CHAPTER 3

Sustainable Knowledge and Resources Management for Environmental Information and Computation

Claus-Peter Rückemann

Introduction

Motivation and Background

The main base for analysis and evaluation in natural sciences and environmental information is reliable, reproducible, and comparable data. The premise of any scientific procedure is that processes must be measured in order to be discussed, evaluated, and improved. Time ranges of natural and environmental processes, which mankind is aware of, span from ultrashort intervals to billions of years. With environmental processes naturally many different processes are overlaid, with different cycles.

For many problems in practical environmental research this results in time intervals of at least many decades or centuries in order to come up with suggestions or reasonable and reliable predictions. In most cases the time interval of the predictions can only span a fraction of the time the long-term data gathering itself affords. Anyhow, up to now there is no general concept with pure isolated applications available to achieve the goal of real long-term persistent knowledge.

All in all, the data and knowledge used as well as resulting from the processes and documentation should be persistently available. This means, gathering and preservation of long-term reusable data and information are among the most important tasks in the scientific workflow. This chapter shows a new way on how structure, classification, and standardized methods can be used and integrated in order to enable long-term sustainable knowledge resources. It presents results from case studies with knowledge resources, computation, and integration. The goal and focus of this research are most complex: Long-term multidisciplinary knowledge integration and application. Therefore, the implicated theoretical frameworks and methodological backgrounds require the consideration of the greatest levels of flexibility. The chapter presents the benefits and challenges of the implemented frameworks and system components. On behalf of these facts all the required components used for the implementations described in this article have been very carefully chosen and described as flexible as necessary and as specific as possible.

Objectives: Knowledge Resources, Computation, and Integration

For the last decades, long-term knowledge resources and concepts have been created in long-term initiatives for supporting discovery and reuse of knowledge and research information. The processes include structuring, classification, and computational access. In an ongoing process knowledge objects, for example, research results, are included in the resources. The content and context creation processes are extended over an unlimited period of time. The available architecture enables to methodologically integrate and kind of material as autonomous data, references, or knowledge objects. Structure, universal classification, and references are basic features that can be exploited. The more, workflows based on the knowledge can be created for any purpose from statistics to complex knowledge discovery. As gathering of information especially in research and education is not time limited, this has not been created as a project itself. Nevertheless case studies and developments can be considered time-limited projects.

Compared to the complexity of the real world processes, the state-of-the-art in computation, namely processing, simulation, and modeling of cases and processes is very much restricted to special and simplified scenarios. Nevertheless, the computational demands are huge. This results in computing resources and architectures, which have to be suited for the needs of these purposes. The High End Computing (HEC) is an industry. Computing resources are a valuable tool and besides in every case the architectures and requirements must be planned over years, their usage must be financed before starting this phase. In addition, any of these resources known must be used and operated economically. As there is currently no real alternative to the market-driven HEC this inherently results in several substantial drawbacks when discussing long-term initiatives. Therefore, the major objectives resulting from this background are the sustainable creation of knowledge resources and resources management especially considering multidisciplinary environmental information, computation, and integration.

This chapter is further outlined as follows: Section II introduces the topic and its background with an introduction to the state-of-the-art resources management for environmental information and computation and points to the basic principles, methodologies, and available components and standards. Section III introduces the theoretical framework and the research objectives and argumentation. Section IV discusses the research methodology including the systematics and methodology required. The core components and a prominent application are presented regarding information, computation, and integration at the case of multidisciplinary result matrices. In this section the results for long-term structure, classification, and processing for any discipline are discussed including contributions from academic research and industry. Section V provides the findings and discussion of results and evaluation, especially considering the resulting practical multidisciplinary classification for environment and climatology and the managerial and practical implications. Section VI summarizes the main results and achievements in a comprehensive conclusion.

Resources Management for Environmental Information and Computation

Some of the most challenging issues with knowledge resources are long-term vitality, universal multidisciplinary documentation, and component integration. These also include scientific data, results, and context on environment, climatology, and any associated context from research and society. Computing as well as storage are only tools and components. Therefore, disciplines using methods, systematics, standards, and tools in a way coping best with the challenges can most probably contribute to achievements relevant for climate change, cognostics, and solutions.

Further data being publicly available can be incorporated in any way under the premise that the data formats are accessible and interfaces have been provided. An example is the CLImatological database for the World’s Oceans (CLIWOC 2017). Further computational examples are wildfire control (Yin, Shaw, Wang, Carr, Berry, Gross, and Comiskey 2011), more or less isolated archaeological records (The Digital Archaeological Record 2017) and traditional collections of historical (World Digital Library 2017) and cultural information (European Cultural Heritage Online 2017). Despite any challenges, the long-term information should be accessible with scientific supercomputing resources in order to create advanced information systems and implement and improve workflows and recommended operation (Wissenschaftsrat 2011) and integration of knowledge (di Maio 2012).

The knowledge resources being part of the LX Foundation Scientific Resources (LX-Project 2017) document the references and the participated organizational bodies and projects in knowledge and resources management for environmental information and computation. These resources can integrate environmental and climatological information with any other knowledge as, for example, environmental sciences (ESSA 1968), geoscientific terms (Bates and Jackson 1980), and environmental terms and methods (Matschullat and Müller 1994; O’Riordan 1996).

Media citations can combine geoscientific and historical information and refer to 3D video animations and dioramic reconstructions as well as even to postcards, for example, linking the Volcanic Explosivity Index (VEI) (Newhall and Self 1982) resolving references to various realia (My 2007; Guardasole 2013; Bonaventura 2007).

The integration of environmental knowledge refers to many facets like turbulent diffusion in the environment (Csanady 1973), environmental chemistry and anthropogenic compounds (Hutzinger 1982), environmental information and documentation systems (Umweltbundesamt 1992), carbon dioxide (Wesoky 1997), but also on secondary context like satellites and their environments (Johnson 1965).

Knowledge resources can integrate natural sciences information with international initiatives and societal and industrial activities, which can be used for a multitude of purposes. Looking for the context of sustainable knowledge and governance aspects on environmental and climatological issues the views are naturally multidisciplinary and multinational as the following context documented within the resources shows.

The United Nations Environment Program-Global Resource Information Database (UNEP-GRID) (UNEP-GRID 2017) is collecting information on global resources.

Partnerships in Environmental Management for the Seas of East Asia (PEMSEA) (PEMSEA 2017), formerly the Sustainable Development Strategy of the Seas of East Asia (SDS-SEA), is especially focusing on governance of human activities, including their Integrated Coastal Management (ICM) activities.

The Australian Ecological Knowledge and Observation System (AEKOS) (AEKOS 2017) has done excellent basic work with the framework for Submission, Harmonization and Retrieval of Ecological Data (SHaRED) (SHaRED 2017) and the Terrestrial Ecosystem Research Network (TERN 2017).

National and international bodies are promoting environmental research and management, for example, the European Environment Agency (EEA 2017) of the European Union, the Space Environment Information System (SPENVIS) of the European Space Agency (ESA) (SPENVIS 2017), the Environmental Information System (ENVIS) (ENVIS 2017) in India, and the U.S. Environmental Protection Agency (EPA 2017). Many of them are considering universal components and widely used standards as with the Global Earth Observation System of Systems (GEOSS) (GEOSS 2017), the Infrastructure for Spatial Information in the European Community (INSPIRE), and Copernicus, The European Earth Observation Program (Copernicus 2017), formerly the Global Monitoring for the Environment and Security (GMES) (GMES 2017). With components for environmental management systems most implementations refer to the ISO 14000 on Environmental management of the International Organization for Standardization (ISO 2017).

Environmental Management and ISO Standards

The International Organization for Standardization (ISO) has published a sequence of standards on environmental management. Besides environmental management, the ISO 14000 series standards (ISO 2017) contain standard recommendation for assessment, evaluation, life cycle analysis, communication, and auditing. ISO 14000 refers to a family of voluntarily used standards and guidance documents with the ISO 14001 being the most widely used environmental management system standard worldwide.

These standards are recommended to be used internationally for Environmental Management and System (EMS) components, for comprehensive, systematic, planned, and documented implementation, including the organizational structures and resources for the implementation. Anyhow, for creating sustainable environmental knowledge resources and applications there are basic requirements for structure and consequent and consistent universal classification, which the ISO series is not supporting at the present stage. All the ISO standards are periodically reviewed by the ISO to ensure that they meet the requirements.

Present EMS is missing facilities for using a universal classification as well as an extended knowledge resources support. A major reason is that the ISO 14000 series standards are still missing to incorporate a universal long-term and multidisciplinary classification.

Besides focusing on the market requirements, trade and production processes, and information on performance improvements for internal and external stakeholders, the EMS components and ISO standards should be essentially reviewed for fostering the creation of sustainable knowledge resources and components by enabling the use of an international universal classification in this context.

Basic Principles and Methodology with ISO 14001

Research Objectives and Argumentation

Environment and climate on earth has continuously changed from the beginning of the planet on. This has always been associated with most complex processes. Climate change can neither be described from a mono-disciplinary point of view nor investigated in an isolated way. Sentimental aspects as well as statements of interest groups are very common. Singular natural events and man-made factors are only a small part of the participated facets.

For the foreseeable future, simulation of scenarios and climate modeling will only be a very limited attempt to face real environment and natural sciences and societies’ coherent context. The more multidisciplinary creation of knowledge has to rely on long-term sustainable resources, the more knowledge resources are in the focus. The conceptional framework is therefore dominated by conceptual knowledge and documentation, in these examples concentrating on Universal Decimal Classification (UDC).

A suitable collaboration framework has to consider the long-term multidisciplinary work on the knowledge, all aspects of use and extension of resources, and the operation of required resources. Therefore, the basic components of the multidisciplinary work are concentrating on knowledge resources, computing and storage resource, and information systems.

The knowledge resources are the most important component as they carry the long-term knowledge and investments. They represent the sustainable and long-term goals. The issues with computing and storage are becoming increasingly important with the high-end facilities for handling big data, enabling complex discovery workflows, and integrating processing, simulation, and modeling. With the high-end resources at hand and a sustainable operation it becomes feasible to create complex Integrated Information and Computing Systems (IICS).

For the objectives on improving the insights on environmental processes, climatology, geophysics, physics, geology, economy, and other associated disciplines, we need a wealth of features from the major means of documentation, information gathering, and computation.

Based on sustainable knowledge resources knowledge can be preserved and developed over decades, centuries, and presumably longer. It can integrate references to realia objects from any geological, prehistorical, and historical epoch as well as their documentation on objects, scientific and social background, and their history.

Structure, Classification, and Processing for Any Discipline

For an efficient and effective processing the knowledge data require a flexible structure and a universal systematic classification. Any knowledge resources documenting complex multidisciplinary reality for discovery applications require features for exact documentation on the one hand and they require soft criteria on the other hand.

The UDC is a classification complying with the classification criteria. Together with the content, which may deliver more detail or differing perspectives, UDC provides a universal view on the classified objects. When requiring faceted classification for multidisciplinary knowledge the universal UDC cannot be ignored as it is the most comprehensive and flexible means available and supported. With the knowledge resources in this research handling 70,000 classes, for 100,000 objects and several million referenced data the algorithms are mostly nonlinear. They allow interactive use, dynamical communication, computing, decision support, and pre- and postprocessing, for example, visualization. The basic information and classification is available in various summaries, translations, and exports (Universal Decimal Classification Consortium [UDCC] 2017; Universal Decimal Classification Summary [UDCS] 2017; Universal Decimal Classification Summary [UDCS] Translations 2017; Universal Decimal Classification Summary [UDCS] Translations: German 2017; Universal Decimal Classification Summary [UDCS] Exports 2017; Universal Decimal Classification Summary [UDCS] Linked Data 2017).

Based on this, the classification deployed for documentation (Rückemann 2012) is able to document any object with any relation, structure, and level of detail as well as intelligently selected nearby hits and references. Objects include any media, textual documents, illustrations, photos, maps, videos, sound recordings, as well as realia, physical objects such as museum objects. UDC is a suitable background classification, for example, the objects use preliminary classifications for multidisciplinary content. Standardized operations used with UDC are coordination and addition (“+”), consecutive extension (“/”), relation (“:”), order-fixing (“::”), subgrouping (“[]”), non-UDC notation (“*”), alphabetic extension (“A–Z”), besides place, time, nationality, language, form, and characteristics. The small examples and unsorted excerpts of the knowledge resources objects features only refer to main UDC-based classes, which for this part of the publication are taken from the Multilingual Universal Decimal Classification Summary (UDCC Publication No. 088; Multilingual Universal Decimal Classification Summary 2012) released by the UDC Consortium under the Creative Commons Attribution Share Alike 3.0 license (Creative Commons Attribution Share Alike 2017) (first release 2009, subsequent update 2012).

Research Approaches

Research, Systematics, and Methodology

The objective is to create long-term, multidisciplinary, and multilingual knowledge resources, which provide structure and universal classification for a sustainable knowledge and resources management. The resources can classify and integrate environmental information for further use for various application scenarios, including dynamical information system components and computational components.

It must be possible to integrate and document data and information from any source. The core classification has to support multidisciplinary knowledge. The resources must be able to support various international standards, scientific methodologies, and methods. The goal is to resume data on past and present status and to mediate the essences between the so-called “precise sciences” and “humanities” for which overall success is necessary to concentrate on provable and understandable results as well as on accepted facts and testimonies of the past. The overall system has to support the definition of processes and workflows. The information is from serious research peer reviews and even more important an independent scientific auditing in addition. It is widely accepted that this is done by independent experienced scientists and not from management or disciplinary superior positions.

The methods used for classification are based on the UDC, which have their origin more than 100 years ago and the structure and environmental data samples are based on the LX Foundation Scientific Resources knowledge resources (LX-Project 2017), which are currently created and developed for more than 25 years and proofed long-term reliable and consistent.

Information, Computation, and Integration: Multidisciplinary Result Matrices

Environmental data and referred knowledge can be described with many associations and attributes. For example, with the knowledge resources a characteristic integrating request is: “environmental impact, natural variations, man-made variations, climate change.” In some context it is less difficult to find near-present information, but more difficult to get the long-term multidisciplinary view. A deep discovery into the resources context is required in these cases. Primary results refer to “geology, volcano, allitic weathering, alps, climate” and secondary result point to “moon, earth axis” and to the information that the rotation of the moon and stabilization of the earth’s axis also stabilized the climate for geological times. In order to get the facets of information the processing employing the UDC allows a universal faceted documentation and a large flexibility with the workflows (Rückemann 2014a).

Enabling a long-term development for the available systems components, the framework integrates Resources Oriented Architectures (ROA), Services Oriented Architectures (SOA), and “Knowledge Oriented Architectures” (KOA) (ROA 2015). In that context, many interactive and dynamical applications components, for example, mapping applications, can be used as well as batch and command line based components (Generic Mapping Tools 2017). Regarding the sustainability the framework of Knowledge Oriented Architectures (KOA) (Rückemann 2013a) is the essential component complementing SOA and ROA concepts.

The base for the implemented IICS is the collaboration house framework (Rückemann 2012b). Application components can also be integrated with the IICS regarding interactive use, advanced workflows, and code reuse (Rückemann 2011b; Rückemann 2012a; Rückemann 2013c) or complex envelopes (Rückemann 2011a; Rückemann and Gersbeck-Schierholz 2011). The integration can respect cognostic aspects of the data, for example, geocognostic views (Edwards 1996).

Workflows can be supported by intelligent system components, for example, Multi-Agent Systems (MAS) (Leitão, Inden, and Rückemann 2013; Rückemann 2013b) and handling operations systems requirements (Inden, Meridou, Papadopoulou, Anadiotis, and Rückemann 2013).

The classified knowledge objects have been used for the documentation of natural sciences research and results, for environmental and climatological information. In this context they have been investigated regarding the reconstruction of historical data, shipping routes, trade dependencies, and cultural developments. This also includes the reconstruction from archaeological data and volcanological data as well as for example the integration of meteorite data (Rückemann 2013d; Rückemann 2014a). With several case studies information referring to external archives have been successfully used in order to improve the result matrices on request, for example, with historical data by including the archives on information about Gottfried Wilhelm Leibniz (1646–1716) (LeibnizCentral 2017; GEXI 2017).

Any knowledge object classified appropriately, implicitly refers to climatological data, agricultural use, periods of climate change and multifold secondary data. The following text excerpt (Figure 3.1) from an intermediate result matrix created from object documentation shows samples of the keyword context from the index within references of the LX.

Figure 3.1 Keyword context example from within the LX foundation scientific resources, entries from an intermediate multilingual result matrix on environment and climatology

Foundation Scientific Resources

Within the knowledge resources these index entry references resolve to the respective sources (Bates and Jackson 1980; Matschullat and Müller 1994; O’Riordan 1996). The index references cannot be handled by string search only. Advanced methods as translations services, phonetic support, and correction support can be used with the classification in order to improve the result matrix with multilanguage information on these topics. For this example an excerpt of a minimal translation support table is given in Figure 3.2.

Figure 3.2 Practical examples of a minimal translation support table

In this case, resolving context at the index entry level means resolving expanded content, which can be considered with the discovery workflow. The result of a request supported by a translation module and filter, considering organization samples and references on environment and climatology is shown in the following excerpt of an intermediate result matrix:

2007/2/EC [GIS, GDI, Geoinformatics, Environment, Climate, ...]:

Directive 2007/2/EC of the European Parliament and of the Council of March 14, 2007 establishing an Infrastructure for Spatial Information in the

European Community (INSPIRE).

2004/35/EC [Environment, Climate, GIS, ...]:

2004/35/EC, European Community, Environmental Liability Directive.

A2C2 [Environment, Climate]:

Albay in Action on Climate Change.

AAOE [Meteorology, Climate]:

Airborne Antarctic Ozone Experiment.

ACC [Environment, Climate, Oceanography]:

Anthropogenic Climate Change.

ACCAD [Climatology, Oceanography, Committee]:

Advisory Committee on Climate Applications and Data (CCl).

ACSYS [Oceanography, Climatology]:

Arctic Climate System Study (WCRP).

ADIOS [Climate, Oceanography]:

Asian Dust Input to the Oceanic System.

AEIDC [Climatology, Environment, Oceanography]:

Arctic Environmental Information and Data Centre, United States.

AEKOS [Environment, Climate, GIS, ...]:

The Australian Ecological Knowledge and Observation System, Australia.

AGCM [Oceanography]:

Atmospheric General Circulation Model.

AMOC [Oceanography, Climatology]:

Atlantic Meridional Overturning Circulation.

APARE [Oceanography]:

East Asian-North Pacific Regional Experiment.

ASEAMS [Oceanography, Association]:

Association of South-East Asian Marine Scientists.

BUIS [Environment, Climate, GIS, ...]:

Betriebliche Umweltinformationssysteme.

CCl [Climate, Oceanography, Commission]:

Commission for Climatology (WMO).

CCOP [Oceanography, Committee, Mining]:

Committee for Coordination of Joint Prospecting for Mineral Resources in

Asian Offshore Areas.

CIP [Environment, Climate, GIS, ...]:

Continual Improvement Process.

Copernicus [Environment, Climate, GIS, ...]:

European EO Program.

European Earth Observation Program.

EAHC [Oceanography, Hydrography, Commission]:

East Asia Hydrographic Commission (IHO).

EEA [Environment, Climate, GIS, ...]:

European Environment Agency, European Union.

EIONET [Environment, Climate, GIS, ...]:

European Environment Information and Observation NETwork.

EIS [Environment, Climate, GIS, ...]:

Environmental Information System

ELD [Environment, Climate, GIS, ...]:

Environmental Liability Directive. 2004/35/EC, European Community.

EMS [Environment, Climate, GIS, ...]:

Environmental Management System.

ENVIS [Environment, Climate, GIS, ...]:

Environmental Information System, India.

EPA [Environment, Climate, GIS, ...]:

U.S. Environmental Protection Agency.

EUA [Environment, Climate, GIS, ...]:

Europäische Umweltagentur.

FAOB [Abbreviation]:

Federation of Asian and Oceanian Biochemists.

FIRE [Climatology]:

First ISLSCP Regional Experiment.

GEOSS [Environment, Climate, GIS, ...]:

Global Earth Observation System of Systems.

GMCC [Institute, Geophysics, Environment, Climate]:

Geophysical Monitoring for Climatic Change.

GMES [Environment, Climate, GIS, ...]:

Global Monitoring for Environment and Security.

GOOS [Environment, Climate, GIS, ...]:

Global Ocean Observing System.

GPCC [Climate]:

Global Precipitation Climatology Center.

GPCP [Oceanography]:

Global Precipitation Climatology Project (WCRP).

IMIS [Environment, Climate, GIS, ...]:

Integriertes Mess- und Informationssystem.

INSPIRE [Environment, Climate, GIS, ...]:

Infrastructure for Spatial Information in the European Community. 2007/2/EC,

European Community.

ISCCP [Satellite, Climate]:

International Satellite Cloud Climatology Project (WCRP).

ISLSCP [Climatology, Oceanography]:

International Satellite Land Surface Climatology Project (WCRP).

ISO 14000 [Environment, Standards]: ...

ISO 14000 [Environmental Management]: ...

LCA [Environment, Climate, GIS, ...]:

Life Cycle Assessment.

SEARNG [Environment, Geophysics]:

S.E. Asian Region Network for Geosciences.

SHaRED [Environment, Climate, GIS, ...]:

Submission, Harmonization and Retrieval of Ecological Data (tool).

SHIFOR [Oceanography]:

A climatology and persistence model.

(not an acronym).

SPENVIS [Environment, Climate, GIS, ...]:

The Space Environment Information System, ESA.

SRBCP [Climatology, Satellite, Oceanography]:

Satellite Radiation Budget Climatology Project (WCRP).

TEK [Environment, Climate, GIS, ...]:

Traditional Ecological Knowledge.

TERN [Environment, Climate, GIS, ...]:

Terrestrial Ecosystem Research Network, Australia.

UEUS [Environment, Climate, GIS, ...]:

Umweltbezogene Entscheidungsunterstützungssysteme.

UIS [Environment, Climate, GIS, ...]:

Umweltinformationssystem.

UNEP [Environment, Climate, GIS, ...]:

United Nations Environment Program.

UNEP-GRID [Environment, Climate, GIS, ...]:

United Nations Environment Program/Global Resource Information Database.

USM [Environment, Climate, GIS, ...]:

Umweltbezogene Instrumente des Strategischen Managements.

WCED [Environment, Climate, GIS, ...]:

World Commission on Environment and Development.

WCRP [Climatology, Oceanography, ...]:

World Climate Research Program.

This small excerpt shows only some main entries from a result matrix. The entries shown list some main information on the acronyms, abbreviations, and alike as well as the broad spectrum and actual and historical range of the references. A full result matrix can contain projects, centers, institutions, terms, and all their references, links, and secondary information.

The references and especially the classification have been left out here in order to concentrate on the disciplinary topics within the results. For example, especially the Directive 2007/2/EC of the European Parliament and of the Council of March 14, 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) is closely coupled with environmental protection, environmental legislation, and environmental monitoring (INSPIRE 2017).

The knowledge resources associate 2007/2/EC with the Directive 2004/35/EC, the Environmental Liability Directive (ELD), due to the environmental relevance (EC 2004; EC 2007).

The matrix can contain not only the disciplinary relevant references on content and context but also the associated topics from interactive online discovery via learning processes, for example, on traditional ecological knowledge (Casimirri 2003).

A secondary view can, for example, document the change on the meaning of certain terms over time and refer to the appropriate context. The U.S. Environmental Science Services Administration (ESSA) defined environmental science in 1968 “A science that is involved with ‘all of nature we perceive or can observe, that is our physical environment-a composite of earth, sun, sea, and atmosphere, their interactions, and the hazards they present’” (Bates and Jackson 1980). Bates and Jackson summarize “Earth science applied to the human habitat.” Later nondiscipline-centered definitions more and more refer to human environment and man-made interactions and changes.

A practical example of top associated terms from above objects in the intermediate result matrix created from the knowledge resources applying the classification is shown in Figure 3.3.

The references deliver the links, for example, to the objects volcano, Vesuvius, Campi Flegrei, phlegra, scene of fire, Pompeji, Herculaneum, volcanic ash, lapilli, catastrophe, climatology, eruption, lava, gas ejection, CO₂, and so on. In turn these links, for example, contribute with detailed disciplinary content and context references (Figure 3.4) to the result matrix on the environmental request.

The excerpt of this entry (Figure 3.4) from the knowledge resources refers to classifications, for example, UDC, keywords, synonyms, geo-locations, and references, which allow the application of advanced processing, mapping, statistics, heuristic methods, phonetic algorithms, translations, and many more. Classification, content, and context can be evaluated together and refer to contributions from integrated and distributed information resources.

Figure 3.3 Practical examples of top associated terms in the intermediate result matrix

Figure 3.4 Excerpt of knowledge resources object entry including classifications, keywords, geo-locations, and references

Contributions to Computational Resources from Academic Research and Industry

The activities in research are multifold but they strongly depend on funding. Political and social activities are reacting on demands from society and economy. The resources providers and computing industry are providing capacities, creating architectures, and selling their products. New resources and architectures are produced and products and services are mostly produced and sold for those best paying. The triangle of that constellation can be summarized with some examples:

Concentrating on the contributions for advanced processing and computing using knowledge resources in the fields of environmental sciences shows a hiatus in sustainability between long-term methodological means and technological strategies.

Technological strategies are underlying regular short-term replacement cycles (e.g., hardware, architectures, programming environments). With these, resources providers and industry frequently change their portfolio of affordable supplies depending mostly only on the top quantity of consumer demands.

Methodological approaches like documentation, simulation, and modeling are in the hands of research disciplines, which struggle with sufficient continuity in their processes in order to cope with the costs and challenges even for short time intervals like 6 to 15 years. The institutions far most widely involved in big data handling and long-term knowledge worldwide are libraries and museums on the on hand and search engines on the other hand.

Besides many more restrictions, libraries are concentrating on a limited spectrum of objects, mostly “written” objects. Museums are strongly limited by the quantity they can handle. Search engines mostly operate on unstructured data, which they do not maintain or develop themselves, neither for structure, content nor for long-term aspects. Overall, the knowledge available for possible long-term activities is extremely heterogeneous and fragmentary resulting from the limitation in variety, structure, consistency, and continuance.

The requirements for making use of such data for large implementations are even less demanding and up to now resulted in simple search and request facilities based on the already available data, which even cannot take real advantage neither of the knowledge nor the computational potential.

In contrast to that, the preliminary academically work and achievements would allow to conceptualize a much more universal and sustainable approach to long-term knowledge. Methodological means have been successfully developed, implemented in various components, and used for decades, for example, for phonetics, statistics, pseudonyms, ligature handling, translations, transcriptions, transliterations, and typographical corrections.

A large number of long-term, multidisciplinary case studies in natural sciences and humanities have shown the efficient integration of systematically and methodological approaches with knowledge resources and universal classification and scientific and FEC (Rückemann 2013d, 2014b).

The available algorithms, frameworks, and tools are numberless. Anyhow, the number of sustainable components, which can in fact be used for long-term resources is quite restricted (Russel and O’Dell 1918; Knuth 1973; National Archives and Records Administration—NARA 2007; Stok 1994; LX-Project SNDX 2017; Rempel 1998; Kupries 2003; ECHO 2017; GMT 2017; GDAL Development Team 2017; Open-MPI 2017; Tcl Developer Site 2017; CTAN 2017).

In all major cases for creating and operating complex long-term resources the efforts for creating the content have shown to be extremely high but also the most rewarding long-term contribution. One of the most frequently used application scenarios, which can be discussed based on some general understanding, is the computation of result matrices within complex knowledge discovery or simple search requests. This includes compute requests, resources’ integration, and workflow creation based on contributions from disciplines, services, and resources’ providers. The goal within this scenario is the creation of result matrices from available knowledge resources utilizing available means.

Computing result matrices is an arbitrary complex task, which can depend on various factors. Applying statistics and classification to knowledge resources has successfully provided excellent solutions, which can be used for optimizing result matrices in context of natural sciences, for example, geosciences, archaeology, volcanology or with spatial disciplines, as well as for universal knowledge (Rückemann 2014c, 2015). The method and application types used for optimization imply some general characteristics when putting discovery workflows into practice regarding components like terms, media, and other context (Table 3.1).

For the study, the number of result matrix entries has been defined to 10 based on 50,000 objects. The number of common workflow operations is in the range of 6,500 with according wall time on one core of about 6,700 seconds. Result matrices generated with special focus, so called “Section Views,” have shown to be dominated by prominent features, the most often used, for example, in combination are time, space, disciplines, attributes, and culture.

Table 3.1 Resulting per-instance-calls for types of methods and applications used for optimization with knowledge discovery

Type	Terms	Media	Workflow	Algorithm	Combination
Mean	500	20	20	50,000	3,000
Median	10	5	2	5,000	50
Deviation	30	5	5	200	20
Distribution	90	40	15	20	120
Correlation	15	10	5	20	90
Probability	140	15	20	50	150
Phonetics	50	5	10	20	50
Regular expressions	920	100	50	40	1,500
References	720	120	30	5	900
Association	610	60	10	5	420
UDC	530	120	20	5	660
Keywords	820	100	10	5	600
Translations	245	20	5	5	650
Corrections	60	10	5	5	150
External resources	40	30	5	5	40

Statistics methods have shown to be an important means for successfully optimizing result matrices. The most widely implemented methods for the creation of result matrices are intermediate result matrices based on regular expressions and intermediate result matrices based on combined regular expressions, classification, and statistics, giving their numbers special weight. Based on these per-instance numbers this results in demanding requirements for complex applications–On “numerical data”: Millions of calls are done per algorithm and dataset, hundreds in parallel or compact numeric routines. On “terms”: Hundred thousands of calls are done per sub-workflow, thousands in parallel or complex routines, are done. Most resources are used for one application scenario only. Only 5 to 10 percent overlaps between disciplines–due to mostly isolated use. Large benefits result from multidisciplinary multilingual integration. The multilingual application adds an additional dimension to the knowledge matrix, which can be used by most discovery processes. As this implemented dimension is of very high quality the matrix space can benefit vastly from content and references. Still, sustainable long-term efforts have to focus on long-term knowledge creation, integrated systems, and complex knowledge discovery.

The importance of scalable workflows is well demonstrated when computing optimized result matrices for the processing of objects from knowledge resources (Rückemann 2014c), which increases the effectiveness and quality of results while reducing the resources’ requirements. A simple workflow can be exemplary summarized with several steps:

Currently there is no publicly supported sustainable long-term funding for the required long-term resources. The driving forces are not coming from the industry itself, this is expected to be done by disciplines, funding agencies, governmental as well as intergovernmental or transgovernmental initiatives. In the future, a stronger and more sustainable collaboration between disciplines, services, and resources providers is required in order to interlink the systematically creation, the methodological implementation, and the sustainable support. This includes political activities on all levels, creating a reliable and sustainable collaboration environment.

Classification for Interlinking Any Multidisciplinary Knowledge

UDC is an excellent classification for interlinking any multidisciplinary knowledge including all disciplines and facets. UDC is currently used by about 150,000 institutions worldwide, many in classifying catalogue context. The integration of UDC classification with long-term knowledge resources is of major benefit for an efficient and sustainable use and long-term vitality of the knowledge as has been shown by all cited practical case studies.

It has been shown how long-term knowledge resources can be created and used for more than 25 years considering content and context with sophisticated workflows implementing various technologies over the years. The knowledge resources have proven to provide a universal way of describing multidisciplinary objects, expressing relations between any kind of objects and data, for example, from archaeology, geosciences, and natural sciences as well as defining workflows for calculation and computation for application components. Systematically structuring, classification, as well as soft “silken” criteria with LX and UDC support have provided efficient and economic means for using information system components and supercomputing resources. With these, the solution scales, for example, regarding references, resolution, and view arrangements even with big data scenarios and parallel computing resources. The components and resources have shown highest extensibility, from knowledge and content perspective as well as from application side.

The creation of long-term knowledge resources and applications derived do benefit vastly from this universal integration of multidisciplinary and long-range content and context information. Regarding the environmental disciplines multidisciplinary can mean natural sciences, humanities, industry, and economy. The long-range information is prehistorical, historical, and archaeological information and the context information are, for example, associations, references, and knowledge objects. The concept can be transferred to numerous applications in a very flexible way and has shown to be most sustainable. The successful integration of IICS components and advanced scientific computing based on structured information and faceted classification of objects has provided a very flexible and extensible solution for the implementation of climatological information systems as well as for archaeological information systems.

Resulting Multidisciplinary Classification for Environment and Climatology

The conceptual knowledge related result of this research is practical classification for environment and climatology (Table 3.2, see Annex). This table shows a classification, which can be integrated with the knowledge resources. This example shows a small excerpt of referred and practically usable UDC codes to be associated the objects. The excerpt includes a range of environmental and climatological topics. Contributions to these multidisciplinary topics are from a very wide range of fields. The context of climatology is naturally multidisciplinary, integrating many disciplines, phenomena, and secondary and tertiary aspects. The full range of classifications and billions of combined classifications and facets can be used with any knowledge resources objects, containers, and other means of creating groups. This small excerpt already shows very well how inter-weaved the climatological, natural sciences, and social sciences are. The main focus is on climatological aspects, environmental issues, as well as on natural sciences and social sciences topics. All the entries can be combined following the UDC rules for creating complex and faceted classifications as well as creating any number of classification views for an object or a group of objects.

Managerial and Practical Implications

Developments in multidisciplinary knowledge can be consistently handled and managed with the steadily evolving classification editions. These can be used in production with the knowledge resources for long-term documentation. The resources and documentation can be multidisciplinary to any extent, fully multilingual, and it can be kept consistent supporting classification editions.

In addition, with a multidisciplinary collaboration framework any application and operation scenario can be supported. As with sustainable long-term strategies, it has been found that with multidisciplinary research the funding of researchers should complement the funding of institutions. Supporting knowledge creation and research at the central researcher and collaboration level is a core requirement for a sustainable long-term knowledge creation and discovery.

It has been demonstrated with case studies over the last years that archaeological and environmental IICS can provide advanced multidisciplinary information as from climatology, archaeology, and geosciences by means of HEC resources. The basic architecture has been created using the collaboration house framework, long-term documentation and classification of objects, flexible algorithms, workflows, and active source components. As shown with the examples, any kind of computing request, for example, discovery, data retrieval, visualization, and processing, can be done from the application components accessing the knowledge resources. Computing interfaces can carry any interactive or batch job description. Anyhow, the hardware and system resources have to be configured appropriately for a use with the workflow. For future applications a kind of “tooth system” for long-term documentation and algorithms for use with IICS and the exploitation of supercomputing resources will be developed. Besides this, it is intended to further extend the content spectrum of the knowledge resources.

Conclusion

Long-term knowledge resources, systematics, and methodology as resulting from this research have shown to provide sustainable and efficient means of documenting, gathering, integrating, reusing, and managing any kind of information. For multidisciplinary research, for example, environmental studies including natural sciences and social sciences context, the knowledge resources support the multidisciplinary knowledge gathering and documentation as well as classification, discovery, and decision-making processes. Implicitly, this is also of huge importance for creating sustainable EMS and information system components based on long-term resources and standardized components. For a sustainable creation of multidisciplinary long-term knowledge it is suggested to have a complementary personalized funding of researchers in addition to the funding of institutions. Knowledge, for example, data or workflow objects, can be created and used for long periods of time contributing to the multidisciplinary integration essentially required for environmental research.

The research and the reasons for climate change effects are multifold. Anthropogenic factors include technological and industrial development. Many aspects and interests, for example, economic, lobby, or network interests, are often contrary to ecological aspects. Contributions can be based on provable natural sciences facts or on the other hand argumentation can be nonscientific. This results in multidisciplinary challenges for documentation as well as for analysis of information.

The use of UDC and knowledge resources with multidisciplinary context can be recommended with any climatology, environmental, and related disciplines. The case studies have shown that the knowledge resources can be efficiently created, used, and extended for sustainable long-term documentation and application components.

Advanced long-term knowledge resources integrating structure and universal classification can successfully provide any information and interlink all the required disciplines and context helping to document and integrate any multidisciplinary knowledge with environmental and climatological application scenarios. Knowledge resources can, for example, deliver references within knowledge, acronym expansions, translations, directives, publication content and context, realia references, media samples. The concept and components are multilingual, support big data on volume, variability, velocity, and vitality, and can be used with HEC–distributed and supercomputing–resources. More than that, the knowledge resources can support data and application assignments as well as application and computing and storage system assignments.

With the systematics and methodological background from scientific research, there are large numbers of basic components, algorithms, and frameworks available from advanced scientific computing and HEC, which are technically well supported by the computing industry. Nevertheless, the triangle of the constellation of scientific disciplines, political activities, and resources providers, computing industry, and engineering in future requires a strong and sustainable collaboration support between disciplines, services, and resources providers.

A lot of environmental, natural sciences, and archaeological topics have been addressed with the last years’ research and developments. Objects and components of any related topics be classified and integrated. There are three major targets for sustainability and long-term vitality: Knowledge resources, consistent universal classification, and multidisciplinary content, for example, on environmental research, results, term, recommendations, best practices, and legal regulations. The primary operational facilities include big data access, internationalization, and multilingual classification. Regarding complexity the deployment of intelligent system components and methods for analysis and advanced discovery has been found very beneficial.

The major benefits of the efforts correlate with the major challenges from the required combination of the components: These challenges are integration and the long-term aspects, for example, the operation of the resources, the integration of components, and the consistency of heterogeneous contributions. On the other side the integrated architectures are designed to eliminate most conceptual limitations. Challenges can mostly arise from a different understanding of real world complexity and from creation and operation of technical implementations on that restricted base. We may suggest that sufficient sustainable holistic and long-term funding can provide a reliable base for future research and education on the ongoing creation and universal utilization of universal knowledge resources.

Acknowledgment

I am grateful to my scientific colleagues at the Westfälische Wilhelms-Universität Münster (WWU) and to the “Knowledge in Motion” (KiM) long-term projects, Unabhängiges Deutsches Institut für Multidisziplinäre Forschung (DIMF), for partially funding this implementation, case study, and publication (grant D2012F2P04492) and to its senior scientific members, especially to Dr. Friedrich Hülsmann, Gottfried Wilhelm Leibniz Bibliothek (GWLB) Hannover, to Dipl.-Biol. Birgit Gersbeck-Schierholz, Leibniz Universität Hannover, and to Dipl.-Ing. Martin Hofmeister, Hannover, for fruitful discussion, inspiration, practical multidisciplinary case studies, and the analysis of advanced concepts. I am grateful to all national and international academic, industry, and business partners in the GEXI and LX Cooperations for the innovative constructive work and the Science and High Performance Supercomputing Centre (SHPSC) for long-term support of collaborative research and the LX-Project for providing suitable resources. Heartfelt thanks to the members of the boards and the participants of the INFOCOMP, GEOProcessing, ICDS/DigitalWorld, and ICNAAM conferences for their excellent collaboration within the last years. Many thanks to the scientific colleagues at the WWU and the Institute for Legal Informatics (IRI), Leibniz Universität Hannover, sharing experiences on ZIV, HLRN, Grid, and Cloud resources and for participating in fruitful case studies as well as the participants of the EULISP Program for prolific scientific discussion over the last years. I am grateful to the UDC Consortium for continuously providing, extending, and improving the excellent UDC for public use. I am grateful to the Gottfried Wilhelm Leibniz Bibliothek (GWLB), Hannover, Germany, for the collection and public provisioning of most complete information on Gottfried Wilhelm Leibniz and related work. Thanks go to the Akademie der Wissenschaften zu Göttingen and the Akademie der Wissenschaften zu Berlin for the successful implementation of information system components enabling an advanced provisioning and integration of information. I do thank the international colleagues from geosciences, informatics, and archaeology in the present collaborations and the peer reviewers for constructive feedback and proof-reading this chapter.

References

AEKOS (Australian Ecological Knowledge and Observation System). 2017. The Australian Ecological Knowledge and Observation System. Australia, Retrieved from http://aekos.org.au/

Bates, R.L., and J. Jackson, eds. 1980. Glossary of Geology: American Geological Institute, 2nd ed. Virginia: Falls Church.

Bonaventura, M.A.L. 2007. Pompeii Reconstructed Book with DVD. Rome: Archeolibri. ISBN: 978-8-89551-223-5.

Casimirri, G. 2003. “Problems with Integrating Traditional Ecological Knowledge into Contemporary Resource Management.” Original unedited version of a paper submitted to the XXI World Forestry Congress, Quebec City, Canada, Retrieved from http://fao.org/docrep/ARTICLE/WFC/XII/0887-A3.HTM

Creative Commons Attribution Share Alike. 2017. “Creative Commons Attribution Share Alike 3.0 license.” http://creativecommons.org/licenses/by-sa/3.0/

CLIWOC (CLImatological Database for the World’s Oceans). 2017. “Climatological Database for the World’s Oceans 1750–1850.” http://pendientedemigracion.ucm.es/info/cliwoc/

Copernicus, N. 2017. “Copernicus, European Earth Observation Programme.” http://copernicus.eu/

Csanady, G.T. 1973. Turbulent Diffusion in the Environment, Vol. 3 of Geophysics and Astrophysics Monographs. Dordrecht: D. Reidel Publishing Company.

CTAN (Comprehensive TeX Archive Network). 2017. “Comprehensive TeX Archive Network.” (TeX/LaTeX), Retrieved from http://ctan.org (March 8, 2017)

di Maio, P. 2012. “A Global Vision: Integrating Community Networks Knowledge.” In Community Wireless Symposium, European Community. Barcelona: EC Infoday, October 5, 2012 Retrieved from http://people.ac.upc.edu/leandro/misc/CAPS_Paola.pdf

EC. 2004. Directive 2004/35/EC. European Community. Directive 2004/35/EC, Environmental Liability Directive.

EC. 2007. Directive 2007/2/EC. European Community. Directive 2007/2/EC of the European Parliament and of the Council of March 14, 2007 Establishing an Infrastructure for Spatial Information in the European Community (INSPIRE).

Edwards, G. 1996. “Geocognostics—A New Paradigm for Spatial Information?” In Proceedings of the AAAI Spring Symposium.

EEA (European Environment Agency). 2017. European Environment Agency. Copenhagen, Denmark: European Union. http://europa.eu/about-eu/agencies/regulatoryagenciesbodies/policyagencies/eea/indexdd.ht m

ENVIS (Environmental Information System). 2017. Environmental Information System, India. http://envis.nic.in/

EPA (Environmental Protection Agency). 2017. U.S. Environmental Protection Agency. Retrieved from http://epa.gov/

ESSA (Environmental Science Services Administration). 1968. U.S. Environmental Science Services Administration. Washington, DC: U.S. Government Printing Office.

ECHO (European Cultural Heritage Online). 2017. Max-Planck Institut für Wissenschaftsgeschichte (English: Max Planck Institute for the History of Science). Berlin. http://echo.mpiwg-berlin.mpg.de/

GDAL Development Team. 2017. GDAL—Geospatial Data Abstraction Library. Open Source Geospatial Foundation, Retrieved from http://gdal.org

GMT (Generic Mapping Tools). 2017. Generic Mapping Tools, Retrieved from http://imina.soest.hawaii.edu/gmt

GEOSS (Global Earth Observation System of Systems). 2017. Global Earth Observation System of Systems, Retrieved from http://earthobservations.org

GEXI (Geo Exploration and Information). 2017. Geo Exploration and Information, Retrieved from http://user.uni-hannover.de/cpr/x/rprojs/de/#gexi

GMES (Global Monitoring for the Environment and Security). 2017. Global Monitoring for the Environment and Security, Retrieved from http://ec.europa.eu/enterprise/policies/space/gmes/

Guardasole. 2013. Vesuvio 1270 m. Guardasole SRL, Napoli, Via Argine, 313, Italia, 2013, Postcard, 40067, Description: 1944 Eruptions and Present Day Crater. Collection: LX, Provider: BGS, Entry date: 2013.

Hutzinger, O, ed. 1982. The Handbook of Environmental Chemistry—Anthropogenic Compounds. 3 vols, Part B. Berlin, Heidelberg, and New York, NY: Springer Verlag.

Inden, U., D.T. Meridou, M.E.C. Papadopoulou, A.C.G. Anadiotis, and C.-P. Rückemann. 2013. “Complex Landscapes of Risk in Operations Systems Aspects of Processing and Modelling.” In Proceedings of The Third International Conference on Advanced Communications and Computation (INFOCOMP 2013), November 17–22, pp. 99–104. Lisbon, Portugal: XPS Press. Retrieved from http://thinkmind.org/index.php?view=article&articleid=infocomp_2013_5_10_10114

INSPIRE (Infrastructure for Spatial Information in Europe). 2017. Infrastructure for Spatial Information in Europe, Retrieved from http://inspire.jrc.ec.europa.eu/

ISO (International Organisation for Standardisation). 2017. ISO 14000—Environmental Management. International Organisation for Standardisation, Retrieved from http://iso.org/iso/iso14000

Johnson, F.S. 1965. Satellite Environment Handbook. California, USA: Stanford.

Knuth, D.E. 1973. The Art of Computer Programming: Sorting and Searching, 3 Vols. Redwood City, CA: Addison-Wesley.

Kupries, A. 2003. tcllib, soundex.tcl, Soundex Tcl Port Documentation. (code after Donald E. Knuth).

Leibniz Central. 2017. Gottfried Wilhelm Leibniz Bibliothek (GWLB). Hannover: Niedersächsische Landesbibliothek. http://leibnizcentral.de

Leitão, P., U. Inden, and C.-P. Rückemann. 2013. “Parallelising Multiagent Systems for High Performance Computing.” In Proceedings of The Third International Conference on Advanced Communications and Computation (INFOCOMP 2013), November 17–22, pp. 1–6. Lisbon, Portugal: XPS Press. http://thinkmind.org/download.php?articleid=infocomp_2013_1_10_10055

LX-Project. 2017. LX-Project, Retrieved from http://user.uni-hannover.de/cpr/x/rprojs/en/#LX

LX-Project SNDX. 2017. LX-SNDX, a Soundex Module Concept for Knowledge Resources. LX-Project Consortium Technical Report, Retrieved from http://user.uni-hannover.de/cpr/x/rprojs/en/#LX

Matschullat, J., and G. Müller, eds. 1994. Geowissenschaften und Umwelt. Berlin, Heidelberg, and Germany: Springer-Verlag.

Multilingual Universal Decimal Classification Summary. 2012. UDC Consortium, Web resource, v. 1.1. The Hague: UDC Consortium (UDCC Publication No. 088). Retrieved from http://udcc.org/udcsummary/php/index.php

NARA (National Archives and Records Administration). 2007. The Soundex Indexing System. (2007-05-30). Retrieved from http://archives.gov/research/census/soundex.html

Newhall, C.G., and S. Self. 1982. “The Volcanic Explosivity Index (VEI): An Estimate of Explosive Magnitude for Historical Volcanism.” Journal Geophysical Research 87, pp. 1231–38.

Open-MPI. 2017. Open-MPI. Retrieved from http://open-mpi.org (March 8, 2017)

O’Riordan, T, ed. 1996. Umweltwissenschaften und Umweltmanagement: Ein interdisziplinäres Lehrbuch. Berlin, Germany: Springer.

PEMSEA (Partnerships in Environmental Management for the Seas of East Asia). 2017. Partnerships in Environmental Management for the Seas of East Asia, Retrieved from http://pemsea.org

Bonaventura, M.A.L. 2007. Pompeii Reconstructed Book with DVD. Archeolibri.

Rempel, E. 1998. tcllib, soundex.tcl, Soundex Tcl Port. (code after Donald E. Knuth).

ROA (Resource-Oriented Architecture). 2015. Resource-Oriented Architecture, Retrieved from http://en.wikipedia.org/wiki/Resource-oriented_architecture

Rückemann, C.-P. 2011a. “Envelope Interfaces for Geoscientific Processing with High Performance Computing and Information Systems.” In Proceedings International Conference on Advanced Geographic Information Systems, Applications, and Services (GEOProcessing 2011), February 23–28, pp. 23–28. [Rückemann, C.-P. and Wolfson, O. (eds.)] Gosier, Guadeloupe, France: DigitalWorld, XPS, Xpert Publishing Solutions. Retrieved from http://thinkmind.org/download.php?articleid=geoprocessing_2011_2_10_30030

Rückemann, C.-P. 2011b. “Application and High Performant Computation of Fresnel Sections.” In Symposium on Advanced Computation and Information in Natural and Applied Sciences, Proceedings of The 9th International Conference on Numerical Analysis and Applied Mathematics (ICNAAM), September 19–25, Vol. 1389, no. 1, pp. 1268–71, Halkidiki, Greece, Proceedings of the American Institute of Physics (AIP), Melville, New York: AIP Press, American Institute of Physics. Retrieved from http://link.aip.org/link/?APCPCS/1389/1268/1 (Permalink)

Rückemann, C.-P. 2012a. “Supercomputing Resources Empowering Superstack with Interactive and Integrated Systems.” In The Second Symposium on Advanced Computation and Information in Natural and Applied Sciences, Proceedings of The 10th International Conference on Numerical Analysis and Applied Mathematics (ICNAAM), September 19–25, Vol. 1479, no. 1, pp. 873–76. Kos, Greece, Proceedings of the American Institute of Physics (AIP), Melville, New York, NY: AIP Press, American Institute of Physics (American Institute of Physics Conference Proceedings). Retrieved from http://link.aip.org/link/apcpcs/v1479/i1/p873/s1 (Permalink)

Rückemann, C.-P. 2012b. “Integrating Information Systems and Scientific Computing.” International Journal on Advances in Systems and Measurements 5, nos. 3–4, pp. 113–27. http://thinkmind.org/index.php?view=article&articleid=sysmea_v5_n34_2012_3/

Rückemann, C.-P. 2013a. “Sustainable Knowledge Resources Supporting Scientific Supercomputing for Archaeological and Geoscientific Information Systems.” In Proceedings of The Third International Conference on Advanced Communications and Computation (INFOCOMP 2013), November 17–22, pp. 55–60. Lisbon, Portugal: XPS Press, ISSN: 2308-3484, ISBN-13: 978-1-61208-310-0, Retrieved from http://thinkmind.org/index.php?view=article&articleid=infocomp_2013_3_20_10034

Rückemann, C.-P. 2013b. “High End Computing and Advanced Scientific Supercomputing: Sustainability, Challenges, and Prospects with Management and Research, Lecture, International Expert Panel on Exa-Intelligence: Next Generations of Intelligent Multi-Agent and High End Computing Systems in Development and Practice.” In The Third International Conference on Advanced Communications and Computation (INFOCOMP 2013), November 18, 2013. Lisbon, Portugal: Retrieved November 17–22, 2013 from http://iaria.org/conferences2013/filesINFOCOMP13/INFOCOMP2013_EXPERT_PANEL.pdf

Rückemann, C.-P. 2013c. “High End Computing for Diffraction Amplitudes.” In The Third Symposium on Advanced Computation and Information in Natural and Applied Sciences, Proceedings of The 11th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), September 21–27, 2013, Rhodes, Greece: Proceedings of the American Institute of Physics (AIP), Volume 1558, Part 1, pages 305–08. AIP Press, American Institute of Physics, Melville, New York, USA, Oktober 2013. ISBN-13: 978-0-7354-1184-5, ISSN: 0094-243X (American Institute of Physics Conference Proceedings, print), DOI: 10.1063/1.4825483, Retrieved from http://link.aip.org/link/?APCPCS/1558/305/1 (Permalink).

Rückemann, C.-P. 2013d. “High End Computing Using Advanced Archaeology and Geoscience Objects.” International Journal On Advances in Intelligent Systems 6, nos. 3–4, pp. 235–55. [Bodendorf, F., (ed.)], ISSN: 1942-2679, LCCN: 2008212456 (Library of Congress), OCLC: 826628364. http://iariajournals.org/intelligent_systems/intsys_v6_n34_2013_paged.pdf

Rückemann, C.-P. 2014a. “Knowledge Processing for Geosciences, Volcanology, and Spatial Sciences Employing Universal Classification.” In Proceedings of The Sixth International Conference on Advanced Geographic Information Systems, Applications, and Services (GEOProcessing 2014), March 23–27, pp. 76–82. Barcelona, Spain: XPS Press. ISSN: 2308-393X, ISBN: 978-1-61208-326-1. http://thinkmind.org/download.php?articleid=geoprocessing_2014_4_10_30044

Rückemann, C.-P. 2014b. “Long-term Sustainable Knowledge Classification with Scientific Computing: The Multi-disciplinary View on Natural Sciences and Humanities.” International Journal on Advances in Software 7, nos. 1–2, pp. 302–17. (ISSN: 1942-2628). http://iariajournals.org/software/soft_v7_n12_2014_paged.pdf

Rückemann, C.-P. 2014c. “Computing Optimised Result Matrices for the Processing of Objects from Knowledge Resources.” In Proceedings of The Fourth International Conference on Advanced Communications and Computation (INFOCOMP 2014), July 20–24, 2014. pp. 156–62. Paris, France: XPS Press. (ISSN: 2308-3484). http://thinkmind.org/download.php?articleid=infocomp_2014_7_20_60039

Rückemann, C.-P. 2015. “Creating Knowledge-based Dynamical Visualisation and Computation.” In Proceedings of the Seventh International Conference on Advanced Geographic Information Systems, Applications, and Services (GEOProcessing 2015), February 22–27, pp. 56–62. Lisbon, Portugal: XPS Press. (ISSN: 2308-393X, ISBN: 978-1-61208-383-4). http://thinkmind.org/download.php?articleid=geoprocessing_2015_3_40_30063

Rückemann, C.-P., and B.F.S. Gersbeck-Schierholz. 2011. “Object Security and Verification for Integrated Information and Computing Systems.” In Proceedings of the Fifth International Conference on Digital Society (ICDS 2011), Proceedings of the International Conference on Technical and Legal Aspects of the e-Society (CYBERLAWS 2011), February 23–28. [Berntzen, L., Villafiorita, A., Perry, M. (eds.)] Gosier, Guadeloupe, France: Digital World XPS, Xpert Publishing Solutions. http://thinkmind.org/download.php?articleid=cyberlaws_2011_1_10_70008

Russel, R.C., and M.K. O’Dell. 1918. U.S. patent 1261167. (Soundex algorithm), patent issued 1918-04-02.

SHaRED 2017. Submission, Harmonisation and Retrieval of Ecological Data (SHaRED). Retrieved from http://aekos.org.au/SHaRED

SPENVIS (Space Environment Information System). 2017. The Space Environment Information System (SPENVIS). Paris, France: The European Space Agency (ESA). https://spenvis.oma.be/

Stok, M. 1994. Perl, Soundex.pm, Soundex Perl Port. (code after Donald E. Knuth).

Tcl Developer Site 2017. Tcl Developer Site. Retrieved from http://tcl.tk/

TERN (Terrestrial Ecosystem Research Network). 2017. Terrestrial Ecosystem Research Network, Australia, Retrieved from http://tern.org.au

tDAR (The Digital Archaeological Record). 2017. Digital Archaeological Record, Retrieved from http://tdar.org

Umweltbundesamt, ed. 1992. UMPLIS Informations- und Dokumentationssystem Umwelt, Umweltforschungskatalog 1992, (UFOKAT ‘92). Erich Schmidt Verlag GmbH & Co., Berlin, 9. Ausgabe, ISBN:3-503-03380-7.

UDCC (Universal Decimal Classification Consortium). 2017. Retrieved from, http://udcc.org

UDCS (Universal Decimal Classification Summary). 2017. Universal Decimal Classification Summary, Retrieved from http://udcc.org/udcsummary/php/index.php

UDCS (Universal Decimal Classification Summary) Translations. 2017. Universal Decimal Classification Summary (UDCS) Translations, Retrieved from http://udcc.org/udcsummary/translation.htm

UDCS (Universal Decimal Classification Summary). Translations: German. 2017. Universal Decimal Classification Summary (UDCS) Translations: German. Retrieved from, http://udcc.org/udcsummary/php/index.php?lang=6

UDCS (Universal Decimal Classification Summary). Exports. 2017. Universal Decimal Classification Summary (UDCS) Exports, Retrieved from http://udcc.org/udcsummary/exports.htm

UDCS (Universal Decimal Classification Summary). Linked Data. 2017. Universal Decimal Classification Summary (UDCS) Linked Data. Retrieved from http://udcdata.info

UNEP-GRID. 2017. United Nations Environment Programme / Global Resource Information Database (UNEP-GRID), Retrieved from http://grid.unep.ch/index.php?lang=en

Wesoky, H.L. 1997. “Research Focal Point Report; CO₂, NO_x and Related Topics.” In Contribution to the Committee on Aviation Environmental Protection. Seville, Spain: ICAO.

Wissenschaftsrat. 2011.Übergreifende Empfehlungen zu Informationsinfrastrukturen, (English: Spanning Recommendations for Information Infrastructures), Wissenschaftsrat, Deutschland, (English: Science Council, Germany), Drs. 10466-11, Berlin, 28.01.2011, 2011, Retrieved from http://wissenschaftsrat.de/download/archiv/10466-11.pdf

WDL (World Digital Library). 2017. World Digital Library, Retrieved from http://wdl.org

Yin, L., S.L. Shaw, D. Wang, E.A. Carr, M.W. Berry, L.J. Gross, and E.J. Comiskey. 2011. “A Framework of Integrating GIS and Parallel Computing for Spatial Control Problems—A Case Study of Wildfire Control.” International Journal of Geographical Information Science 26, no. 4, 621–41. doi:10.1080/13658816.2011.609487

Further Reading

Inden, U., D.T. Meridou, M.E.C. Papadopoulou, A.C.G. Anadiotis, I.S. Venieris, and C.-P. Rückemann. 2014. “Aspects of Modelling and Processing Complex Networks of Operations’ Risk.” International Journal on Advances in Software, ed. L. Lavazza, vol. 7, nos. 3–4, pp. 501–25. ISBN-13: 978-1-63439-815-2, Retrieved from http://thinkmind.org/index.php?view=article&articleid=soft_v7_n34_2014_7

Leitão, P., U. Inden, and C.-P. Rückemann. 2013. “Parallelising Multi-agent Systems for High Performance Computing.” In Proceedings of The Third International Conference on Advanced Communications and Computation (INFOCOMP 2013), November 17–22, pp. 1–6. Lisbon, Portugal: XPS Press. (ISSN: 2308-3484, ISBN-13: 978-1-61208-037-6). http://thinkmind.org/download.php?articleid=infocomp_2013_1_10_10055

Meridou, D. T., U. Inden, C.-P. Rückemann, C.Z. Patrikakis, D.T.I. Kaklamani, and I.S. Venieris. 2015. “Ontology-based, Multi-agent Support of Production Management.” In The Fifth Symposium on Advanced Computation and Information in Natural and Applied Sciences, Proceedings of the 13th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), September 23-29. Rhodes, Greece, Proceedings of the American Institute of Physics (AIP), AIP Conference Proceedings, volume 1738. AIP Press, American Institute of Physics, Melville, New York, USA, Juni 2016. Simos, T. E., Tsitouras, C. (eds.), ISBN-13: 978-0-7354-1392-4, ISSN: 0094-243X (American Institute of Physics Conference Proceedings, print), DOI: 10.1063/1.4951834.

Rückemann, C.-P. 2016. “Advanced Association Processing and Computation Facilities for Geoscientific and Archaeological Knowledge Resources Components.” In Proceedings of The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services (GEOProcessing 2016), April 24–28, pp. 69–75. Venice, Italy: XPS Press, Rückemann, C.-P., and Y. Doytsher, eds. (ISSN: 2308-393X, ISBN-13: 978-1-61208-469-5). Retrieved from http://thinkmind.org/index.php?view=article&articleid=geoprocessing_2016_4_20_30144

Rückemann, C.-P. 2016. “Enhancement of Knowledge Resources and Discovery by Computation of Content Factors.” In Proceedings of The Sixth International Conference on Advanced Communications and Computation (INFOCOMP 2016), May 22–26, 2016. pp. 24–31. Valencia, Spain. XPS Press. [Rückemann, C.-P., Pankowska, M. (eds.)], (ISSN: 2308-3484, ISBN-13: 978-1-61208-478-7). Retrieved from http://thinkmind.org/download.php?articleid=infocomp_2016_2_30_60047

Rückemann, C.-P. 2015. “Cognostics and Knowledge Used With Dynamical Processing.” International Journal on Advances in Software, ed. L. Lavazza, vol. 8, nos. 3–4, pp. 361–76. ISSN: 1942-2628, LCCN: 2008212462 (Library of Congress). Retrieved from http://iariajournals.org/software/soft_v8_n34_2015_paged.pdf

Annexure

Table 3.2 Example for classification references, which can be integrated with the knowledge resources: References based on the Universal Decimal Classification (UDC) provided under creative commons license, used for objects on environment and climatology

Classification	Description text (excerpt, english version)
UDC:3	Social sciences
UDC:349.6	Environmental protection law
UDC:379.84	Outdoor, open-air recreation (according to physical environment)
UDC:5	Mathematics. Natural sciences
UDC:500	Natural sciences
UDC:502/504	Environmental science. Conservation of natural resources. Threats to the environment and protection against ...
UDC:502	The environment and its protection
UDC:502.1	The environment and society. Conservation and protection in general
UDC:502.11	Interaction, interdependence of environment and society. Mutual benefits, etc.
UDC:502.12	Environmental awareness. “Green” outlook. Green issues. “Greenness”
UDC:502.13	Conservation measures and management
UDC:502.131	Development
UDC:502.14	Social, administrative, legislative measures on environmental conservation
UDC:502.15	Environment in relation to planning and development
UDC:502.17	Protection of the environment in general
UDC:502.171	Protection, rational use and renewal of natural resources
UDC:502.174	Restoration, salvage, reclamation, rescue measures. Wasteless and low-waste technology
UDC:502.175	Control of environmental quality. Control of pollution
UDC:502.2	The environment as a whole
UDC:502.21	Natural resources and energy
UDC:502.211	The living world. The biosphere
UDC:502.3	Atmospheric environment
UDC:502.5	Earth’s surface. Landscape. Scenery
UDC:502.51	Hydrospheric environment
UDC:502.52	Lithospheric environment
UDC:502.6	Glacial environment
UDC:504	Threats to the environment
UDC:504.1	Direct damage. Depredation. Threat of depredation
UDC:504.4	Damage from natural causes. Natural disasters. Natural hazards
UDC:504.5	Damage from harmful materials. Pollution
UDC:504.61	Damage by man to the environment
UDC:504.7	Global warming. “Greenhouse effect”
UDC:51	Mathematics
UDC:510	Fundamental and general considerations of mathematics
UDC:511	Number theory
UDC:512	Algebra
UDC:514	Geometry
UDC:517	Analysis
UDC:519.1	Combinatorial analysis. Graph theory
UDC:519.2	Probability. Mathematical statistics
UDC:519.6	Computational mathematics. Numerical analysis
UDC:519.7	Mathematical cybernetics
UDC:519.8	Operational research (OR): mathematical theories and methods
UDC:52	Astronomy. Astrophysics. Space research. Geodesy
UDC:53	Physics
UDC:531/534	Mechanics
UDC:535	Optics
UDC:536	Heat. Thermodynamics. Statistical physics
UDC:537	Electricity. Magnetism. Electromagnetism
UDC:538.9	Condensed matter physics. Solid state physics
UDC:539	Physical nature of matter
UDC:54	Chemistry. Crystallography. Mineralogy
UDC:542	Practical laboratory chemistry. Preparative and experimental chemistry
UDC:543	Analytical chemistry
UDC:544	Physical chemistry
UDC:546	Inorganic chemistry
UDC:547	Organic chemistry
UDC:548/549	Mineralogical sciences. Crystallography. Mineralogy
UDC:55	Earth Sciences. Geological sciences
UDC:550	Earth sciences
UDC:550.2	Geoastronomy. Cosmogony
UDC:550.3	Geophysics
UDC:550.31	Generalities
UDC:550.34	Seismology. Earthquakes in general
UDC:550.38	Terrestrial magnetism (geomagnetism)
UDC:550.4	Geochemistry
UDC:550.42	Occurrence and distribution of chemical elements and their isotopes
UDC:550.424	Migration of chemical elements
UDC:550.47	Biogeochemistry
UDC:550.7	Geobiology. Geological actions of organisms
UDC:550.75	Action of humans
UDC:550.8	Applied geology and geophysics. Geological prospecting and exploration. Interpretation of results
UDC:550.83	Geophysical exploration techniques
UDC:550.93	Geochronology. Geological dating. Determination of absolute geological age
UDC:551	General geology. Meteorology
UDC:551.1	General structure of the Earth
UDC:551.2	Internal geodynamics (endogenous processes)
UDC:551.21	Vulcanicity. Vulcanism. Volcanoes. Eruptive phenomena. Eruptions
UDC:551.23	Fumaroles. Solfataras. Geysers. Hot springs. Mofettes. Carbon dioxide vents. Soffioni
UDC:551.24	Geotectonics
UDC:551.26	Structural-formative zones and geological formations
UDC:551.3	External geodynamics (exogenous processes)
UDC:551.312	Limnic type. Formation by fresh water
UDC:551.32	Glaciology
UDC:551.322	Solid water substance. Ice and snow
UDC:551.324	Land ice. Glaciers
UDC:551.326	Floating ice
UDC:551.35	Marine deposits
UDC:551.4	Geomorphology. Study of the Earth’s physical forms
UDC:551.43	Relief forms of the Earth’s surface. Landforms. Morphostructures
UDC:551.435	Morphosculptures. Relief forms created by exogenous processes. Dynamic and climatic geomorphology
UDC:551.44	Speleology. Caves. Fissures. Underground waters
UDC:551.46	Physical oceanography. Submarine topography. Ocean floor
UDC:551.461	General features. Sea level. Horizontal extent
UDC:551.462	Submarine topography. Sea-floor features
UDC:551.463	Seawater. Physical properties of seawater
UDC:551.465	Structure, dynamics, circulation of the sea
UDC:551.466	Sea waves and tides
UDC:551.5	Meteorology
UDC:551.50	Practical meteorology
UDC:551.51	Physics of the atmosphere. Composition and structure of the atmosphere. Dynamic meteorology
UDC:551.52	Radiation. Temperature
UDC:551.55	Wind and turbulence
UDC:551.57	Aqueous vapour. Hydrometeors
UDC:551.576	Cloud
UDC:551.578	Particular forms of precipitation
UDC:551.581	Theoretical climatology. Climatic zones
UDC:551.582	Climatology of particular places, regions, parts of the Earth
UDC:551.583	Natural variations of climate. Climatic change
UDC:551.584	Mesoclimatology. Microclimatology
UDC:551.585.7	Mountain climates
UDC:551.588	Influence of environment on climate
UDC:551.59	Various phenomena and influences
UDC:551.594	Electrical phenomena in the atmosphere
UDC:551.7	Historical geology. Stratigraphy
UDC:551.8	Palaeogeography
UDC:552.1	Rock characteristics and properties generally. Physical and physicochemical petrology
UDC:552.2	General petrography. Classification of rocks
UDC:552.4	Metamorphic rocks
UDC:552.5	Sedimentary rocks
UDC:552.6	Meteorites
UDC:556	Hydrosphere. Water in general. Hydrology
UDC:556.01	Theory. Principles of research and investigation
UDC:556.04	Observations. Data. Records
UDC:556.06	Hydrological forecasting and forecasts
UDC:556.1	Hydrologic cycle. Properties. Conditions. Global water balance
UDC:556.11	Water properties
UDC:556.12	Precipitation, rainfall, snow etc. (as element in the hydrologic cycle)
UDC:556.3	Groundwater hydrology. Geohydrology. Hydrogeology
UDC:556.31	Properties of groundwater
UDC:556.33	Aquifers. Water-bearing strata
UDC:556.34	Groundwater flow. Well hydraulics
UDC:556.51	Drainage basins. Catchment areas. River basins. Watersheds
UDC:556.52	Potamology. River systems
UDC:556.53	Rivers. Streams. Canals
UDC:556.536	Hydrodynamics of rivers. Fluvial hydraulics
UDC:556.546	Estuarine hydraulics and hydrodynamics
UDC:556.55	Limnology. Lakes. Reservoirs. Ponds
UDC:56	Palaeontology
UDC:57	Biological sciences in general
UDC:574.3	Populations and environment
UDC:58	Botany
UDC:581.5	Habits of plants. Plant behaviour. Plant ecology. Plant ethology. The plant and its environment. Bionomics ...
UDC:59	Zoology
UDC:591.5	Animal habits. Animal behaviour. Ecology. Ethology. Animal and environment. Bionomy