An Exploratory Investigation on the Invasiveness of Environmental Modeling Frameworks

Environmental modeling frameworks provide an array of useful features that model developers can harness when implementing models. Each framework differs in how it provides features to a model developer via its Application Programming Interface (API). Environmental modelers harness framework features by calling and interfacing with the framework API. As modelers write model code, they make framework-specific function calls and use framework specific data types for achieving the functionality of the model. As a result of this development approach, model code becomes coupled with and dependent on a specific modeling framework. Coupling to a specific framework makes migration to other frameworks and reuse of the code outside the original framework more difficult. This complicates collaboration between model developers wishing to share model code that ma y have been developed in a variety of languages and frameworks. This paper provides initial results of an exploratory investigation on the invasiveness of environmental modeling frameworks. Invasiveness is defined as th e coupling between application (i.e., model) and framework code used to implement the model. By comparing the implementation of an environmental model across several modeling frameworks, we aim to better understand the consequences of framework design. How frameworks present functionality to modelers through APIs can lead to consequences with respect to model development, model maintenance, reuse of model code, and ultimately collaboration among model developers. By measuring framework invasiveness, we hope to provide environmental modeling framework developers and environmental modelers with valuable in formation to assist in future development efforts. Eight implementations (six framework-based) of Thornthwaite, a simple water balance model, were made in a variety of environmental modeling frameworks and languages. A set of software metrics were proposed and applied to measure invasiveness between model implementation code and framework code. The metrics produced a rank ordering of invasiveness for the framework-based implementations of Thornthwaite. We compared model invasiveness results with several popular software metrics including size in lines of code (LOC), cyclomatic complexity, and object oriented coupling. To investigate software quality implications of framework invasiveness we checked for relationships between the Chidamber and Kemerer (1994) object oriented software metrics and our framework invasiveness measures. For the six framework-based implementations of Thornthwaite we found a five-fold variation in code size (LOC). We observed up to a seven-fold variation in total cyclomatic complexity, and a two to three-fold variation in object oriented coupling. For the model implementations we found that total size, total complexity, and total coupling all had a significant positive correlation. The raw count version of our invasiveness measures correlated with application size (LOC), total cyclomatic complexity, total efferent coupling (fan out) and total afferent coupling (fan in). Large size, complexity, and high levels of coupling between units (classes, modules) in a software system are often cited in software engineering as causes of high maintenance costs due to poor understandability and flexibility of the code. This study provides initial results but further investigation is desired to evaluate the utility of our invasiveness measurement approach as well as the software quality implications of framework invasiveness

Environmental Modeling Framework Invasiveness: Analysis and Implications

Environmental modeling frameworks support scientific model development by providing an Application Programming Interface (API) which model developers use to implement models. This paper presents results of an investigation on the framework invasiveness of environmental modeling frameworks. Invasiveness is defined as the quantity of dependencies between model code and the modeling framework. This research investigates relationships between invasiveness and the quality of modeling code. Additionally, we investigate the relationship between invasiveness and two common framework designs (lightweight vs. heavyweight). Five metrics to measure framework invasiveness were proposed and applied to measure invasiveness between model and framework code of several implementations of Thornthwaite and the Precipitation-Runoff Modeling System (PRMS), two hydrological models. Framework invasiveness measurements were compared with existing software metrics including size (lines of code), cyclomatic complexity, and object-oriented coupling with generally positive correlations being found. We found that models with lower framework invasiveness tended to be smaller, less complex, and have lower coupling. In addition, the lightweight framework implementations of the Thornthwaite and PRMS models were less invasive than the heavyweight framework model implementations. Our initial results suggest that framework invasiveness is undesirable for model code quality and that lightweight frameworks may help reduce invasiveness

Clustering von großen hochdimensionalen und unsicheren Datensätzen in der Astronomie

Ein ständiges Wachstum der Datenmengen ist in vielen IT-affinen Bereichen gegeben. Wissenschaftliche und insbesondere astronomische Datensätze weisen komplexe Eigenschaften wie Unsicherheiten, eine hohen Anzahl an Dimensionen sowie die enorme Anzahl an Dateninstanzen auf. Beispielsweise besitzen astronomische Datensätze mehrere Millionen Dateninstanzen mit jeweils mehreren tausend Dimensionen, die sich durch die Anzahl unabhängiger Eigenschaften bzw. Komponenten widerspiegeln. Diese Größenordnungen bzgl. der Dimensionen und Datenmengen in Kombination mit Unsicherheiten zeigen, dass automatisierte Analysen der Datensätze in akzeptabler Analysezeit und damit akzeptabler Berechnungskomplexität notwendig sind. Mit Clustering Verfahren existiert eine mögliche Analysemethodik zur Untersuchung von Ähnlichkeiten innerhalb eines Datensatzes. Aktuelle Verfahren integrieren jedoch nur einzelne Aspekte der komplexen Datensätze im Verfahren, mit einer teilweise nicht-linearen Berechnungskomplexität im Hinblick auf eine steigende Anzahl an Dateninstanzen sowie Dimensionen. Diese Dissertation skizziert die einzelnen Herausforderungen der Prozessierung komplexer Daten in einem Clustering Verfahren. Darüber hinaus präsentiert die Arbeit einen neuartigen parametrisierbaren Ansatz zur Verarbeitung großer und komplexer Datensätze, genannt Fractal Similarity Measures, der die Datenmengen in log-linearer Analysezeit prozessiert. Durch das ebenfalls vorgestellte sogenannte unsichere Sortierungsverfahren für hochdimensionale Daten, stellt die dafür notwendigen Initialisierungsverfahren Gitter bereit. Mit Hilfe des neuen Konzepts des fraktalen Ähnlichkeitsmaßes bzw. dem fraktalen Informationswert analysiert das Verfahren die möglichen Cluster sowie die Dateninstanzen auf Ähnlichkeiten. Zur Demonstration der Funktionalität und Effizienz des Algorithmus evaluiert diese Arbeit das Verfahren mit Hilfe eines synthetischen und eines reellen Datensatzes aus der Astronomie. Die Prozessierung des reellen Datensatzes setzt eine Vergleichbarkeit der gegebenen Spektraldaten voraus, weshalb ein weiteres Verfahren zur Vorprozessierung von Spektraldaten auf Basis des Hadoop-Rahmenwerks vorgestellt wird. Die Dissertation stellt darüber hinaus Ergebnisse des Clustering-Vorgangs des reellen Datensatzes vor, die mit manuell erstellten Ergebnissen von Domänennexperten qualitativ vergleichbar sind

Social Influences on User Behavior in Group Information Repositories.

Group information repositories are systems for organizing and sharing files kept in a central location that all group members can access. These systems are often assumed to be tools for storage and control of files and their metadata, not tools for communication. The purpose of this research is to better understand user behavior in group information repositories, and to determine whether social factors might shape users' choices when labeling and organizing information. Through interviews with group information repository users and analysis of system log data, I found that users tend to restrict their activities in a repository to files they "own," are reluctant to delete files that could potentially be useful to others, dislike the clutter that results, and can become demotivated if no one views files they uploaded. I also conducted an online experiment in which participants labeled and organized short text files into a file-and-folder hierarchy, and later completed search tasks in the hierarchies created by others. Participants came from two intellectual communities, and were instructed to organize the files for one of three different audiences: themselves, someone from the same intellectual community, and someone from the other community. I found that when participants created hierarchies for an audience they imagined was like them, everyone searched more efficiently, regardless of whether they shared community membership with the hierarchy's creator. Further, analyses of the hierarchies showed that users performed better when file and folder labels were more similar to the text of the documents they represented. These results show that audience design, a communication process, can affect group information management tasks. The findings from both studies suggest that sharing files via a group information repository is more complicated than simply making them available on a server. Processes that affect spoken communication also impact word choices when the "interaction" is mediated by a repository. With this new knowledge, it is possible to begin design work on a new class of systems that go beyond mere storage, and better support the social aspects of user behavior in group information repositories.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64758/1/ejrader_1.pd