Production Process Data Research Articles

Interdisciplinary oceanographic observations: the wave of the future

SUMMARY: Oceanographic measurements, though difficult and expensive, are essential for effective study, stewardship, preservation, and management of our oceanic and atmospheric systems. Ocean sciences have been driven by technologies enabling new observations, discoveries, and modelling of diverse interdisciplinary phenomena. Despite rapid advances in ocean sampling capabilities, the numbers of disciplinary variables that are necessary to solve oceanographic problems are large and increasing. In addition, the time and space scales of key processes span over ten orders of magnitude; presently, there remain major spectral gaps in our sampling. Thus, undersampling presents the main limitation to our understanding of global climate change; variability in fish biomass and regime shifts; and episodic and extreme events. Fortunately, recent advances in ocean platforms and in situ autonomous sampling systems and satellite sensors are enabling unprecedented rates of data acquisition as well as the expansion of temporal and spatial coverage. Consequently, improved sampling strategies will lead to a reduction in ocean forecasting error for predictions of a multitude of atmospheric and oceanic processes. Nonetheless, major challenges remain to massively increase the variety and quantity of ocean measurements and to effectively coordinate, synthesize, and distribute oceanographic data sets. In particular, numbers of measurements are limited by the costs of instruments and their deployment as well as data processing and production of useful data products and visualizations. Looking forward, many novel and innovative technologies involving computing, nanotechnology, robotics, information and telemetry technologies, space sciences, and molecular biology are being developed at a fast pace for numerous applications (Kaku, 1997; Kurzweil, 1999). It is anticipated that several of these can and will be transitioned to the ocean sciences and will prove to be extremely beneficial for oceanographers in the next few decades. Already, autonomous, ‘robotic’ in situ sampling, high spectral resolution optical and chemical instrumentation, multi-frequency acoustics, and biomolecular techniques are being utilized by a limited number of oceanographers. Also, increased temporal and spatial sampling capabilities for expanding numbers of interdisciplinary variables are being accelerated thanks to both new technologies and utilization of data assimilation models coupled with autonomous sampling platforms. Data networks coupled with internet connectivity are rapidly increasing access to and utilization of data sets. In this essay, we review recent technological progress for solving some key oceanographic problems and highlight some of the foreseeable challenges and opportunities of ocean science technologies and their applications.

Modeling distributed GIS data production workflow

Real-time access to status of project progress, control of production processes and activities, and automation of production procedures are major hurdles in further improving production throughput and project management in distributed GIS data production projects. Workflow technology promises to address these issues by facilitating, automating or controlling GIS data production processes. The success of applying workflow technologies to distributed GIS data production requires comprehensive understanding of the underlying production processes. This paper presents the first research effort on rigorously modeling distributed GIS data production workflows, with particular attention to a GIS data quality control process using the Internet-based workflow management technologies. Existing practices of distributed GIS data production projects are used as the essential sources for modeling the required workflow. The paper discusses some issues faced and concepts such as workflow process repository and GIS data QC testing program repository.

The Naturally-Occuring Chat Machine

The Naturally-Occuring Chat Machine

Ex post and ex ante analysis of provisional data

In this paper we suggest a framework to assess the degree of reliability of provisional estimates as forecasts of final data, and we re-examine the question of the most appropriate way in which available data should be used for ex ante forecasting in the presence of a data-revision process. Various desirable properties for provisional data are suggested, as well as procedures for testing them, taking into account the possible non-stationarity of economic variables. For illustration, the methodology is applied to assess the quality of the US M1 data production process and to derive a conditional model whose performance in forecasting is then tested against other alternatives based on simple transformations of provisional data or of past final data. Copyright © 1999 John Wiley & Sons, Ltd.

Social Change: Measurement and Theory*

SummarySocietal change, which takes a variety of directions and forms and in no way can be assimilated or reduced to a single dimension, is often accompanied by a perception of insufficient understanding and lack of control. There is a frustrated need for mastering complexity and instability, separating the voluntary from the involuntary, the intended from the unintended, opportunities from risks, getting to the real causes and dominating the uncertain implications of social change. Social change catches us unprepared and confused.In this context statistics are generally considered a fundamental instrument of knowledge, but also part of the problem! In the public debate and in the specialized literature, the ability to measure social phenomena through current statistics and indicators is increasingly questioned. Data‐it is claimed‐are lacking, particularly longitudinal data; their quality (accuracy, relevance, timeliness, comparability, etc.) should be improved; indicators do not provide early warning signals, policy performance evaluation, and a precise indication of outcomes. Statistics cannot be used as a reliable and timely basis for decision making by individuals, organizations, governments, and for understanding these decisions. In some cases, statistics have been accused of giving a misleading and false picture of reality: do we measure the real extent of social exclusion and unemployment? Do we fully capture the quality of life and the degradation of the environment? Mismeasurement has been deemed by some commentators as being responsible for the wrong focus in inflation and stabilization policies, science and technology, unemployment and poverty. The productivity paradox, the informal economy, failure to measure welfare and the quality of urban life are instances where statistics do not seem to provide complete and satisfactory answers to the demand for information and knowledge. Our paper illustrates how, quite independently of measurement techniques and data production processes, the inadequacy of the conceptual framework may explain mismeasurement in relation to complex (multidimensional) and dynamic social phenomena. It is then to social theories, explanations and interpretations that statisticians need to turn, in order to come to grips with the new challenges in social measurement.We will develop this thesis looking at a few cases where measurement issues can be connected to both theoretical and empirical difficulties. The statistical gap which reveals itself in the mismeasurement or difficult measurement of social phenomena is closely interconnected with the social science gap. Only close collaboration between statisticians and social scientists can bring about continuous advancement in social science and quality improvement in social statistics.

Ex post andex ante analysis of provisional data

In this paper we suggest a framework to assess the degree of reliability of provisional estimates as forecasts of final data, and we re-examine the question of the most appropriate way in which available data should be used for ex ante forecasting in the presence of a data-revision process. Various desirable properties for provisional data are suggested, as well as procedures for testing them, taking into account the possible non-stationarity of economic variables. For illustration, the methodology is applied to assess the quality of the US M1 data production process and to derive a conditional model whose performance in forecasting is then tested against other alternatives based on simple transformations of provisional data or of past final data. Copyright © 1999 John Wiley & Sons, Ltd.

Ex Post and Ex Ante Analysis of Provisional Data

In this paper we suggest a framework to assess the degree of reliability of provisional estimates as forecasts of final data, and we reexamine the question of the most appropriate way in which available data should be used for ex ante forecasting in the presence of a data revision process. Various desirable properties for provisional data are suggested, as well as procedures for testing them, taking into account the possible nonstationarity of economic variables. For illustration, the methodology is applied to assess the quality of the US M1 data production process and to derive a conditional model whose performance in forecasting is then tested against other alternatives based on simple transformations of provisional data or of past final data.

Extending theory for user-centered information services: Diagnosing and learning from error in complex statistical data

Utilization of complex statistical data has come at great cost to individual researchers, the information community, and to the national information infrastructure. Dissatisfaction with the traditional approach to information system design and information services provision, and, by implication, the theoretical bases on which these systems and services have been developed has led librarians and information scientists to propose that information is a user construct and therefore system designs should place greater emphasis on user-centered approaches. This article extends Dervin's and Morris's theoretical framework for designing effective information services by synthesizing and integrating theory and research derived from multiple approaches in the social and behavioral sciences. These theoretical frameworks are applied to develop general design strategies and principles for information systems and services that rely on complex statistical data. The focus of this article is on factors that contribute to error in the production of high quality scientific output and on failures of communication during the process of data production and data utilization. Such insights provide useful frameworks to design a distributed system of social cognition that will detect, diagnose, communicate, and learn from error. Strategies to design systems that support communicative competence and cognitive competence emphasize the utilization of information systems in a user-centered learning environment. This includes viewing cognition as a generative process and recognizing the continuing interdependence and active involvement of experts, novices, and technological gatekeepers. © 1997 John Wiley & Sons, Inc.

Mariner 9—Image processing and products

The purpose of this paper is to describe the system for the display, processing, and production of image-data products created to support the Mariner 9 Television Experiment. Of necessity, the system was large in order to respond to the needs of a large team of scientists with a broad scope of experimental objectives. The desire to generate processed data products as rapidly as possible to take advantage of adaptive planning during the mission, coupled with the complexities introduced by the nature of the vidicon camera, greatly increased the scale of the ground-image processing effort. This paper describes the systems that carried out the processes and delivered the products necessary for real-time and near-real-time analyses. References are made to the computer algorithms used for the, different levels of decalibration and analysis.