In recent years, modern approaches
to creating a new generation of data warehouses (DW) use Data Fabric (DF)
architecture and have been actively developing [1]. To integrate data from
heterogeneous structured or unstructured sources, both Internet and corporate
ones, as well as local resources of enterprises or clients DF technology leverages
methods, which are performed not on the principles of consolidation, but on the
principles of virtual data integration (federalization) without physically
moving them to a single data storage. So, DF provides a unified view of all
enterprise data without data centralization and instead of replacing or
rebuilding the existing infrastructure DF adds a new, an integration and
orchestration layer built on top of multiple heterogeneous data sources like
text collections, web resources, relational databases, data warehouses, data
lakes, IoT, legacy systems, etc. This abstract layer is a knowledge graph that
may be implemented as machine learning (ML) and/or ontology-powered resource.
The knowledge graph describes
concepts and relationships in terms of the subject area of data sources, which
should be integrated, as well as metadata about them, including both
information about the storage format and the native instrumental environment,
and the specification of their semantic and pragmatic content, as well as
information about synonyms (considering the context) and abbreviations.
In this paper, we propose the using
of data factory technology to create personal, not just corporate, visual
analytics systems in the field of digital medicine. Tuning to the specifics of
subject areas and data sources is carried out thanks to an ontological layer of
knowledge describing the semantic content of data and semantic relationships
between them in terms of established concepts of the relevant subject area.
Because volume of personal data sources and velocity of their updating are not
comparative with Big Data ones we suggest using visual ontology editors to
create ontology layer and if its necessary use ML methods to design of ontology
by means of ontology learning methods.
The presented approach makes it
possible to perform semantic integration of data from heterogeneous sources,
unify the means of their processing and analysis, significantly simplify access
to end–user data, providing the user the opportunity to uniformly access medical
and other personal data stored in different places and in different formats –
in the form of familiar queries in natural language (NL) or languages by
analogy with queries in search engines of Internet services. This greatly
contributes to the successful implementation in practice of all the main
components of the concept of 4P medicine [2], which are:
1.
Personalization (individual approach to each
patient).
2.
Prediction (creating a probabilistic health
forecast).
3.
Prevention (prevention of the development of
diseases).
4.
Participativeness (motivated participation of
the patient).
As shown below, the using of data
factory technology to create modern visual analytics systems in the field of
digital medicine and personal, not just corporate, data factories is well
justified.
The paper’s content is deliberately
presented from the standpoint of "human-centricity", and the emphasis
is not on the person of the doctor, but on the person of the patient
("patient-centricity" [2]) and the availability at his/her request of
all accessible data associated with the person from different sources at the
right time and in the right place location ("personal patient data
factory"). From a technological point of view, there is no difference
between the described approach "personal patient data factory" and
the "personal data factory of a doctor", which is based on semantic
integration of heterogeneous data about patients of a particular doctor for
full-fledged analytics and medical decision-making.
It should be noted that serious
consideration of information security issues in accordance with the concept of
the proposed approach is the subject of a separate discussion and out of the
scope of this paper.
Gartner identifies data fabric
implementation as one of the top strategic technology trends for 2022 and
expects that by 2024, data fabric deployments will increase the efficiency of
data use while halving human-driven data management tasks (read more at:
https://www.cxotoday.com/hardware-software-development/data-fabric-architecture-is-key-to-modernizing-data-management/).
A data factory can be defined as a
virtual area with an integrated development environment (IDE), the
architecture and services of which provide a semantic relationship between data
stored in heterogeneous sources, regardless of the format of their storage and
the specifics of native data source systems. Source data remains in its
original place, and DF is able to deliver it to the right place as a virtual
view and at the right time of "on-demand access", performing the
necessary preprocessing, aggregation and analytics on demand.
To tackle the problems of
integrating heterogeneous data and their visual analytics, modern intelligent
data factories are designed and implemented mainly on the principles of
model–driven architecture (MDA – Model Driven Architecture). In addition to
models based on UML and domain-specific languages, ontology models have been
increasingly used for these purposes lately. As a tool environment for creating
data factories, you can use existing domestic solutions, in particular, the
Russian DataFabric KGL platform (http://datafabric.cc/), created by a resident
of Skolkovo LLC "DATAFABRIC".
As noted in the review of the
analytical agency TAdviser (https://www.tadviser.ru/index.php/Ñòàòüÿ:AI:_ot_dannyh_–_known),
DataFabric
has created a universal corporate ontology platform DataFabric KGL, which
allows you to unify access to all data in the enterprise using an open source
data virtualization platform. The DataFabric KGL (or Logical DW) of an
enterprise is implemented on the basis of a graph (semantic) data model in
terms of the corresponding domain. It allows federally accessing heterogeneous
data from different sources without the need for their preliminary collection,
aggregation and storage: data is accessed in the terminology of the subject
area through an abstraction layer in the form of a business glossary and does
not require performing any operations at the physical level of data storage.
Let's look at the problems of
distributed data processing in the field of digital medicine. It is no secret
that the human body intensively generates large amounts of data across the lifecourse,
which have one of the most important characteristics of Big Data –
"Variety" (diversity, heterogeneity). Another thing is that not all
of this data is regularly digitized and stored, and the person usually has
problems accessing his/her own personal digital data stored in different
environments. For example, even the data available to the patient in the
personal account of a concrete Medical Information System (MIS), firstly, is
actually not fully available, which prevents the implementation of the
principles of prediction and prevention, if operational analysis of the data
outside the framework of this MIS is required. Secondly, personal data reuse
within the environment of third-party MIS of other public or private medical
institutions, as well as available analytical platforms for both individual
researchers and large medical institutions are often impossible or very
difficult.
The difficulties are explained by
the fact that it requires not just data conversion due to differences in
formats, but automated preprocessing and semantic normalization, aggregation
and transformation of heterogeneous data. These problems are solved in the
environment of traditional DW, built on the principles of consolidation,
however, it requires the organization of an ongoing management process of DW in
order to synchronize updates between sources and DW, otherwise there is a
problem of "freshness" of data. Such ongoing maintenance is costly not
only for individuals, but also for entire organizations, although recently
there is hope that the expansion of domestic cloud storage services will lead
to a reduction in the cost of their services. Nevertheless, it is important to
note that if we are talking about personal, and not corporate, DW, then the
need to provide data for analytics is "on demand" (and not ongoing)
comes first. Along with retrospective personal medical data, the most
up-to-date information should be available for monitoring and analysis, which
can be stored both in third-party MIS databases and on the local personal
computer (for example, the results of laboratory tests in pdf format, which
have been obtained on the initiative of the patient himself without consulting
a doctor).
We believe that any person should
rightfully have access to most of his or her personal digital data, including
the digital data from third-party storage systems with the possibility of their
virtual integration in the process of analysis with data stored in different
formats on the personal computer. This is necessary for health monitoring,
operational analysis and decision-making, for example, to urgently consult a
doctor or reasonably promptly adjust your diet. It seems that there can be no
question of any real participativeness without the person being the rightful
owner of the own digitized personal data with the right to promptly provide the
data to third parties, for example, employees of sanatorium-resort institutions
or ambulance doctors.
In terms of volume, the majority of
data streams over 100 GB per day can be classified as Big Data, which is not
always typical for most personal medical data at the current stage of IT
technology development. However, over time, with the development of IoT and
Ubiquitous Computing as well as improvement, growth in demand and availability
of wearable personal devices, volume of the digital footprint of an increasing
number of people will be able to grow to the volume of Big Data. In addition to
purely medical data, so-called "health diaries", which are kept by
the patient himself locally on the personal computer must be considered for the
purposes of analytics. The patient's personal data can also include data from
various questionnaires about environmental, psychological and social living
conditions, lifestyle and human activity, including professional factors. To
semantic integration and analysis all of mentioned heterogeneous data must be
preprocessed automatically.
The second main characteristic of
Big Data, Velocity, implies not only the speed of data
generation/growth/change, which is also not typical for personal medical data
yet, but also the need for their high-speed processing, which is essential for
the implementation of all the basic principles of 4P medicine.
As for the third main
characteristic, Variety, where diversity is understood as the heterogeneity of
data and the need for simultaneous and unified processing of various types of
both structured, semi-structured and unstructured data, it is fully inherent in
the specifics of a personal data and corresponds to the goals, objectives and
key features of building data factories. It is important to note that this
third characteristic has been widely recognized as the main driver of the
development of modern Big Data technologies.
To emphasize the validity of the
use of DF technologies for the analysis of personal data to solve the problems
of 4P medicine, we note the similarity of their goals and objectives, which are
the automatic extraction of new useful information and knowledge from the source
data, including previously unknown patterns in order to automatically solve
analytical problems to support evidence-based decision making and make
predictions to prevent some risks, in particular. Modern DF technologies are
the best suited for monitoring and integrating the patient's fresh personal
data with previously received results of predictions in order to assess health
risks and prevent the development of diseases. The methods and tools used in DF,
in particular, the methods and tools of the Semantic Web [1] and Visual
Analytics do not require the construction and maintenance of a new data
warehouse based on the principles of consolidation.
It is known that data integration
systems are capable of providing integration at various levels: physical,
logical and semantic. Integration of data at the physical level usually comes
down to converting data from various sources into some single format of their
physical representation. Data integration at the logical level provides the
ability to access data from various sources in terms of a single global schema
that describes their joint representation considering structural and behavioral
(in the case of object models) properties without considering the semantic
properties of the data. Data integration at the semantic level provides support
for a unified representation of data based on their semantic properties in the
context of a common knowledge graph about the related subject area. The
end-user interface hides all the technical aspects of storing data sources such
as location, storage format, structure (if the data source is structured) and
others.
Figure 1 shows a simplified diagram
of two approaches to data integration for comparison: the traditional one based
on the principles of consolidation (ETL – Extract, Transform, Load) and the
virtual one based on the principles of federalization. In BI (Business
Intelligence) applications and OLAP systems, in the ETL process, all the
necessary data available from different sources are loaded into a single
datastore even before users start requesting them. Before loading, to bring
heterogeneous data to a single representation, which is often called
"normalization" of data, DW use a global reference data schema. As a
result of ETL processes, physical and logical data integration is provided.
After uploading data to a single repository, access to sources is not required,
however, in corporate DW there is a problem of "freshness" of data.
Figure 1:
Data integration schemes based on the principles of ETL
consolidation (left) and federalization (right)
Unlike consolidation,
federalization allows you to extract data from various sources, combine them
and perform analytics in real time without the need for physical movement: the
data remains with their owners, and access to them is carried out on demand, i.e.
in response to a request when there is a need in the analysis process. Thus,
federalization supports a single virtual space for a variety of heterogeneous
data sources, ensures their semantic integration and does not require the cost
of creating and maintaining a single physical DW. The user formulates NL query
in terms of the knowledge graph, which allows for semantic search and semantic
integration of data, considering the description of semantic relationships
between data elements presented in the knowledge graph. All necessary data
transformations are carried out in the process of extracting them from sources.
It is important to note that virtual integration helps to ensure the rules of
the security policy and license restrictions, if direct copying of data from
source systems is prohibited.
Logically, virtualization is
carried out due to an additional intermediate layer that isolates the physical
storage of data from applications. The central component of the integration
system based on the principles of federalization is the so-called mediator,
which integrates data received from adapters (wrappers). Adapters are
components that ensure uniform interaction of the intermediary with data
sources (in terms of a single model). The intermediary supports a unified user
interface based on a global knowledge graph describing metadata and semantic
content of data across various sources, and also supports mapping between
global and local representations of data. A user query formulated by means of a
unified interface is automatically decomposed into a set of subqueries addressed
to the necessary data sources. Based on the results of their processing, a
complete response to the request is synthesized as a virtual view on the
personal computing device.
It is thanks to the architecture
described above that data factories are able to implement semantic integration
based on the principles of federalization. If we talk about the possible
disadvantages of DF, they may be associated with additional costs for access to
numerous data sources, especially if they are large, which affects the
performance of analytical services. However, as noted above, two of the three
main characteristics of Big Data (Volume and Velocity) are not the main
features of the patients' personal data. In addition, the semantic coherence of
data from different sources is guaranteed, at least, by their belonging to the
same person that helps to eliminate the specified problem.
Approaches to the implementation of
systems of semantic integration of heterogeneous data based on intermediaries
(mediators) in the early 90s were based on the use of relational data models:
the main metamodel was ODMG–93; then from the end of 90s to 2004-2005,
ontologies were mainly used for these purposes: the main metamodel was ontology
description language; since 2004, the focus had shifted to application of Semantic
Web technologies and RDF as the main meta–model. The Semantic Web has raised
the role of ontologies to a new, one might say, "worldwide" level,
because they play a key role in the automatic semantic markup and integration
of data from different Internet resources, regardless of which natural language
a particular web resource is presented in.
The development of Semantic Web
technologies made virtual integration more promising. Although it should be
noted that recently there has been a convergence of different approaches to
data integration [3]. When implementing modern corporate data factories,
integration based on the principles of federalization is used, where ontologies
act as a knowledge graph. It is this approach that we propose to use, expanding
its application to support the principles of 4P medicine and the creation of patient
personal data factories, which enabled analytical services based on the
semantic integration of heterogeneous personal data.
As noted above, data factories are
built on the principles of semantic virtual integration of heterogeneous data
sources based on their consistent model, presented in the form of a knowledge
graph (in our case, in the form of ontology). DF services allow the user to pose
a natural language query (NL query) to the entire virtual data space in terms
of this knowledge graph, without caring about the place of data real storage.
And because this data can be stored in different formats (plain text, database,
web resource, etc.) then for their unified processing by means of Natural
Language Processing (NLP) methods it is necessary storing not only data
semantics description, synonyms (considering the context) and abbreviations but
also the metadata (including data about storage format and native data
environment).
Since we are discussing the
creation of not corporate, but personal DF with an emphasis on the field of
medicine, an important aspect of our approach lies in the possibility of the
semantic integration personal data and results of data analysis stored in a
variety of information system environments, for example, public or private MIS,
with the person-related data from private or public cloud (for example, with
personal data generated by wearable devices) and from personal files stored
locally (on user's personal computer, on flash media, etc.). In the last case,
local personal files usually have text or spreadsheet format and contain the
personal data about monitoring blood pressure, daily diet, physical activity,
etc. On the one hand, the processing of medical data is complicated by the fact
that in this area there are many terms and abbreviations that are not generally
accepted, but well-established and constantly circulating in this or that
community of professionals. Moreover, different concepts may correspond to the
same designations. For example, "CP" can mean "Chest Pain"
when administered by a cardiologist or ambulance physician, but it can also
mean "Cerebral Palsy" when administered by a neurologist or
pediatrician. And on the other hand, the processing of unstructured local
personal files is complicated by the fact that a person can use household, not
professional vocabulary.
We use traditional NLP services,
which utilized ML algorithms to identify and correct data quality issues (such
as missing values, duplicates, and inconsistencies). Because the complexity of
medical data processing, we integrate the using of traditional ML tools,
methods and tools of ontology engineering and open NL resources such as
lexico-syntactic patterns, medical digital dictionaries and reference books as
well as our own ones to automate mapping and intelligent transformation of data
when integrating.
Based on our experience of prior
developments (for example, [4]) and using SciVi visual analytics platform as
IDE [5-6] as well as the collaborative projects with BIOGENOM LLC [7], we
propose to use not one single large ontology as a knowledge graph, but a coherent
set (family) of ontologies related to a particular domain, which represents the
single global integrated schema (called mediated schema) of underlying sources.
This set of ontology has a common model, which is described below. Ontologies
are stored in the so-called smart repository managed by a meta-ontology
containing metadata about the ontologies themselves (date, owners, location,
binding to the category of described data such as "Group of
diseases", "Medical records", "Questionnaires",
"Reference Books", "Health Diary", etc.). Meta-ontology is
used for semantic indexing when searching and mapping ontologies. Various
methods and means of ontology integration are well studied and actively used in
practice [8].
Figure 2 shows a fragment of
ontology of personal data that acts as a knowledge graph layer providing the
semantic integration of heterogeneous data (some related data has a database
format, but the other ones have text or xml format). As the system presents one
global integrated schema, users enable pose their queries in terms of the
corresponding application domain.
Figure 2:
Fragment of the personal data ontology layer providing the
semantic integration of heterogeneous data
According to DF virtual integration
approach we use the solution that utilizes a wrapper layer known from mediated
systems to provide data semantic integration (see Figure 1). Firstly,
mediator’s mechanisms find the corresponding ontologies in the repository and,
if necessary, establish a semantic correspondence between them, which is stored
in the form of a mapping specification based on the principles of OBDA mapping
[9]. Such correspondence in the case of the ontologies remain unchanged is
established one time and stored in the repository as a metadata for a further
use. If some ontology has been subjected to change, the special mechanism
monitors this fact and automatically starts re-mapping.
Then, the mediator uses adapters to
organize access to the necessary data and their aggregation in response to a
request. The user’s query is decomposed into sub-queries to individual sources
based on their descriptions. These sub-queries are sent to the wrappers of
individual sources, which will execute them over local models and schemas of
sources. The mediator receives answers from wrappers, combines them into one
answer, and sends it to the user as a virtual view. You can find the typical
architecture of a mediation system and more detailed description in [10].
To design the ontology, we utilize
our own visual ontology editor named ONTOLIS [4, 11-12] that unlike most other
ontology editors focuses not only on knowledge engineers but also on untrained
users. This visual editor itself is also implemented as an ontology driven
solution and makes it easy to adapt the visualization tools provided by it to
the individual preferences of end-users. ONTOLIS has been developed to design
so-called lightweight ontologies [13].
Lightweight ontology represents a
thesaurus of domain concepts and a basic set of supported types of relations
between them, but does not include axiomatics. That is, axiomatics is not
explicitly specified as a separate component of the ontology model, and ONTOLIS
has no tools to define it. But in ONTOLIS, any node or arc of an ontology graph
can have attributes, which provide a way to store additional data associated
with the nodes and the arcs in a graph. We use this opportunity to uniform
interpreting the semantics of various types of relationships represented by
arcs between concepts represented by nodes during logical inference by means of
an ontology-driven mechanism implementation.
In accordance with
our approach, the personalized visual analytics
system under development includes a special uniform reasoner mechanism to
interpret the specification of data stored in the attributes. For this, all
data specifications, which can be stored in the attributes, are categorized and
belong to some category. When the reasoner finds the keyword, which represents concrete
category the interpretation mechanism is running and the corresponding
functions are called automatically from so-called smart repository. The set of
categories is expandable. To expand this set it is necessary to add new
interpretation function with corresponding metadata to smart repository.
All specifications stored in the
attributes have a json-like format, which allows a unified way to describe different
actions across reasoning such as calling a library function to interpret the
semantics of a certain type of relationship and others according metadata
categories of attributes. Specification of the category begins with unique
identifier, for example, the path to the function is specified after the
keyword "path", and the “def” is used for denoting the definition of
a concept represented by some node. When the reasoner bypasses the ontology
graph and find a non-empty attribute of the current node or arc, it interprets
the metadata specification in accordance with corresponding category. For
example, if the "path" specification is presented in the attribute
description, then the corresponding function from the system repository is
automatically executed and specified arguments of the function are extracted
from the ontology.
Figure 3 depicts fragment of the
ontology for assessing the risk of atherosclerosis (arcs have no name because
they represent the same type – causal relationship). This ontology was also
created within the ONTOLIS environment. Because one of the important tasks of
any ontology driven solution is to validate the ontology we demonstrate here
how reasoner uses the attribute properties of ontology nodes and arcs storing
the specification of the path to the needed functions and models to run the
dialog form providing domain expert to evaluate the validity of relations
established between different factors of risk and the correctness of models
evaluating the common effect of the different factors to the same risk. Because
intellectual property protection, the Figure 3 does not show formulas for
calculating the assessment of the joint influence to the same risk of several
factors at once.
Figure 3:
Fragment of the knowledge graph for assessing the risk of atherosclerosis using
the attribute properties of nodes and arcs of ontology
Typically, a reasoner is designed
as a program that infers logical consequences from a set of explicitly asserted
facts or axioms to automated support the reasoning to tackle such tasks as
classification, debugging and querying. In our case, the reasoner automatically
interprets all different attribute specifications (including those that
interpret the semantics of relationships and different types of restrictions)
while all the elements of the ontology are accessible by navigating through the
ontology graph during the inference engine performance. So, in some sense, our
approach to implementing inference engine based on automatic procedural
interpretation of categorized attributes of ontology graph acts the same role
as the interpreting of axioms, which are represented in declarative form and
used across the different solutions based on heavyweight ontologies.
Accessing other services of the
system is performed similarly. For example, when solving some analytical
problem, the reasoner can find related specifications in the attributes of
nodes or arcs of the current applied ontology for launching concrete machine
learning methods and the corresponding library functions stored in the system
repository are called. Despite the fact that our approach assumes, if necessary,
using a whole family of interrelated ontologies instead of single one, all of
them have a common model, which allows to perform the reasoning different
ontologies in a uniform way. In particular, the same function is used for
different ontologies to interpret "a_part_of"
("part-whole") type relationship. This type of relationships you can
see in Figure 2. This figure illustrates also that the attributes of the node
"start" store the specification an API call to launch the
corresponding interpreter function.
The common ontology model is
described below. We use the such model to implement ontology driven visual
analysis tools that are easily adaptable to such personal preferences as:
—
data source and its location;
—
type of graph that displays the result of data
analysis;
—
data structure (description of the
correspondence of data structure fields to graph elements indicating which data
measurements are postponed along the axes of abscissa, ordinate and
application, if the graph is three-dimensional, what is the displayed interval,
etc.);
—
color scheme used for rendering.
Fragment of the lightweight
ontology describing the software entities utilized for visualization, their
properties and using, as well as corresponding data sources is demonstrated in Figure
4 (you can see nodes representing csv files with statistical data on the growth
of diseases in China and the USA indicated as data sources). The set of domain
concepts in the model of this ontology includes such root concepts as
"Data Source" (data source), "Data Field" (data field),
"Chart" (chart), "Chart View" (chart plotting area),
"Color" (color), etc., part of the taxonomy of chart types in
NChart3D and the instances of data sources and charts. The represented ontology model
has been successfully used to ontology driven visual data analysis tools
development in different real-world projects for a long time.
Figure 4:
Fragment of the applied ontology for ontology-driven adaptable visual analytics
tools development
The model supports the following set
of types of relationships between concepts:
1.
"is_a" ("subclass–class")
describes the relationship between child and parent concepts and support an
inheritance of properties of the parent class.
2.
"has"– describes one concept as a
property or attribute of another one.
3.
"is_instance" describes the
relationship of belonging of some instance (individual) to some class
("instance-class" relationship).
4.
"is_used" describes the using one
instance by other ones by means of calling during the program performing.
The "is_used"
relationship is very important when developing ontology-driven software
solutions to tackle visual analytics problems, since it determines the actual
interactions between instances of software entities described by the ontology
and affects the scenario of program execution so to be able managing the
program behavior in dynamics. As noted above, the set of axioms described in
the ontology model we use is empty, because, as our experience with using
ontologically managed services of the SciVi visual analytics platform has shown
the leveraging of applied lightweight ontologies is enough for the purpose of
adapting visualization tools to the specifics of analyzed data and personal
preferences of the users.
It is known that using interactive
and cognitive computer graphics tools contributes to greater human involvement
in the analysis process than just a simple look at the visual image. In the
context of the topic under discussion, such tools work more to implement the
principle of participativeness of 4P medicine. In our practice, traditionally,
to automate visualization tools development and to customize them to the
personal preferences of both end-users and developers, we use the methods and
tools of SciVi visual analytics platform [5-6] enabling personalized visual
analytics tools according to the ontological user profile. We have expanded
SciVi repository with new medical-oriented visualization models.
As an example of visualization
model that, in our opinion, is adequate for the implementation of interactive
cognitive graphics tools in the personal data factory environment (as a part of
visual analysis services based on the virtual integration of heterogeneous
medical data), may be a model of the human body in the form of a subway
diagram. For example, in Underskin Body Map created by Sam Loman (see Figure
5), instead of the usual metro lines, the main physiological lines of the human
body are displayed, marked with different colors (human arteries are indicated
in red, the venous system is indicated in blue, etc.). To have explanations the
colors for marking the human muscular system, central nervous system,
respiratory system, lymphatic circulation system, excretory and digestive
systems see the in-figure legend.
Figure 5:
Underskin Body Map created by Sam Loman
(https://vizworld.com/2010/03/sam-lomans-underskin-visualization/)
The main nodes of all the listed
physiological systems of the human body are singled out separately in something
similar to the stations on the metro line diagram. The digital healthcare
industry could use visualization techniques such as "anatomical subway
maps" to better convey medical concepts to patients, explain problems with
their current health status, and assist doctors in analyzing heterogeneous
patient data.
This method of visualizing the
physiological systems of the human body scales well and is suitable for
rendering on mobile device screens. Using the SciVi semantic filtering
mechanism and the semantic integration services of data factories described
above, it is possible to output all needed personal information available from
the virtual space in relation to the certain organ systems and the main
physiological systems of the human body in the simple and understandable view.
The size of the pictograms may
correspond to the volume of available data about the particular organ or organ system
in humans, the color intensity – about the level of health risks of the related
organ or physiological system in humans according to the results of integrated
analysis of the accessible medical data, genetic and environmental factors,
data on human nutrition or lifestyle. The corresponding analysis is performed
by machine learning methods and/or by knowledge engineering methods.
Causal, temporal and other types of relations are automatically considered to
assess the risks of developing diseases and healthy longevity. This kind of
visual integration on one screen of a large volume of heterogeneous data based
on the principles of cognitive compression of information is quite natural,
visual and understandable not only to professionals. Means of interactive
graphics (clicking on the icon of the relevant organ or the "metro
line") allow, in addition to the initial and aggregated information, to
see an explanation of the conclusions made about health risks.
The use of such visualization
models greatly contributes to the implementation of such principles of 4P
medicine as prediction and prevention, because the interaction with this kind
of graphics, provided the completeness and consistency of the analyzed data,
can help to identify in advance in a person a set of those key factors that
affect to the occurrence some disease, and formulate recommendations to change
a lifestyle and/or the need to contact specialists to solve problems that
require the participation of a doctor. Recommendations for lifestyle correction
and preservation of current results in a personal data factory can help
accompany the patient on the way to changing his/her daily behavior and forming
new habits in terms of sleep, nutrition, movement, stress management, etc.
As practice shows, the use of
lightweight ontologies can reduce the threshold for entry of newcomers into the
field of ontological engineering, labor intensity and the time for developing
ontologies [13]. It is important to emphasize that one of the main advantages
of developing ontology driven solutions is that such software systems easily
adapt to changes in the subject area of the tasks being solved. For the
adaptation, it is enough to change the ontologies without changing the source
code of the software platform. This is fundamentally important when creating
data factories both for adaptation to new data sources and their semantic
integration on the principles of federalization, and for automating the
development of DF and its services in general. In addition, through the use of
ontological engineering methods, the adaptation of the functioning of
analytical services of data factories to the personal preferences of users is
simplified, which is also aimed at implementing one of the most important
principles of 4P medicine – personalization. These principles are implemented
especially effectively when DF has a microservice architecture.
So, from traditional
enterprise-level data factories, "personal data factories" inherit
mainly the following:
1.
Data sources, using the capabilities of modern
graphical interfaces (graphical APIs), receive end-to-end integration.
2.
Microservice architectures are used instead of a
single block of software platforms.
3.
Along with local data sources (personal
collections of documents, as well as person-related data from MIS of both
public and private clinics), the largest number of possible cloud solutions are
used in the "personal data factory" environment.
4.
Information flows are orchestrated (based on
special services).
5.
The quality of information is improved through
unification and virtualization.
6.
The quality of analysis, monitoring, hypothesis
testing, risk assessment and predicative analytics is improved through the use
of advanced scientific visualization tools and visual cognitive data analysis
by AI methods.
7.
Regardless of the type and volume of the data
source, it is provided with quick access (to sites, relational databases, data
warehouses, data lakes, etc.).
8.
Providing secure and delimited access to
different categories of users.
But unlike traditional enterprise-level data factories, the "personal data factory" contains only those
data that effectively work to perform global end-to-end (across the entire set of all data sources)
analytical NL-queries about the concrete person. The time of doctors for a comprehensive analysis of
information obtained about the patient from various third-party sources can significantly be reduced due to DF and modern AI methods.
The approach proposed in the paper,
called "personal data factory", scales well, and the use of
microservice architecture allows effectively expanding the functionality of the
visual analytics systems through a unified way of integrating new services and
data resources to the DF platform. At the same time, there is no need to make
changes to the existing infrastructure of data sources: an additional semantic
layer based on ontologies is added between DF services and data sources, which
deals with metadata management and access to heterogeneous data based on the
principles of federalization. DF services help optimize data storage,
processing and analysis, improve the quality of service of personal information
resources and hardware and allow building ontological profile of the health
status of the owner of the "personal data factory".
This profile is automatically
updated being available for further semantic analysis, health monitoring and
contextual search, for example, in order to get advice from a psychologist or
recommendations on nutrition and physical activity. The information presented
in the ontological profile aggregated from various sources in relation to
various systems of human organs and interactive visual analysis tools allow you
to visually track the dynamics of changes in the state of health and promptly
recommend to consult a doctor of the right specialty.
The described approach to
ontology-driven data integration has a synergistic effect, because it allows
through semantic integration to expand the capabilities of search services
compared to the capabilities of modern Internet search engines. In particular,
the search service becomes more expert due to the ability to give answers like
"YES"/"NO", automatically determining the need for such an
answer option. In addition, the semantic power of search engines increases in
the case when no resource contains a complete answer to the query and automatic
semantic aggregation of search results from different resources is required.
So, instead of issuing a list of links to different resources each containing
only some part of the required information, a single and more complete response
to the query is generated on the principles of semantic integration using NLP
tools and Semantic Web technology. This opens up new opportunities for the
personalized IT industry and not only in relation to the field of 4P medicine.
This study received support from the Federal State Task Program by Scientific
and Technological Center of Unique Instrumentation of the Russian Academy of Sciences (FFNS-2022-0010). This work was performed
using the equipment of the Shared Research Facilities of the Scientific and Technological Centre of Unique
Instrumentation of the Russian Academy of Sciences.
1.
Patel A., Debnath, N.C., Bhushan, B. (Eds.).
Semantic Web Technologies: Research and Applications (1st ed.). CRC Press.
2022. 404 p.
DOI:10.1201/9781003309420
2.
Martyushev-Poklad A.V., Yankevich D.S.,
Panteleev S.N., Pryanikov I.V., Guliev Y.I. Healthcare information systems and
organizational model of care: current situation and opportunities for progress //
Vrach i informazionnie technologies. 2020. Vol. 5. P. 6-16. DOI:10.37690/1811-0193-2020-5-6-16
(in Russian)
3.
Business Intelligence and Analytics: On-demand
ETL over Document Stores /
M. Souibgui,
F. Atigui,
S. B. Yahia,
S. S.-S. Cherfi
// In book: Research Challenges in
Information Science. 2020. Vol. 385. P. 556–561.
DOI:
10.1007/978-3-030-50316-1_38
4.
Chuprina S.I., Postanogov I.S. Enhancing Legacy
Information Systems with a Natural Language Query Interface Service // Vestnik of Perm State
University. 2015. Vol. 2 (29). P.78-86. (in Russian)
5.
Ryabinin K., Chuprina S. Development of
ontology-based multiplatform adaptive scientific
visualization system // Journal of Computational
Science. Elsevier. 2015. Vol. 10. P. 370-381. DOI:
10.1016/j.jocs.2015.03.003
6.
Ryabinin K., Belousov K., Chuprina S. Novel
Circular Graph Capabilities for Comprehensive Visual Analytics of
Interconnected Data in Digital Humanities // Scientific Visualization. 2020.
Vol. 12(4). P. 56–70. DOI:
10.26583/sv.12.4.06
7.
Chuprina S.I. Data Fabric Technologies as the
Basis of Intelligent Analytical Platforms of Preventive and Resort Medicine Digital
Environment // (Problems of Balneology, Physiotherapy, and Exercise Therapy. 2023.
Vol. 100(3). P. 219. DOI:10.17116/kurort202310003222 (in Russian)
8.
Noy, N.F. Ontology Mapping // Handbook on
Ontologies. Springer, Berlin. 2009. P. 573–590.
DOI:10.1007/978-3-540-92673-3_26
9.
Chuprina S., Postanogov I., Nasraoui O. Ontology
Based Data Access Methods to Teach Students to Transform Traditional
Information Systems and Simplify Decision Making Process // Procedia Computer
Science. 2016. Vol. 80. P. 1801–1811. DOI:
10.1016/j.procs.2016.05.458
10.
Macura, M. Integration of Data from Heterogeneous Sources Using ETL
Technology // Computer Science. 2014. Vol. 15 (2). P. 109-132. DOI:
10.7494/csci.2014.15.2.109
11.
Chuprina S.I., Zinenko D.V. Adaptable Visual Ontology Editor ONTOLIS
// Vestnik of Perm State University. 2013. Vol. 3 (22). P. 106-110. (in
Russian)
12.
Chuprina S., Nasraoui O. Using Ontology-Based Adaptable Scientific
Visualization and Cognitive Graphics Tools to Transform Traditional Information
Systems into Intelligent Systems // Scientific visualization. 2016. Vol. 8(1).
P. 23-44.
13.
Davies J. Lightweight Ontologies // Theory and Applications of
Ontology: Computer Applications. 2010. P. 197–229.
DOI:10.1007/978-90-481-8847-5_9
