trends. The models are validated by comparing the predictive data and the actual data

MAMOS - A monitor system under IBSYS for the IBM 7090/7094

MAMOS - monitor system under IBM basic monitor for IBM 7090/7094 compute

Period change of massive binaries from combined photometric and spectroscopic data in Cygnus OB2

Context. Mass loss is an important property in evolution models of massive stars. As up to 90% of the massive stars have a visual or spectroscopic companion and many of them exhibit mass exchange, mass-loss rates can be acquired through the period study of massive binaries. Aims. Using our own photometric observations as well as archival data, we look for variations in orbital periods of seven massive eclipsing binary systems in the Cygnus OB2 association and estimate their mass-loss rates and stellar parameters. Methods. We use a Bayesian parameter estimation method to simultaneously fit the period and period change to all available data and a stellar modelling tool to model the binary parameters from photometric and radial-velocity data. Results. Four out of the seven selected binaries show non-zero period change values at two-sigma confidence level. We also report for the first time the eclipsing nature of a star MT059.Comment: 12 pages, 18 figures, accepted for publication in A&