=Paper=
{{Paper
|id=Vol-1638/Paper98
|storemode=property
|title=Universal data model for solving research problems
|pdfUrl=https://ceur-ws.org/Vol-1638/Paper98.pdf
|volume=Vol-1638
|authors=Denis E. Yablokov,Vladimir A. Saleev
}}
==Universal data model for solving research problems==
https://ceur-ws.org/Vol-1638/Paper98.pdf
Data Science
UNIVERSAL DATA MODEL FOR SOLVING
RESEARCH PROBLEMS
D.E. Yablokov, V.A. Saleev
Samara National Research University, Samara, Russia
Abstract. The article deals with the methodology of creation a universal data
storage for applications oriented to the specialists who participate in scientific
researches. It is assumed that the storage structure is not rigidly bound to any
scientific domain, and it will be demanded in many projects related to the data
accumulation, data analysis and processing. The proposed approach to the or-
ganization of the storage system is based on fundamental principles, concepts
and technologies that used in the software design process. This process is based
on the subject domain analysis and the detection criteria of commonality and
variability which guarantee high abstraction level as one of the main tools for
working with data.
Keywords: universal data model, storage structure, the abstraction level, data
typification, internal classification, data categorization, external classification.
Citation: Yablokov DE, Saleev VA. Universal data model for solving research
problems. CEUR Workshop Proceedings, 2016; 1638: 828-837. DOI:
10.18287/1613-0073-2016-1638-828-837
1 Introduction
The creation of software systems for scientific researches is a difficult task. As in-
creasing their complexity, the appropriate software products building require more
and more time, and the development costs grow exponentially. It also related to de-
sign process and implementation of the storage subsystems for applications, which are
used in scientific activity, and also provide researchers of diverse, but at the same
time actual and complete information. Many scientific laboratories or research centers
could work with applications based on the proposed data model because of its univer-
sality and the high level of abstraction when describing the properties of objects and
their classification. For the specialists making an experiment it is necessary to obtain
and process the correct and complete information connected with research problems,
but during of such researches is negatively influenced some factors requiring change
the structures of data storage [1] with information about studied data domain or the
solvable tasks. Among them: need of careful selection of the experimental data ac-
cording to a storage format, use of varied initial information for complex research
and, as a result, need for data interpretation or normalization for use their within ex-
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periment. To overcome these disadvantages it is necessary to move to a new level of
the software development of such class. It is necessary to create universal data model
[2] for storing and processing various information. This data model could be a basis
for building of different information systems. It is also necessary to create the envi-
ronment for accumulation of the formalized data which can be applied to calculations,
processes of diagnostics, prediction or identification [3] in any field of the experi-
mental sciences.
2 Main idea
Objects of any type can be described in terms of some simplified data model for
which the basic concepts are entities and attributes. Attributes are the predefined lexi-
cal units or descriptors needed to describe the basic semantic meaning of the key con-
cepts in the data domain.
Fig. 1. Entities and attributes
When such concepts are defined, it is not difficult to move to a more specific descrip-
tion. It is possible to extend semantics of the selected conceptual model to the entities
relationships and relationships attributes.
Fig. 2. Entities relationships and relationships attributes
This example is a very simplistic, but at the same time it can support most applica-
tions and provide adding data of any type without specific names of tables or fields
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that are required when using a relational data model [1] in native form. When using
the universal model it is possible to input information which structure is not defined
in advance, and change of structural links like “entity–attribute”, “entity–entity” or
“relationship–attribute” can be made in runtime.
3 Data classification
Classification is the most simple and at the same time, the most often solvable task of
the universal model of data storage. The result of classification is the detection of the
signs characterizing groups of the researched objects as classes to which is possible to
refer new object. It is important to keep common concepts that defines the commonal-
ity and variability, and also other features already classified or again obtainable or
analyzable information. In fact, classification is any system distribution of objects,
phenomena or concepts by any essential signs selected for convenience of their repre-
sentation and processing. Thus, the classified information can be provided as the set
of the abstract or concrete entities ordered by some principle, which have similar
classification criteria (one or more properties). It is necessary for determination of
criteria of a commonality in the description, behavior or any other dimensions of
source unstructured data. Usually classification tools are separate by a method of their
impact on the classified element of information. For example, it is possible to organ-
ize internal classification, which can be made on the essential signs characterizing a
commonality of objects, concepts or phenomena. The data domain, in this case, de-
scribes by the elementary units of data, related to some primitives, which are deter-
mined a priori. The main concepts are “meta-type”, “instance type”, “instance”, “hier-
archy type” and “relationship attribute”.
Fig. 3. Data typification and entity relationships
These concepts determine a set of tables that fixes the entity-relation structure in the
database. A unique feature of such approach is the possibility of arrangement of un-
limited number of objects over a limited set of concepts. For example, the concepts in
the materials science are connected to definitions of graph theory that allows one to
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describe crystal-chemical data [4] using graph abstractions. With interrelated concepts
from chemistry and discrete mathematics, it is possible to classify the basic objects,
such as atom or bond as well as more difficult objects, such as molecules, rings, lig-
ands, nets, etc… Applying such classification for the applications of storage and pro-
cessing crystallographic data, the information about the molecule as a set of atoms,
can be represented as a spatial graph, i.e. non-empty set of vertices and the set of its
two-element subsets – edges. Applying the concept of "is a" [5] allows to store infor-
mation on objects with similar behavior and to consider their relations as hierarchy by
the principle "from the general to the particular". Applying the concept "part of" [5]
allows storing information on objects and their relations, using the principles of ag-
gregation and composition. This allows considering their relationships in accordance
with the principle "from the whole to the part". The concept "is a" allows storing in-
formation about similar behavior of objects, such as atoms and void centers or bonds
and channels. Concept "part of" allows describing the data hierarchies “atom–
structure fragment–net” or “atom-bond-ring-tile”, etc. Also possible the artificial or
additional classification by external sign and used for giving to a set of the researched
elements of data necessary criterion of ordering. Such classification can be expressed
through categorization when categories provide the necessary level of indirection. In
this case, the commonality criteria definition for data elements is independent of their
belonging to a specific class of data. The entity can be connected to one or more cate-
gories that makes possible in the analysis or decomposition obtaining the information
about entity in terms of a set of the related categories. In this case the main concepts
are “category type”, “category”, “subcategory” and “linked category”.
Fig. 4. Data categorization
Such classification can be used for periodic table of chemical elements where the
mechanism of categorization can be applied to specifying to what group belongs
chemical element, for example, of alkali metals, halogens, etc.
4 Scope of application
The storage structure is constructed by the principles of universal data model logically
can be separated into some interdependent components. These include: classification
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tools (typification and categorization), entity instances (with a possibility of assign-
ment for instance type a specific property set), entity relationships (allowing to speci-
fy the relationship types and values of the appropriate attributes), properties palette
(combining mechanisms to define primitive and composite properties).
Fig. 5. Logical storage structure
This makes it possible to use the proposed universal data model in different fields of
scientific research. These problems can be in the scope of theoretical and experi-
mental chemistry connected with design and structural analysis of compounds and
new materials [6], classification and systemized data representation about compound
framework types [7], prediction chemical properties of the crystal structures [8]. In
addition, in number of physics problems the proposed universal data model can be
used to store various information about results of experiments with different level of
details or to be a source of data for creation of mathematical models during research-
es. These include the problem of analysis and recognition of the nanoscale images [9,
10], spectral-spatial classification of hyperspectral images [11], increasing of sensor
sensitivity [12], etc.
5 Implementation
The following are code samples from the DDL-script for the database creation using
the universal model of data storage.
1. Code sample of creation of the meta-types table, as a set of the elementary primi-
tives related to some internal base concepts of data domain, which are defined a
priori.
CREATE TABLE IF NOT EXISTS meta_type
(
row_id uuid NOT NULL DEFAULT uuid_generate_v4(),
alias varchar(128) NOT NULL,
description text,
CONSTRAINT meta_type_pkey PRIMARY KEY(row_id),
CONSTRAINT meta_type_uindex UNIQUE(alias)
);
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2. Code sample of creation of the instance types table, as a list of the concepts defin-
ing the criterion of identity that distinguish the corresponding entity from other en-
tities, which aren’t corresponding to this criterion.
CREATE TABLE IF NOT EXISTS instance_type
(
row_id uuid NOT NULL DEFAULT uuid_generate_v4(),
meta_type_id uuid NOT NULL,
alias varchar(128) NOT NULL,
description text,
CONSTRAINT instance_type_pkey PRIMARY KEY(row_id),
CONSTRAINT instance_type_fkey
FOREIGN KEY(meta_type_id)
REFERENCES meta_type(row_id),
CONSTRAINT instance_type_uindex UNIQUE(alias)
);
3. Code sample of the table creation of category types, as a set of the elementary
primitives related to some external concepts of data domain, which are determined
a priori.
CREATE TABLE IF NOT EXISTS category_type
(
row_id uuid NOT NULL DEFAULT uuid_generate_v4(),
alias varchar(128) NOT NULL,
description text,
CONSTRAINT category_type_pkey PRIMARY KEY(row_id),
CONSTRAINT category_type_uindex UNIQUE(alias)
);
4. Code sample of creation of the categories table as a tool providing necessary flexi-
bility through the additional level of indirection in case of determination of criteria
of commonality for entities independent of their typification.
CREATE TABLE IF NOT EXISTS category
(
row_id uuid NOT NULL DEFAULT uuid_generate_v4(),
category_type_id uuid NOT NULL,
alias varchar(128) NOT NULL,
description text,
not_available boolean,
CONSTRAINT category_pkey PRIMARY KEY(row_id),
CONSTRAINT category_fkey
FOREIGN KEY(category_type_id)
REFERENCES category_type(row_id),
CONSTRAINT category_uindex UNIQUE(alias)
);
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5. Code sample of creation of the categories hierarchy table for determining external
criteria of commonality by the principle of similar behavior.
CREATE TABLE IF NOT EXISTS category_hierarchy
(
category_id uuid NOT NULL,
parent_category_id uuid NOT NULL,
CONSTRAINT category_hierarchy_pkey
PRIMARY KEY(category_id, parent_category_id),
CONSTRAINT category_hierarchy_fkey_1
FOREIGN KEY(category_id)
REFERENCES category(row_id),
CONSTRAINT category_hierarchy_fkey_1
FOREIGN KEY(parent_category_id)
REFERENCES category(row_id)
);
6. Code sample of creation the table of linked categories that contains data on the ex-
ternal of commonality by the principle from part to whole.
CREATE TABLE IF NOT EXISTS category_link
(
category_id uuid NOT NULL,
linked_category_id uuid NOT NULL,
CONSTRAINT category_link_pkey
PRIMARY KEY(category_id, linked_category_id),
CONSTRAINT category_link_fkey_1
FOREIGN KEY(category_id)
REFERENCES category(row_id),
CONSTRAINT category_link_fkey_2
FOREIGN KEY(linked_category_id)
REFERENCES category(row_id)
);
7. Code sample of procedures for creation the data row in the table meta_type.
CREATE OR REPLACE FUNCTION create_meta_type_row
(
meta_type_id uuid,
alias varchar(128),
description text = NULL
)
RETURNS VOID AS
$BODY$
BEGIN
INSERT INTO meta_type(row_id, alias, description)
VALUES(meta_type_id, $2, $3);
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END;
$BODY$
LANGUAGE plpgsql;
8. Code sample of procedure for update the data row in the table instance_type.
CREATE OR REPLACE FUNCTION update_instance_type_row
(
instance_type_id uuid,
bit_mask integer,
meta_type_id uuid = NULL,
alias varchar(123) = NULL,
description text = NULL
)
RETURNS void AS
$BODY$
BEGIN
UPDATE instance_type t SET
meta_type_id =
CASE bit_mask & 1
WHEN 1 THEN $3
ELSE t.meta_type_id
END,
alias =
CASE bit_mask & 2
WHEN 2 THEN $4
ELSE t.alias
END,
description =
CASE bit_mask & 4
WHEN 4 THEN $5
ELSE t.description
END
WHERE t.row_id == instance_type_id;
END;
$BODY$
LANGUAGE plpgsql;
9. Code sample of data reading from the table category_type.
CREATE OR REPLACE FUNCTION read_category_type_rows
(
category_type_ids VARIADIC UUID[]
)
RETURNS SETOF record AS
$BODY$
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Data Science Yablokov DE, Saleev VA. Universal…
BEGIN
RETURN QUERY SELECT t.* FROM category_type t
WHERE t.row_id == ANY(category_type_ids);
END;
$BODY$
LANGUAGE plpgsql;
10. Code sample of procedure for deleting data row from the table category_link.
CREATE OR REPLACE FUNCTION delete_catgory_link_rows
(
category_id uuid,
use_linked boolean = NULL
)
RETURNS void AS
$BODY$
BEGIN
DELETE FROM category_link t
WHERE $1 =
CASE COALESCE($2, false)
WHEN true THEN t.linked_category_id
ELSE t.category_id
END;
END;
$BODY$
LANGUAGE plpgsql;
6 Conclusions
The problem of creation of universal data storage becomes significant in the case of
implementation of unstructured data collection using an object-oriented approach [13]
for information representation and a relational database. During the creation of system
of such type, the most appropriate approach is a deductive method. It provides de-
composition of complex concepts into elementary units of data with mathematically
and semantic expected behavior. Units of information, in accordance with some pre-
defined concepts, describe the data domain. The universal data model allows storing
information of any type and complexity by using elementary primitives to describe
the hierarchical links and data relationships. Using such primitives is a prerequisite
for the development of an effective and reliable method of the description and extrac-
tion of information necessary for researches. The article provides explanations on
proposed solutions and methodologies based on fundamental concepts of program-
ming [14] and data analysis. The main advantage of the proposed approach is the
possibility of applying a universal model for any type of information, and also a pos-
sibility of determination of system of the concepts that provide the foundation for the
creation a domain-specific language [15]. Closest to the context of the specific scien-
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Data Science Yablokov DE, Saleev VA. Universal…
tific knowledge, such language can represent correlations between structure of the
data domain and how it expressed through the criteria of commonality and variability.
Acknowledgements
This work was supported by the Russian government (Grant 14.В25.31.0005).
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