As one example and a case study, in this paper a digital library support of the creation and exploitation of the next generation of Ukrainian mono- and multilingual dictionary resources (UDRs), which is based on Adaptive Resources Model (ARM model) for adaptive UDRs spaces and integration interactions among these spaces is described.

In Ukrainian lingua-information fund of the National Academy of Sciences of Ukraine, an Electronic Library of Ukrainian language and literature (ELUL) is created and is permanently developing with research and industrial partners. It is used for creation and exploitation of UDRs and is based on three logical functional CELL-spaces: collections for constructing of UDRs (C-constructing-space); collections for exploiting of UDRs (E-exploiting-space); and related learning resources (LL- lifelong learning-space). A number of UDRs and use of those resources depend both on the CELL-spaces and interactions among these spaces.

Learning technologies, services, and resources for UDR's community are being created in International Research and Training Center of Information Technologies and Systems UNESCO/IIP. It should be born in mind that the UDRs must be equally accessible to persons with varying levels of knowledge and skills in related fields, including both novices and experts. The lack of UDRs-related skills among the authors and users, the need to enhance awareness of the Internet and its services in the UDRs communities, as well as the transfer of the skills and know-how to make the most effective use of these opportunities, can best be resolved through a virtual learning environment for UDRs communities and integration of this environment into DLS.

The first steps toward the new learning UDRs communities were done within STACCIS project (EC DG XXIII) aimed at strengthening the cooperation in telematics applications in the different areas of virtual education between the European countries of the Commonwealth of Independent States and their counterparts in the European Union. Information and Demonstration Center (IDC) (http://www.dlab.kiev.ua/idc/staccis.htm) of STACCIS in Ukraine provides education and training in telematics applications in various domains, disseminates the new forms of educational software, distance courses, workshops, provides navigation support in information resources search etc. The further steps include the organization on the base of IDC a Multimedia Distance Education Centre (MATEN project, Inco Copernicus) – MDEC, which will serve as a highly flexible virtual environment. The top priorities of this center include distance education and training, courseware production, consulting service. MDEC will provide:

• channels for new knowledge;
• excellent scientific education;
• growing opportunities (consolidate research activities, knowledge transfer and education intermingle);
• an important role in guiding users.

The practice of exploitation and development of ELUL for support of UDR's activity in Ukraine, described by a wide range of resource authors, owners, service providers and users, allowed for identification of the following national level problems of digital library support for creation and exploitation of the next generation of distributed UDRs:

• the lack of a common conceptual basis with flexibility, adaptivity and scalability features,
• the lack a national-distributed infrastructure that might enable a large-scale integration of heterogeneous (and dynamic) UDRs and coordination and development of adaptive UDRs content,
• the lack of continuous learning support for UDR's users with varied levels of knowledge and skills in related fields.

A CELL-space is characterized by the presence of cooperative and competitive processes which, together with the external forces of environment, form the dynamics of the spaces. The sources of this dynamics are: permanent changes of linguistic objects and knowledge about them as well as user's needs; relatively discrete changes of normative base for UDRs; organizational and technical environment of UDRs, etc.

A short and simplified CELL-space dynamics (in a time dimension) is described in a following way. Permanent change of federate distributed linguistic objects and knowledge about them is fixed in dynamic collections of illustration and documentation language fund. New objects of linguistic structure (words, terms, idioms, phraseological units, etc.) are permanently extracted from these collections by formal or heuristic procedures and are added to collections of general fund of structural objects of language. These collections are served as a basis for construction of other UDRs collections. Core of dictionary collections are academic collections (general language, special, terminological, translation), that play a role of standards in UDR. Fragments of these standards must be dynamically disseminated into the other UDRs collections. Academic dictionaries form the basis for the creation of other dictionaries – school, branch, as well as linguistic handbooks, text-books, etc. UDR's collections content has learning resources (and learning standards), including learning objects, that are embedded into other digital object content.

The dynamics CELL-space is characterized by competitive processes and requirements in a number of dimensions and scales:

• time (for example, C-space is permanently changing vs. discrete changes of E-space),
• territories (for example, corporate user's needs vs. connectivity regions for the CELL-space),
• users (for example, learning, linguistics, etc. standards for the CELL-spaces vs. UDR's communities with varying levels of education and skills in related fields), etc.

One of the possible solutions to meet these wide range of requirements is a creation and exploitation of the adaptive digital library CELL-spaces. Essentially different ways of this adaptation we call adaptive services. Therefore, conceptually our approach is digital library support for creation and exploitation of the next generation of UDRs, that is based on ARM model for adaptive UDRs spaces and integration interactions among these spaces.

Conceptually, in ARM model an adaptive DLS (A-DLS) are defined as : (1) a set of inter-related generators of DLS resources. Generators provide the mechanisms for aggregation of access to the sets of adaptive resources and adaptation services in DLS; and (2) a central body for coordination and development of adaptive content in DLS.

Denote generators of A-ADS resources as Gi (i=1, 2,..., n). For each Gi in ARM model the following entities are defined:

• SelectionImport – a set of surrogates for selection of external resources, that are imported into the Gi from broader (outer) space,
• SelectionExport – a set of surrogates for selection of internal resources Gi, that export these resources from the Gi into broader space,
• DisseminatorImport – a set of surrogates (a set of service requests) that release disseminations from broader space into Gi,
• DisseminatorExport – a set of surrogates (a set of service requests) that release disseminations from the Gi into broader space,
• AdaptativeServices – a set of adaptive services for Gi,
• BelongingToClassG – sets of generators of resources, that have equivalent functional and/or structural features,
• SurrogatesAdaptation – set of other surrogates for adaptation of a Gi (and A-DLS).

For A-DLS resources, PairsRelations – a set of pairs relations is defined and described: (the element of SelectionExport for Gi) A-relation with (the element of SelectionImport  for Gj). The A-relation is a distributed active relationship. The combinations of PairsRelations are used to form various trajectories of resources adaptation in DLS.

In A-DLS, a form of Gi is separated from a content of Gi in the following way. A-DLS content is defined by a set of values for respectively, SelectionImport, SelectionExport, DisseminatorImport, DisseminatorExport, AdaptiveServices, and SurrogatesAdaptation.

A resource (child) is considered as adapting to another resource (parent) if for this pair there exists at least one non-empty value from PairRelations. The final result of adaptation of a child to a parent resource in a concrete A-DLS is a change of a state or behavior of a child resource.

In ARM model the DLS will require a central body, that provide mechanisms for:

• coordination and development of DLS adaptive content,
• coordinating, setting and dissemination of a domain independent and/or domain specific standards for the DLS as a whole and for its components,
• identifying and supporting the development of necessary adaptation services (For example, mechanisms for collecting data to study and assess usage of adaptive services),
• self-regulation and self-organization of adaptive content,
• provide adaptation sessions for A-DLS.

Creation of the ARM model for the CELL-space has been performed as follows. For C-space and E-space, interactions collections and their functional and structural components were defined. On the basis of structural theory of the objects of linguistic structure and information and lexicography systems, information and lexicography models and systems have been created, following by adaptive containers and their WFC for the basic structural objects of UDRs from the collections of illustration and documentation fund of the language; collections of the general fund of structural objects of the language; academic dictionary collections (standards for the CELL-space objects).

For example, conceptually, digital dictionary for constructed ARM model is defined as:

• a set of criteria (and/or set of templates) for selecting dictionary content from the broader space;
• set of a metadata and tools for discovery a dictionary content;
• a set of dictionary inter-related objects – concepts and the List of Designations Concepts (LDC);
• dictionary entries, which contain the designation of concept (words, term, etc.) and a set of metadata for this concept that define dictionary microstructure;
• a set of metadata for LDC, which define dictionary macrostructure (for instance, sets of "form" and "content" parts of dictionary entry and relations and links between dictionary entries, composition of the dictionary sub-dictionaries etc.), etc.

A collection in the CELL-space is defined as a set of criteria (and/or set of templates) for selecting UDRs from the broader space which provides metadata and tools for discovery of resources in the collection.

For collections of general fund of structural objects of the language and academic dictionary, a collection is defined as:

• a set of inter-related structural objects of the language and their aggregations;
• a set of metadata types for these objects both on macro- and microstructure level;
• a set of possible aggregations of these metadata on microstructure level;
• possible structural aggregations (disaggregations) for each metadata type, as well as their interaction.

After creation of functional and structural components of the CELL-space, necessary surrogates (and their meanings), SelectionImport and SelectionExport for each Gi from the A-DLS for the CELL-space and others were constructed .

Currently, a number of adaptive services to adapt resources of some collections of the CELL-spaces to each other have been implemented. These services are intended for the use within National Ukrainian linguistic network which is currently under construction.

This paper has presented adaptive resources model (ARM) for a digital library space. Such resources have a dynamic state and behavior. It may be characterized by the presence of cooperative and competitive processes which, together with the external forces of the environment, form the dynamics of the space in a number of dimensions and scales. The main idea of ARM model construction is to use information and other surrogates of the resources. Conceptually, in ARM model an adaptive digital library space is defined as: (1) a set of inter-related generators of resources. Generators provides the mechanisms for the aggregation of access to the sets of adaptive resources and adaptation services in this space; and (2) a central body for coordination and development of adaptive content.

The ARM model provides a rich conceptual structure for the development of adaptive resources and services. Large-scale test-case ARM model now is implemented in Ukraine for adaptive digital library support for the creation and exploitation of the next generation of Ukrainian mono- and multilingual dictionary resources. Now we see the following benefits and advantages of ARM model for our digital library space:

• improved adaptivity and development of content,
• improved coordination and development of adaptive content,
• improved interoperability of adaptive content,
• improved dissemination of standard content,
• flexibility and scalability.

The work that is described here is based on the experience with the Electronic Library of Ukrainian language and literature, which now contains components of three spaces: (1) collections original Ukrainian texts and system components for constructing dictionaries, (2) collections of an Ukrainian dictionaries, (3) learning resources for users with varying levels of education and skills in related fields.