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Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment
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Means for Ensuring Compatibility of Cognitive Heterogeneous 8th Annual International Conference on Biologically Inspired Architectures,Data BICA 2017 Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment Means for Ensuring Compatibility of Heterogeneous Data Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment Means for Ensuring Compatibility of Heterogeneous Data 1 1 2 Models in an Interactive Environment Viacheslav E. Wolfengagen , LarisaVisualization Yu. Ismailova , Sergey V. Kosikov , Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment 2Interactive 2 2 Models in an Visualization Environment 1 1 2 , Irina A. Parfenova and Viktor A. Kcholodov Ilya A. E. Nikulin Viacheslav Wolfengagen , Larisa Yu. ,Ismailova , Sergey V. Kosikov2 , 1 1 Models in an Visualization Environment Viacheslav E. Wolfengagen , Larisa Yu. V. Kosikov 2Interactive 2 Ismailova1 , Sergey 2 1 2, Ilya A. Nikulin , Irina A. Parfenova , and Viktor A. Kcholodov
Viacheslav Wolfengagen Yu. Kosikov 1 ,”MEPhI” 1 , Sergey 2, National Research Nuclear (Moscow Engineering PhysicsV. Institute), 2University 2 Ismailova 2 Moscow, Viacheslav E. Wolfengagen , Larisa Larisa Yu. , Sergey V. Kosikov , , Irina A. Parfenova ,Ismailova and Viktor A. Kcholodov Ilya A. E. Nikulin 2 2 2 1 1 2 115409 RF 1 ,, Irina A. Parfenova and Viktor A. Kcholodov Ilya A. Nikulin 2University 2 ,Ismailova 2 Moscow, Viacheslav E. Wolfengagen ,”MEPhI” Larisa Yu. , Sergey V. Kosikov , National Research Nuclear (Moscow Engineering Physics Institute), Irina A. Parfenova , and Viktor A. Kcholodov Ilya A. Nikulin 1 [email protected], [email protected] 2University ”MEPhI” 2 2 Moscow, National Research Nuclear (Moscow Engineering Physics Institute), 115409 RF , Irina A.Education Parfenova , and ViktorMoscow, A. Kcholodov Ilya A. Nikulin 2 1 Institute for Contemporary ”JurInfoR-MGU”, 119435 RF Moscow, Nuclear University ”MEPhI” (Moscow Engineering Physics Institute), 1 National Research 115409 RF [email protected], [email protected] National Research Nuclear University ”MEPhI” (Moscow Engineering Physics Institute), Moscow, [email protected] 115409 RF 2 1 [email protected], [email protected] Institute Nuclear for Contemporary ”JurInfoR-MGU”, Moscow, RF Moscow, 115409(Moscow RF National Research University Education ”MEPhI” Engineering Physics119435 Institute), 2 [email protected], [email protected] Institute for Contemporary Education ”JurInfoR-MGU”, Moscow, 119435 RF [email protected] [email protected], [email protected] 115409 RF 2 Education ”JurInfoR-MGU”, Moscow, 119435 RF 2 Institute for Contemporary [email protected] Institute for Contemporary Education ”JurInfoR-MGU”, Moscow, 119435 RF [email protected], [email protected] [email protected] 2 [email protected] Institute for Contemporary Education ”JurInfoR-MGU”, Moscow, 119435 RF Abstract [email protected] 1
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The methods of working with interfor informational support for the implementation of the best available technologies (BAT). Keywords: heterogeneous information, semantic data interpretation, interpretation con© 2018 Thecoordination Authors. Published byhave Elsevier Ltd.partially This characteristics, is antested open access article under the CC BY-NC-ND license pretation tools been when implementing various applications for informational support for the implementation of the best available technologies (BAT). which semantic characteristics of processed data appear. The methods of working with interpretation coordination tools have been partially tested when implementing various applications (http://creativecommons.org/licenses/by-nc-nd/3.0/). trol, multi heterogeneous model, support model information, compatibility, visualizing heterogeneous information, visualization support for informational for the implementation of the best available technologies (BAT). Keywords: semantic characteristics, data interpretation, interpretation conpretation coordination tools have been partially tested implementing various applications for informational support for thescientific implementation of the thewhen best available technologies (BAT). Peer-review under responsibility ofenvironment the committee of 8th Annual International Conference on contools, interactive visualization Keywords: heterogeneous information, semantic characteristics, data interpretation, interpretation trol, multi Inspired model, support model compatibility, visualizing heterogeneous information, visualization support Biologically Cognitive Architectures for informational for the implementation of the best available technologies (BAT). Keywords: heterogeneous information, semantic characteristics, data interpretation, interpretation control, model,visualization model information, compatibility, visualizing heterogeneous visualization support Keywords: heterogeneous semantic characteristics, datainformation, interpretation, interpretation contools,multi interactive environment trol, multi model,visualization model compatibility, visualizing heterogeneous visualization support tools, interactive environment Keywords: heterogeneous semantic characteristics, datainformation, interpretation, interpretation control, multi model, model information, compatibility, visualizing heterogeneous information, visualization support tools, interactive environment trol, multi model,visualization model compatibility, visualizing heterogeneous information, visualization support tools, interactive visualization environment 1 Introduction tools, interactive visualization environment
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When processing heterogeneous data in the whole and when visualizing it in particular, it takes considerable effort to harmonize the models of the data being processed. In general case the data model defines the structures of the data being processed, the methods for their processing as well as the constraints imposed on them. When setting them in different models, as a rule, not only different language agreements are used, but different, often difficultly compatible representations about the nature of data processing are made, which makes it difficult to solve the problem of model matching. Coordination of models of data, including for their visualization, assumes establishment of interrelations of interpretations of the relevant data. In the most general setting this task represents version of the task of establishment of equivalence of algorithms and therefore is algorithmically undecidable task. Therefore the specifications of the task allowing to select rather wide classes of special cases of establishment of the considered mappings are of the considerable interest. The method of data interpretation is one of their properties, and as such can be conceptualized and processed in terms, used in general processes of the data characterization. To increase the flexibility of the interpretation control system the essential meaning acquire the possibilities to establish links between the characteristics of the data interpretation (in particular, the rights for access for different classes of users) and other characteristics, obtained as a result of data analysis, their classification and so on. The use of various methods and the criteria of classification, including those that are dynamically computed, provides the opportunities of flexible interpretation control. The interpretation restrictions ultimately are determined by the meaning of observed data that allows include the task of managing data interpretation into the common context of the development of methods of data processing semantically oriented. In this area the methods of conceptual modelling are acknowledged to be good [1]. The inclusion of control of interpretation means into a common conceptual model of data provides the fundamental possibility of taking into account the semantic data characteristics when solving the task of interpretation control, which, in its turn, can count on getting rather flexible methods of building of object-relational mapping. The semantic characteristics of data, when organizing the interpretation of them, can be accounted in different way. It is possible to bind specific means of data interpretation control with conceptual classes, singled out in models, with frames and other conceptual essences. On this way, in particular, conceptual models can be built similar in features to classic means of object-oriented modelling [2]. It is also possible to use the model parametrization with the definition of tools for manipulating data, depending on the parameter or sets of parameters. Various methods of interpretation can also be connected with the peculiarities of the data model chosen for the presentation of data within a common conceptual model. For example, in the case when the classical relational model is used the main structure is relation. Accordingly, interpretations can be specified in the form of expressions over relationships or their structural elements (e.g., columns): ways of visibility in the form of formulas for determining visible relationships, and methods of manipulating in the form of specialization of operations for manipulating relationships. The more powerful and transparent mathematical model of the data is, the finer restrictions may be expressed, and the more uniform way of expression appears to be. Thus, in the case of network model of the data the limitations, as a rule, are associated with the selections in general graph, representing the semantic network, of subgraphs of a special type. In the case of database without schemes the limitations can be expressed by fragments of software code. In the 2
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case of a combination of several models of data, the selected methods of interpretation control support may take into account the peculiarities of both individual models and the relationships between them. The most important stage is the identification of similar structures in different models. The degree of correspondence of the found subgraphs to the user’s request depends on the way of determining such structures. The presence of variational similarity criteria makes it possible to carry out ”semantic filtering” of the visualized information. Essentially a large class of tasks implemented in the Web is brought to the construction of acceptable mechanisms of semantic filtering by ensuring the compatibility of heterogeneous data models [3]. The data, in this case, can be obtained from multiple sources. At the same time both the syntax characteristics of data and their semantic structure, generally speaking, are not harmonized with each other. The syntactic features mean not only the format of the data (for example, the HTML), but also other characteristics specified in the framework of the used format (e.g., in the case of submission of a collection of articles the authors of a separate article can be named before the title of the article, or after it, and so on). In addition, the data may be irrelevant or insufficiently relevant to solve the task for which they are selected. The problems, appeared in this case, may be interpreted as limitation of interpretation of a specific type.
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Support for Model Compatibility in an Interactive Visualization Environment
The analysis of approaches to the problem of ensuring the consistency of heterogeneous data models in an interactive visualization environment of establishing the semantic similarity of graph structures shows that it is of interest to consider the following factors: • ability to link semantic information with visual elements of the information representation; • use in the course of visualization of meta-information, including specified in the form of specialized properties of visualized objects within the submodels of their representation; • ability to take into account the conceptual dependencies of domain objects models; • ability to work with composite models obtained as a result of the use of coordination operations. Special interest in the harmonization of models causes the possibility of using coordination tools in the form of models of conceptual dependencies, in particular, frame networks. A network of frames is an information graph - a semantic network - with a fairly rich structure. In particular, on the frames, a relationship can be established that defines the inclusion of entity classes - the ISA-hierarchy. In addition, the arcs of frames can be associated with quantifier constructions having a specific semantics. An adequate semantic mechanism that determines the possibility to coordinate the fragments of models described in the form of frame networks is fitting, which raises interest in studying the possibility of using the fitting when harmonizing data models for visualization purposes. The semantic network will be regarded as consisting of concepts and frames. We’ll consider the primary concepts, among which we will highlight the general concepts and constants, and derivative concepts derived from the (previously defined) concepts and frames with the help of the operation of definition. Among the frames we’ll allocate frames simple and complex frames received from the simple ones using operations with frames [3, 4]. 3
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The semantic network allows to describe data and data collections, and also to highlight their semantic characteristics, that is done on the basis of a certain type of frames (characteristic frames). These frames allow to link the general concepts that describe some of the classes of data, selected in the subject area, with concepts that describe sets of values of their characteristics, and to connected the specific data (presented in a semantic network as constants) with specific values of characteristics. One of the basic operations with the semantic network is an operation of determination, which allow to consider the expressions, specified by the combination of constructions of semantic network, as atomic, i.e., similar to the primary ones. This operation can be considered as a variant of the abstraction of structures of semantic network. The network as a whole at the same time receives a character of multiply nested structure. The substitution operation may be considered as the reverse operation; such operation allows to determine an object of the semantic network, correlating with the concept, considered as a place for substitution (concept - variable). The substitution, thus, defines the specialization of the semantic network by imposing restrictions on the values of some set of nodes of network. It is essential that not only atomic object may be substituted, but also a composite object. The essential characteristic of the semantic network of the considered type is the possibility of its nesting in the applicative computing system. This nesting makes it possible to compute the semantic characteristics by determining the mapping value or the evaluating map. The result of computation - value - can also be represented as an object of a semantic network. The applicative nature of the network provides, in particular, for the possibility of network processing by means of the network itself, i.e., self-applicability of the network. Depending on the choice of comprehensive applicative computing system greater or lesser degree of selfapplicability might be possible. In particular, it is possible to apply the means of determination of semantic characteristics and the appropriate means of classification to the structures of the network. On this basis it is possible to determine the semantic construction, which provides a description of possible methods of access to the value contained in the semantic network, and methods of manipulation of that value. Technically, such construction may present a concept that describes a set of frames that define the means of access and manipulation. This construction, however, by itself does not provide flexible methods of data interpretation, and can only be regarded as a basic building block for the description of corresponding means for interpretation. To achieve the flexibility requires parametrization of the considered structure, i.e., provision of dependence for a set of the available interpretation methods on the parameters, which are mainly the semantic characteristics of the data being processed. The study of the possibilities of specifying interpreting methods in the semantic network leads to the possibility of setting the task of supporting the definition of object-relational mapping of data as the task of developing methods for the specialized coordination of semantic network constructions, means of support for harmonization and methods of using it. Methods of specialized coordination should provide: • determination of ways to harmonize data and / or data groups that are homogeneous in one way or another, including those on the basis of the semantic characteristics assigned to them; • definition of matching methods as objects of the semantic network, which can be built into objects specifying data interpretation of a more general type; • a description of the procedures for the mutual matching of policies of harmonization methods; 4
Means for ensuring compatibility . . E. . Wolfengagen Wolfengagen, Ismailova, Kosikov, Nikulin, Parfenova and Kcholodov Viacheslav et al. / Procedia Computer Science 123 (2018) 195–202
• definition of global constraints on the system of harmonization methods, including those describing consistency and completeness, and ways to meet these constraints. Tools for supporting harmonization methods should provide: • setting the harmonization methods as objects of the semantic network, including fixing the context for determining the method of matching and restoring the context when applying the appropriate method; • editing the harmonization methods, adding them to the system and removing them from the system; • a multilevel composition of harmonization methods, providing both expansion and narrowing of the corresponding data interpretation capabilities; • definition and verification of global restrictions for data changes, determined by a given set of matching methods. The methods of using coordination methods should provide the following possibilities: • definition of coordination methods templates oriented to solve typical problems of the data coordination; • linking the coordination methods with the general methods of data interpretation and their integration into such methods, which is essential, in particular, for the automated preparation of documentation; • debugging data coordination methods, including visualization of the results of harmonization methods application. The solution of the problem to support the harmonisation of data models relies on the specific features of the encompassing semantic network. Essential is appeared to be the applicative nature of the network, which provides the construction of specialized interpreters or abstract machines that provide access to fragments of the semantic network, satisfying the given limitations.
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Applicative Methods to Represent the Models
The models being coordinated are represented as formal applicative systems. The basic definition is the applicative structure M = M, · , where ‘·’ is a binary operation over M . The combinatorial algebra is an applicative structure, where exist k, s ∈ M such that (k · x) · y = x, ((s · x) · y) · z = (x · z) · (y · z) for all x, y, z ∈ M . It is possible to model a lambda abstraction in a combinatory algebra M using k and s. So it is possible to build an interpretation of lambda terms in M . 5
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Let C be a set of constants. A set of lambda terms (possibly) containing constants from C is denoted Λ(C). If M = (X, ·, k, s) is an applicative structure then Λ(M ) is by definition Λ(X). Let M be a combinatory algebra. Then coordinating models can be represented in the lambda or combinatorial form. It is possible to establish mappings CL : Λ(M ) → T (M ), defined as follows:
Λ : T (M ) → Λ(M ),
xCL = x cCL = c (M N )CL = MCL NCL (λx.M )CL = λ∗ x.MCL
and
xΛ = x KΛ = λxy.x SΛ = λxyz.xz(yz) cΛ = c (M N )Λ = MΛ NΛ
For M, N ∈ Λ(M ) by definition [M ]ρ = [MCL ]ρ M, ρ |= M = N ⇔ [M ]ρ = [N ]ρ M |= M = N ⇔ M, ρ |= M = N ∀ρ where ρ is the computational environment. Lambda algebras are defined as combinatory algebras M where AΛ = BΛ ⇒ M |= A = B for all A, B ∈ T (M ). Proposition. Let M be a combinatory algebra. M is a lambda algebra if and only if for all M, N ∈ Λ(M ): (i) M = N follows M |= M = N ; (ii) M |= KΛ,CL = K, M |= SΛ,CL = S.
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Visualization Support Tools
The methods of working with interpretation coordination tools have been partially tested when implementing various applications for informational support for the implementation of the best available technologies (BAT). Most clearly methods to support multi-model visualization due to the harmonization of heterogeneous models were used in the editor of conceptual descriptions of the domain. The editor of conceptual descriptions is oriented to the description of concepts of a rather general structure. Description of the concept is accompanied by a description of the characteristic frames of the given concept. Arguments of characteristic frames can be either simple concepts or the results of conceptual operations. The editor provides two metalanguages, one of which is intended to describe the model of a given class of editable concepts, and the other specifies the description methods to visualize 6
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Partial model by Expert1
Partial model by Expert2
Visual model
Visual model
Graphical images
Graphical images
Binding objects
Binding objects
Adjusting objects
Adjusting objects
Restrictions
Restrictions
Operations
Operations
Problem domain data
Problem domain data
Coordinated model Rules of choice Rules of composition Rules of coordination Rules of blending
Visual model Graphical images Binding objects Adjusting objects
Restrictions Operations Problem domain data
Figure 1: The coordination of partial models for learn-methodical complex the objects of the model. Based on the description of the model corresponding to the set of concepts, a coordinated description of the heterogeneous model is developed taking into account the structure of the concepts. It is possible to select concepts according to the condition specified in the framework of the agreed model, as well as the change in the concept according to the rule that depends on the harmonization method. Separate mechanisms for harmonizing heterogeneous models were also tested when creating a training and methodological complex on the legal basis for the introduction of BAT. One of the information elements of the complex was the training program. Since the BAT implementation is an integrated branch of law, the program, during the development of the complex, included fragments that differ in presentation mode and in semantic characteristics. As separate methods of presentation caused methodological interest, a data structure was chosen that allows to represent the program elements in a multi-model way. A set of interface elements providing navigation through a multi-model structure was also proposed. The coordination of partial models for learn-methodical complex is shown on Figure 1. On the whole the approbation showed simultaneously a significant expansion of the capabilities of application systems, arising from the introduction of model matching capabilities in them, and the lack of elaboration of methods for describing and manipulating multi-model objects, making it difficult to develop means for their visualization, navigation and interpretation. As expected, a number of problems can be overcome through the use of applicative systems with a richer internal structure.
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Conclusion
This paper proposes an approach to solving the problem of ensuring the consistency of heterogeneous data models in an interactive visualization environment based on the use of conceptual dependencies. Here the coordinated models are immersed in a specialized environment of representation of dependencies on the basis of applicative computing systems. The correctness of 7
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constructing support means is provided by immersing the model of semantic network processing in the application computing system. The proposed method of construction provides, in particular, the following possibilities: • possibility of parametrization of models matching means by taking into account the semantic characteristics of the data; • possibility of describing various ways of coordination taking into account various aspects of data consideration; • possibility of achieving compatibility of models, including those aimed at different classes of users, which leads to the possibility of dynamic formation of user coalitions based on common fragments of models. The elements of the proposed approach were tested when developing the information systems that support the promotion of institutional foundations for the introduction of the best available technologies in the Russian Federation. The approbation demonstrated the possibility of achieving the set goals, which determines the practical significance of the proposed approach.
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Acknowledgements
Authors acknowledge support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005). The research is supported in part by the RFBR grants 16-07-00892, 17-0700893 and 16-07-00909, 16-07-00914, 16-07-00912, 15-07-06933, 15-07-06898 .
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Means for Ensuring Compatibility of Cognitive Heterogeneous 8th Annual International Conference on Biologically Inspired Architectures,Data BICA 2017 Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment Means for Ensuring Compatibility of Heterogeneous Data Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment Means for Ensuring Compatibility of Heterogeneous Data 1 1 2 Models in an Interactive Environment Viacheslav E. Wolfengagen , LarisaVisualization Yu. Ismailova , Sergey V. Kosikov , Means for Ensuring Compatibility of Heterogeneous Data Models in an Interactive Visualization Environment 2Interactive 2 2 Models in an Visualization Environment 1 1 2 , Irina A. Parfenova and Viktor A. Kcholodov Ilya A. E. Nikulin Viacheslav Wolfengagen , Larisa Yu. ,Ismailova , Sergey V. Kosikov2 , 1 1 Models in an Visualization Environment Viacheslav E. Wolfengagen , Larisa Yu. V. Kosikov 2Interactive 2 Ismailova1 , Sergey 2 1 2, Ilya A. Nikulin , Irina A. Parfenova , and Viktor A. Kcholodov
Viacheslav Wolfengagen Yu. Kosikov 1 ,”MEPhI” 1 , Sergey 2, National Research Nuclear (Moscow Engineering PhysicsV. Institute), 2University 2 Ismailova 2 Moscow, Viacheslav E. Wolfengagen , Larisa Larisa Yu. , Sergey V. Kosikov , , Irina A. Parfenova ,Ismailova and Viktor A. Kcholodov Ilya A. E. Nikulin 2 2 2 1 1 2 115409 RF 1 ,, Irina A. Parfenova and Viktor A. Kcholodov Ilya A. Nikulin 2University 2 ,Ismailova 2 Moscow, Viacheslav E. Wolfengagen ,”MEPhI” Larisa Yu. , Sergey V. Kosikov , National Research Nuclear (Moscow Engineering Physics Institute), Irina A. Parfenova , and Viktor A. Kcholodov Ilya A. Nikulin 1 [email protected], [email protected] 2University ”MEPhI” 2 2 Moscow, National Research Nuclear (Moscow Engineering Physics Institute), 115409 RF , Irina A.Education Parfenova , and ViktorMoscow, A. Kcholodov Ilya A. Nikulin 2 1 Institute for Contemporary ”JurInfoR-MGU”, 119435 RF Moscow, Nuclear University ”MEPhI” (Moscow Engineering Physics Institute), 1 National Research 115409 RF [email protected], [email protected] National Research Nuclear University ”MEPhI” (Moscow Engineering Physics Institute), Moscow, [email protected] 115409 RF 2 1 [email protected], [email protected] Institute Nuclear for Contemporary ”JurInfoR-MGU”, Moscow, RF Moscow, 115409(Moscow RF National Research University Education ”MEPhI” Engineering Physics119435 Institute), 2 [email protected], [email protected] Institute for Contemporary Education ”JurInfoR-MGU”, Moscow, 119435 RF [email protected] [email protected], [email protected] 115409 RF 2 Education ”JurInfoR-MGU”, Moscow, 119435 RF 2 Institute for Contemporary [email protected] Institute for Contemporary Education ”JurInfoR-MGU”, Moscow, 119435 RF [email protected], [email protected] [email protected] 2 [email protected] Institute for Contemporary Education ”JurInfoR-MGU”, Moscow, 119435 RF Abstract [email protected] 1
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The methods of working with interfor informational support for the implementation of the best available technologies (BAT). Keywords: heterogeneous information, semantic data interpretation, interpretation con© 2018 Thecoordination Authors. Published byhave Elsevier Ltd.partially This characteristics, is antested open access article under the CC BY-NC-ND license pretation tools been when implementing various applications for informational support for the implementation of the best available technologies (BAT). which semantic characteristics of processed data appear. The methods of working with interpretation coordination tools have been partially tested when implementing various applications (http://creativecommons.org/licenses/by-nc-nd/3.0/). trol, multi heterogeneous model, support model information, compatibility, visualizing heterogeneous information, visualization support for informational for the implementation of the best available technologies (BAT). Keywords: semantic characteristics, data interpretation, interpretation conpretation coordination tools have been partially tested implementing various applications for informational support for thescientific implementation of the thewhen best available technologies (BAT). Peer-review under responsibility ofenvironment the committee of 8th Annual International Conference on contools, interactive visualization Keywords: heterogeneous information, semantic characteristics, data interpretation, interpretation trol, multi Inspired model, support model compatibility, visualizing heterogeneous information, visualization support Biologically Cognitive Architectures for informational for the implementation of the best available technologies (BAT). Keywords: heterogeneous information, semantic characteristics, data interpretation, interpretation control, model,visualization model information, compatibility, visualizing heterogeneous visualization support Keywords: heterogeneous semantic characteristics, datainformation, interpretation, interpretation contools,multi interactive environment trol, multi model,visualization model compatibility, visualizing heterogeneous visualization support tools, interactive environment Keywords: heterogeneous semantic characteristics, datainformation, interpretation, interpretation control, multi model, model information, compatibility, visualizing heterogeneous information, visualization support tools, interactive environment trol, multi model,visualization model compatibility, visualizing heterogeneous information, visualization support tools, interactive visualization environment 1 Introduction tools, interactive visualization environment
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When processing heterogeneous data in the whole and when visualizing it in particular, it takes considerable effort to harmonize the models of the data being processed. In general case the data model defines the structures of the data being processed, the methods for their processing as well as the constraints imposed on them. When setting them in different models, as a rule, not only different language agreements are used, but different, often difficultly compatible representations about the nature of data processing are made, which makes it difficult to solve the problem of model matching. Coordination of models of data, including for their visualization, assumes establishment of interrelations of interpretations of the relevant data. In the most general setting this task represents version of the task of establishment of equivalence of algorithms and therefore is algorithmically undecidable task. Therefore the specifications of the task allowing to select rather wide classes of special cases of establishment of the considered mappings are of the considerable interest. The method of data interpretation is one of their properties, and as such can be conceptualized and processed in terms, used in general processes of the data characterization. To increase the flexibility of the interpretation control system the essential meaning acquire the possibilities to establish links between the characteristics of the data interpretation (in particular, the rights for access for different classes of users) and other characteristics, obtained as a result of data analysis, their classification and so on. The use of various methods and the criteria of classification, including those that are dynamically computed, provides the opportunities of flexible interpretation control. The interpretation restrictions ultimately are determined by the meaning of observed data that allows include the task of managing data interpretation into the common context of the development of methods of data processing semantically oriented. In this area the methods of conceptual modelling are acknowledged to be good [1]. The inclusion of control of interpretation means into a common conceptual model of data provides the fundamental possibility of taking into account the semantic data characteristics when solving the task of interpretation control, which, in its turn, can count on getting rather flexible methods of building of object-relational mapping. The semantic characteristics of data, when organizing the interpretation of them, can be accounted in different way. It is possible to bind specific means of data interpretation control with conceptual classes, singled out in models, with frames and other conceptual essences. On this way, in particular, conceptual models can be built similar in features to classic means of object-oriented modelling [2]. It is also possible to use the model parametrization with the definition of tools for manipulating data, depending on the parameter or sets of parameters. Various methods of interpretation can also be connected with the peculiarities of the data model chosen for the presentation of data within a common conceptual model. For example, in the case when the classical relational model is used the main structure is relation. Accordingly, interpretations can be specified in the form of expressions over relationships or their structural elements (e.g., columns): ways of visibility in the form of formulas for determining visible relationships, and methods of manipulating in the form of specialization of operations for manipulating relationships. The more powerful and transparent mathematical model of the data is, the finer restrictions may be expressed, and the more uniform way of expression appears to be. Thus, in the case of network model of the data the limitations, as a rule, are associated with the selections in general graph, representing the semantic network, of subgraphs of a special type. In the case of database without schemes the limitations can be expressed by fragments of software code. In the 2
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case of a combination of several models of data, the selected methods of interpretation control support may take into account the peculiarities of both individual models and the relationships between them. The most important stage is the identification of similar structures in different models. The degree of correspondence of the found subgraphs to the user’s request depends on the way of determining such structures. The presence of variational similarity criteria makes it possible to carry out ”semantic filtering” of the visualized information. Essentially a large class of tasks implemented in the Web is brought to the construction of acceptable mechanisms of semantic filtering by ensuring the compatibility of heterogeneous data models [3]. The data, in this case, can be obtained from multiple sources. At the same time both the syntax characteristics of data and their semantic structure, generally speaking, are not harmonized with each other. The syntactic features mean not only the format of the data (for example, the HTML), but also other characteristics specified in the framework of the used format (e.g., in the case of submission of a collection of articles the authors of a separate article can be named before the title of the article, or after it, and so on). In addition, the data may be irrelevant or insufficiently relevant to solve the task for which they are selected. The problems, appeared in this case, may be interpreted as limitation of interpretation of a specific type.
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Support for Model Compatibility in an Interactive Visualization Environment
The analysis of approaches to the problem of ensuring the consistency of heterogeneous data models in an interactive visualization environment of establishing the semantic similarity of graph structures shows that it is of interest to consider the following factors: • ability to link semantic information with visual elements of the information representation; • use in the course of visualization of meta-information, including specified in the form of specialized properties of visualized objects within the submodels of their representation; • ability to take into account the conceptual dependencies of domain objects models; • ability to work with composite models obtained as a result of the use of coordination operations. Special interest in the harmonization of models causes the possibility of using coordination tools in the form of models of conceptual dependencies, in particular, frame networks. A network of frames is an information graph - a semantic network - with a fairly rich structure. In particular, on the frames, a relationship can be established that defines the inclusion of entity classes - the ISA-hierarchy. In addition, the arcs of frames can be associated with quantifier constructions having a specific semantics. An adequate semantic mechanism that determines the possibility to coordinate the fragments of models described in the form of frame networks is fitting, which raises interest in studying the possibility of using the fitting when harmonizing data models for visualization purposes. The semantic network will be regarded as consisting of concepts and frames. We’ll consider the primary concepts, among which we will highlight the general concepts and constants, and derivative concepts derived from the (previously defined) concepts and frames with the help of the operation of definition. Among the frames we’ll allocate frames simple and complex frames received from the simple ones using operations with frames [3, 4]. 3
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The semantic network allows to describe data and data collections, and also to highlight their semantic characteristics, that is done on the basis of a certain type of frames (characteristic frames). These frames allow to link the general concepts that describe some of the classes of data, selected in the subject area, with concepts that describe sets of values of their characteristics, and to connected the specific data (presented in a semantic network as constants) with specific values of characteristics. One of the basic operations with the semantic network is an operation of determination, which allow to consider the expressions, specified by the combination of constructions of semantic network, as atomic, i.e., similar to the primary ones. This operation can be considered as a variant of the abstraction of structures of semantic network. The network as a whole at the same time receives a character of multiply nested structure. The substitution operation may be considered as the reverse operation; such operation allows to determine an object of the semantic network, correlating with the concept, considered as a place for substitution (concept - variable). The substitution, thus, defines the specialization of the semantic network by imposing restrictions on the values of some set of nodes of network. It is essential that not only atomic object may be substituted, but also a composite object. The essential characteristic of the semantic network of the considered type is the possibility of its nesting in the applicative computing system. This nesting makes it possible to compute the semantic characteristics by determining the mapping value or the evaluating map. The result of computation - value - can also be represented as an object of a semantic network. The applicative nature of the network provides, in particular, for the possibility of network processing by means of the network itself, i.e., self-applicability of the network. Depending on the choice of comprehensive applicative computing system greater or lesser degree of selfapplicability might be possible. In particular, it is possible to apply the means of determination of semantic characteristics and the appropriate means of classification to the structures of the network. On this basis it is possible to determine the semantic construction, which provides a description of possible methods of access to the value contained in the semantic network, and methods of manipulation of that value. Technically, such construction may present a concept that describes a set of frames that define the means of access and manipulation. This construction, however, by itself does not provide flexible methods of data interpretation, and can only be regarded as a basic building block for the description of corresponding means for interpretation. To achieve the flexibility requires parametrization of the considered structure, i.e., provision of dependence for a set of the available interpretation methods on the parameters, which are mainly the semantic characteristics of the data being processed. The study of the possibilities of specifying interpreting methods in the semantic network leads to the possibility of setting the task of supporting the definition of object-relational mapping of data as the task of developing methods for the specialized coordination of semantic network constructions, means of support for harmonization and methods of using it. Methods of specialized coordination should provide: • determination of ways to harmonize data and / or data groups that are homogeneous in one way or another, including those on the basis of the semantic characteristics assigned to them; • definition of matching methods as objects of the semantic network, which can be built into objects specifying data interpretation of a more general type; • a description of the procedures for the mutual matching of policies of harmonization methods; 4
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• definition of global constraints on the system of harmonization methods, including those describing consistency and completeness, and ways to meet these constraints. Tools for supporting harmonization methods should provide: • setting the harmonization methods as objects of the semantic network, including fixing the context for determining the method of matching and restoring the context when applying the appropriate method; • editing the harmonization methods, adding them to the system and removing them from the system; • a multilevel composition of harmonization methods, providing both expansion and narrowing of the corresponding data interpretation capabilities; • definition and verification of global restrictions for data changes, determined by a given set of matching methods. The methods of using coordination methods should provide the following possibilities: • definition of coordination methods templates oriented to solve typical problems of the data coordination; • linking the coordination methods with the general methods of data interpretation and their integration into such methods, which is essential, in particular, for the automated preparation of documentation; • debugging data coordination methods, including visualization of the results of harmonization methods application. The solution of the problem to support the harmonisation of data models relies on the specific features of the encompassing semantic network. Essential is appeared to be the applicative nature of the network, which provides the construction of specialized interpreters or abstract machines that provide access to fragments of the semantic network, satisfying the given limitations.
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Applicative Methods to Represent the Models
The models being coordinated are represented as formal applicative systems. The basic definition is the applicative structure M = M, · , where ‘·’ is a binary operation over M . The combinatorial algebra is an applicative structure, where exist k, s ∈ M such that (k · x) · y = x, ((s · x) · y) · z = (x · z) · (y · z) for all x, y, z ∈ M . It is possible to model a lambda abstraction in a combinatory algebra M using k and s. So it is possible to build an interpretation of lambda terms in M . 5
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Let C be a set of constants. A set of lambda terms (possibly) containing constants from C is denoted Λ(C). If M = (X, ·, k, s) is an applicative structure then Λ(M ) is by definition Λ(X). Let M be a combinatory algebra. Then coordinating models can be represented in the lambda or combinatorial form. It is possible to establish mappings CL : Λ(M ) → T (M ), defined as follows:
Λ : T (M ) → Λ(M ),
xCL = x cCL = c (M N )CL = MCL NCL (λx.M )CL = λ∗ x.MCL
and
xΛ = x KΛ = λxy.x SΛ = λxyz.xz(yz) cΛ = c (M N )Λ = MΛ NΛ
For M, N ∈ Λ(M ) by definition [M ]ρ = [MCL ]ρ M, ρ |= M = N ⇔ [M ]ρ = [N ]ρ M |= M = N ⇔ M, ρ |= M = N ∀ρ where ρ is the computational environment. Lambda algebras are defined as combinatory algebras M where AΛ = BΛ ⇒ M |= A = B for all A, B ∈ T (M ). Proposition. Let M be a combinatory algebra. M is a lambda algebra if and only if for all M, N ∈ Λ(M ): (i) M = N follows M |= M = N ; (ii) M |= KΛ,CL = K, M |= SΛ,CL = S.
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Visualization Support Tools
The methods of working with interpretation coordination tools have been partially tested when implementing various applications for informational support for the implementation of the best available technologies (BAT). Most clearly methods to support multi-model visualization due to the harmonization of heterogeneous models were used in the editor of conceptual descriptions of the domain. The editor of conceptual descriptions is oriented to the description of concepts of a rather general structure. Description of the concept is accompanied by a description of the characteristic frames of the given concept. Arguments of characteristic frames can be either simple concepts or the results of conceptual operations. The editor provides two metalanguages, one of which is intended to describe the model of a given class of editable concepts, and the other specifies the description methods to visualize 6
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Partial model by Expert1
Partial model by Expert2
Visual model
Visual model
Graphical images
Graphical images
Binding objects
Binding objects
Adjusting objects
Adjusting objects
Restrictions
Restrictions
Operations
Operations
Problem domain data
Problem domain data
Coordinated model Rules of choice Rules of composition Rules of coordination Rules of blending
Visual model Graphical images Binding objects Adjusting objects
Restrictions Operations Problem domain data
Figure 1: The coordination of partial models for learn-methodical complex the objects of the model. Based on the description of the model corresponding to the set of concepts, a coordinated description of the heterogeneous model is developed taking into account the structure of the concepts. It is possible to select concepts according to the condition specified in the framework of the agreed model, as well as the change in the concept according to the rule that depends on the harmonization method. Separate mechanisms for harmonizing heterogeneous models were also tested when creating a training and methodological complex on the legal basis for the introduction of BAT. One of the information elements of the complex was the training program. Since the BAT implementation is an integrated branch of law, the program, during the development of the complex, included fragments that differ in presentation mode and in semantic characteristics. As separate methods of presentation caused methodological interest, a data structure was chosen that allows to represent the program elements in a multi-model way. A set of interface elements providing navigation through a multi-model structure was also proposed. The coordination of partial models for learn-methodical complex is shown on Figure 1. On the whole the approbation showed simultaneously a significant expansion of the capabilities of application systems, arising from the introduction of model matching capabilities in them, and the lack of elaboration of methods for describing and manipulating multi-model objects, making it difficult to develop means for their visualization, navigation and interpretation. As expected, a number of problems can be overcome through the use of applicative systems with a richer internal structure.
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Conclusion
This paper proposes an approach to solving the problem of ensuring the consistency of heterogeneous data models in an interactive visualization environment based on the use of conceptual dependencies. Here the coordinated models are immersed in a specialized environment of representation of dependencies on the basis of applicative computing systems. The correctness of 7
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constructing support means is provided by immersing the model of semantic network processing in the application computing system. The proposed method of construction provides, in particular, the following possibilities: • possibility of parametrization of models matching means by taking into account the semantic characteristics of the data; • possibility of describing various ways of coordination taking into account various aspects of data consideration; • possibility of achieving compatibility of models, including those aimed at different classes of users, which leads to the possibility of dynamic formation of user coalitions based on common fragments of models. The elements of the proposed approach were tested when developing the information systems that support the promotion of institutional foundations for the introduction of the best available technologies in the Russian Federation. The approbation demonstrated the possibility of achieving the set goals, which determines the practical significance of the proposed approach.
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Acknowledgements
Authors acknowledge support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005). The research is supported in part by the RFBR grants 16-07-00892, 17-0700893 and 16-07-00909, 16-07-00914, 16-07-00912, 15-07-06933, 15-07-06898 .
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