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Managers as decision-makers present in different sectors should be supported in efficient and more and more sophisticated way. There are huge number of software tools developed for such users starting from simple registering data from business area – typical for operational level of management – up to intelligent techniques with delivering knowledge -for tactical and strategic levels of management. There is a big challenge for software developers to create intelligent management dashboards allowing to support different decisions. In more advanced solutions there is even an option for selection of intelligent techniques useful for managers in particular decision-making phase in order to deliver valid knowledge-base. Such a tool (called Intelligent Dashboard for SME Managers – InKOM) is prepared in the Business Intelligent framework of Teta products. The aim of the paper is to present solutions assumed for InKOM concerning on management of stored knowledge bases offering for business managers. The paper is managed as follows. After short introduction concerning research context the discussed supporting managers via information systems the InKOM platform is presented. In the crucial part of paper a process of knowledge transformation and validation is demonstrated. We'll focus on potential and real ways of knowledge-bases acquiring, storing and validation. It allows for formulation conclusions interesting from knowledge engineering point of view.
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AbstractManagers as decision-makers present in different
sectors should be supported in efficient and more and more
sophisticated way. There are huge number of software tools developed
for such users starting from simple registering data from business area
typical for operational level of management up to intelligent
techniques with delivering knowledge - for tactical and strategic levels
of management. There is a big challenge for software developers
to create intelligent management dashboards allowing to support
different decisions. In more advanced solutions there is even an option
for selection of intelligent techniques useful for managers in particular
decision-making phase in order to deliver valid knowledge-base. Such
a tool (called Intelligent Dashboard for SME Managers InKOM)
is prepared in the Business Intelligent framework of Teta products.
The aim of the paper is to present solutions assumed for InKOM
concerning on management of stored knowledge bases offering
for business managers. The paper is managed as follows. After short
introduction concerning research context the discussed supporting
managers via information systems the InKOM platform is presented.
In the crucial part of paper a process of knowledge transformation and
validation is demonstrated. We’ll focus on potential and real ways
of knowledge-bases acquiring, storing and validation. It allows
for formulation conclusions interesting from knowledge engineering
point of view.
KeywordsBusiness Intelligence, Decision Support Systems,
Knowledge Management, Knowledge Transformation, Knowledge
Validation, Managerial Systems
I. INTRODUCTION
MALL
business plays significant role in modern economy.
Processes of decision-taking by managers representing
small companies are quite complex and sometimes more
difficult than in bigger corporations. On the other hand there are
many software tools prepared to support decision makers but
rarely there are addresses for managers of small and medium-
sized enterprises (SMEs) with option of more intelligent
solutions. The main idea of the creators of an interactive portal
for decision-makers was applying of selected techniques
allowing for data mining.
Main assumptions of the discussed tools should be
stressed. First, there are many problems where intelligent
techniques can be applied. Second, in every case generated
Katarzyna Marciniak Ph.D. Student in the Department of Business
Intelligence Systems of Wrocław University of Economics, 53-345 Wrocław,
Komandorska 118/120 (corresponding author to provide e-mail:
katarzyna.marciniak@ue.wroc.pl).
knowledge base should be stored in the package container
including context and input data of the process. The third, most
important from knowledge base maintenance point of view
knowledge validation and keeping for the further usage.
Especially the last one seems to be very attractive from
the research point of view. Generated knowledge bases
represent many cases and discovered knowledge can be applied
in similar circumstances and moreover can extend existing
domain knowledge. Therefore managing knowledge formed
in such a way become big research challenge.
The paper is managed as follows. The next part is devoted to
discuss about knowledge management, let’s stress knowledge
which in automatically created as a result of applied data mining
or other intelligent techniques in order to support managers.
Then a general idea of the prepared software (InKOM portal) is
described. Some aspects of knowledge validation and
maintenance is discussed in the next section. Finally conclusion
remarks, summarising the research are presented. The target
group of the developed package are managers from SMEs.
II. LEVELS OF SUPPORTING MANAGERS AND KNOWLEDGE
MANAGERS
Development of systems supporting managers (shortly MIS
Management Information Systems) has a long history.
Traditionally, at least three levels of supporting managers are
taken into account.
At the first level identified with transaction
systems managers are supporting basic functions
at operational level of management. These systems
are basically databases which can be a source
of more sophisticated solutions; for example can
be input stream for created data warehouses.
At the second level - more advanced - some
functionalities allowing for preparation of decision
prototypes are available. This kind of systems
(called decision support systems) contain
specialized methods for generation decisions
at tactic or strategic levels of management.
At the third, the most advanced, level domain
knowledge is represented and intelligent techniques
for knowledge processing are applied. Expertise
or solutions for the defined problems are generated
Mieczysław L. Owoc, Director of Department of Business Intelligence
Systems of Wrocław University of Economics, 53-345 Wrocław,
Komandorska 118/120 (corresponding author to provide e-mail:
mieczyslaw.owoc@ue.wroc.pl).
Knowledge Management in the Interactive
Portal for Decision Makers on InKOM Example
Katarzyna Marciniak, Mieczysław L. Owoc
S
with explanation of used ways to reach the system
goal.
Nevertheless main goal of these systems is the same: to support
managers in decision-making processes.
The solution proposed in the InKOM portal represents
the highest level of supporting managers e.g. offering
intelligent techniques able to support decisions. It worth
to stress knowledge included to the portal resources is twofold:
global (with extension its interdisciplinary approaches)
and domain connected with the problem to be solved.
Therefore management of knowledge present in the system
containers becomes a big challenge for the portal authors.
Being aware of all possible kinds of company’s knowledge
resources and knowing value of this crucial factor,
it automatically refers to the essence of knowledge
management, treated as a priority in the strategic management
techniques[4]. Knowledge management, as a relatively young
field of management encompasses the latest methods and
techniques that are designed to provide most spectacular use
of knowledge.[13],[7] To make it more detailed, “knowledge
management system is a modern concept, involving
the effective use of knowledge and transforming the company
into a lasting value for customers and employees
of the organization”[22], [3]. Moving forward, “knowledge
management is clearly defined and systematic management
of vital knowledge for the organization and its associated
processes of creating, gathering, organizing, diffusion, use and
exploitation of knowledge, carried out in pursuit
of the objectives of the organization”[20]. Knowledge
management can also be treated as a specially designed “system
that helps organizations to acquire, analyse the use (re-use)
of knowledge in order to make faster, smarter and better
decisions, so that they can achieve a competitive
advantage”[11]. In order to obtain a complete picture of
knowledge management need to mention two aspects. The first
talking about the fact that knowledge management is
“management of information, knowledge and expertise
available within the organization, i.e. the creation, collection,
storage, sharing and use, to ensure the organization’s future
development of existing resources”[12], [21]. Second, where
knowledge management is regarded as a “deliberate business
strategy, which selects, distils, stores, organizes, packs and
provides information relevant to the company’s business in a
way that improves staff efficiency and competitiveness”[5].
As we can see, authors of the above definition also
emphasizes the two items related to knowledge management.
Both accessible to, the information, experience, staff and their
expertise and the technological, where the focus is on codifying
knowledge, its acquisition, collection, analysis, storing
and sharing at any time, by a specific user. The logical also
is the fact that the development of knowledge takes place
through the exchange of experiences, analysis, opinion, finding
new sources of information, where the information systems
are the basis to allow all of the actions.[21]
In the article authors accepted both attitudes as crucial and
admitted that knowledge management can be a part of advanced
intelligent information system, classified as Business
Intelligence.
Management of knowledge resources pertaining to general
concepts and more detailed technological solutions must be
supported by specialized tools. The list of main tasks necessary
to effectively aid the whole process of knowledge management
includes [1]:
Acquiring of knowledge from heterogeneous
resources,
Creation and coding various knowledge pieces
in order to integrate the onto immanent full enclosure,
Support of knowledge sharing and ensure of team-
works.
In practice the list of mentioned above tasks is supported
by the following tools [1] and [18]:
Document and project management,
Workflow and teamworks,
Corporate portals,
Intelligent profiles and expert systems,
Business intelligence,
E-learning portals and knowledge maps.
Examples of taks supported by itemized tools are: document
distribution and monitoring, project definition
and implementation, communication between customers and
companies, controlling of workflow and teamwork. Some
of tools cover specialized tasks while others offer more
integrated approach. Knowledge in these tools is interpreted
very flexible; from unstructured document up to formalized
knowledge bases.
III. BUSINESS INTELLIGENCE AS A COMPONENT OF TETA
SOFTWARE
A. An Essence of Business Intelligence
By linking the benefits of process approaches of
understanding the function of organization, characteristics
of decision-making processes and the opportunities that
currently provide dedicated solutions for managers, it is fair to
say that streamline the decision making process is made
possible by Business Intelligence solutions. The process of
Business Intelligence is based on transformation of data to
information, then into Decisions and finally into actions. [19]
With the development of technology and the increasing demand
for more complete information for managers in organizations,
systems have been enhanced with comprehensive modules
aimed at providing more detailed business analysis. The main
purpose of Business Intelligence is to provide useful managerial
expertise at every level of management. [7], [14] The main
characteristics of the Business Intelligence system can
be described as: [14], [6]
Integrating heterogeneous data from different systems,
creating a single, cohesive data repository
Allowing the manipulation of the data available
Showing the analysed data in many different ways
(e.g., in the form of tables, graphs, lists, etc.)
Easy and intuitive to use
Safety and limiting access to the data, as a basis
for action
Provide the user the possibility of supplementing
a repository of data on its own knowledge
(eg.as a result of analytical simulations).
Business Intelligence as an innovative technology that uses
the latest innovations in the field of IT, seems to be the one who
can provide the necessary information and knowledge
management. And the easiest way to explain Business
Intelligence idea is to say it is an information technology, which
is used to transform large volumes of data into information, and
then to transform information into knowledge. Is primarily
addressed to policy makers at various levels of management,
mainly tactical and strategic and analysts, such as marketing,
human resource management, etc. .. [15]
The key to success in implementation of Business
Intelligence systems in the enterprise is the fact that the system
allows to search for answers for business questions advisable
taking into account not only the available data and information
repositories, but also due to the possibility of introducing
and analysing the experiences and knowledge, which
contributes to suggesting the system more reliable solutions.
[2], [9]
How authors described later in the article analysis, both
authors of this article and others developers of InKOM agree
with idea that Business Intelligence systems are master systems
in relation to knowledge management systems. [17] However,
these two systems are inherent elements that only together can
create added value in the form of powerful, intelligent solutions
classified as Business Intelligence. The authors of this article,
consider, along with other researchers, that the Business
Intelligence systems use knowledge management module
for integrating heterogeneous data and to extend
the functionality of smart IT solutions. In addition, according
to the authors, Business Intelligence systems are fully focused
on solving business problems and are designed to improve
decision-making processes faced by the managers
of the organization, and knowledge management systems
provide tools for the proper functioning of business intelligence
systems.[7]
B. TETA BI An Engine of InKOM Developers
Why researchers from the Wrocław University of Economics
decided to develop TETA Business Intelligence system?
The answer is simple: implementation of integrated, intelligent
analytical tools in large companies, corporations
or conglomerates is a natural phenomenon. It is considered
as a standard solution for analytics in big companies to improve
process of developing long-term strategies based on the vast
amount of available data. In contrast, implementation
of Business Intelligence in SME in Poland is still a new
phenomenon and is an interesting practical, scientific, practical
and functional issue for their customers (enterprises SMEs),
suppliers (software developers) and analysts and researchers.
Because the employees of the Institute of Business
Informatics of Wrocław University of Economics with
the UNIT4TETA team with the support of a group of experts in
the field of finance with Credit Agricole and employees
of the Department of Financial Investment and Risk
Management jointly developed a tool called the Intelligent
Dashboard for Managers (pl. InKOM), which is available in the
standard development product offerings of Teta - TETA BI,
Business Intelligence tools dedicated to financial managers
of small and medium-sized enterprises.
Because of TETA BI is a tool for improving decision-making
processes occurring in enterprises, it is necessary to draw
attention to the available functionalities, which will be briefly
presented below.
Business Intelligence systems are designed to improve
business decisions using all available enterprise resources
(collected data, information systems, internal organization, data
and information contained in external sources - Internet:
specific portals, information portals, legal portals, business
competition, statistical data, reports indicated research groups,
business partners, systems, portals, brokerage, etc.), experience
and knowledge of the business participants (including
the company’s employees, systems, data and information
to business partners, knowledge centres, consulting, etc.,)
in order to accurately understand its dynamics. [9]
TETA BI also includes data collection, management,
analysis and distribution of information. It also allows
to download, collect and processing of data from different
sources and systems. TETA BI not only meets this standard but
also allows shared in real time and via mobile device or web
browser anywhere at any time, managerial information
generated ad hoc. With it, the decisions can be made in an
orderly manner and strategic steps determining the
development of the company are based on clear and
unambiguous information.
TETA BI actively supports the controlling organization,
provides complete and reliable information on all areas
of enterprise activity, based on a set of data in the warehouse.
TETA BI allows to create daily reports, summaries, analysis,
multi-period, calculating the deviation between actual
and planned values, valuation services, estimating profitability,
planning and control of the budget. Contains solutions
dedicated to the construction of integrated information
management systems that support the enterprise or institution.
It means that TETA BI is a tool designed primarily to assist
in the processes of management accounting in the enterprise,
allowing you to develop, monitor, control and quickly
implement procedures allowing for eliminating or removing
extreme situations for the company by maintaining solid
financial analysis of both the internal area of the company
(accountancy accountant) and taking into account the data
and external information (controlling).
C. Functionality of TETA BI
Early threat detection in the enterprise is one of the key
elements of strategic management, so it is necessary
for Business Intelligence tools were equipped with basic
functionality that has, among others, TETA BI product [23]:
CREATING VARIOUS VERSIONS OF PLANS - this
module allows generating not only the standard required
in financial reporting or accounting reports and financial
statements, but most importantly allows to create different
versions of the same plan (eg.in the form of the base,
the most pessimistic and most optimistic) with the support
specific solutions forecasting, specified by the user
in the method of planning, while maintaining the simplicity
and readability of the documents prepared. The ability
to create simulations and preparing several versions
of tactical or strategic plans is important from the point
of view of managers, decision-makers, because this
process makes it really increase the profitability
of the enterprise, reduce operating costs, organizational
improvements, expanding markets, improving the quality
of production, through the implementation of the strategy
properly prepared development / operation of the company,
respectively, comprehensively developed strategies based
on real data, information and knowledge contained
in the company.
METHODS OF PLANNING - no company is able
to function without a previously prepared plan of action.
Create strategies and tactic plans at the end of its
operational activities prepared based on the actual
condition of the company is the most desired functionality
of any decision-making process in the company. Therefore,
it is essential that based on the data and information
contained in enterprise data repositories to create the most
accurate scenarios with fundamental issues, such as:
sales and Operations Planning
budgeting
supply chain planning
planning and production scheduling
planning regarding service activities
planning and scheduling of employees
investment planning,
what really constitutes a standard module in TETA BI planning.
DEVIATION ANALYSIS and RATIO ANALYSIS - one
of the key issues in the decision making process
in the company. In TETA BI can be find a possibility
of conducting analysis of indicator taking into account key
issues such as liquidity ratios, profitability, debt, market
activity, market value of shares and share capital, which
is presented using graphs and indicators. This module
allows to conduct analysis not only current, but mainly
on the comparison of specific time periods.
KPI are coordinated with measurements of performance
in relation to the strategic objectives of the company.
Indicators can be different depending on the company
and are usually defined by the shareholders
of the company. Changes in the value of the KPI
is a measure of the degree of realization of the strategic
objectives of the company. [16] Identifying and measuring
the effectiveness of using relevant indicators is essential
in organizations because it allows you to assess whether
the company is headed in the right or wrong direction. This
is typical of managerial control tool used to evaluate
current activities to implement the strategy. TETA BI
allows further defining their own indicators specified users
/ managers in the company, which allows to customize
the tool to the individual needs of the organization.
In the consequence, it can each organization taking into
account KPI can learn faster and develop enterprise wisely.
Should also be noted that individual indicators
are presented using a properly prepared graphic form such
as a visualization meters.
STATUS - the functionality required in the management
plans; TETA BI to assign a status to the specified plans
and presents the progress of the fulfilment of the work
on specific plans, also in a graphical manner,
which facilitates the reception and understanding of
specific phenomena.
RESPONSIBILITY AND IMPLEMENTATION PLAN -
in TETA BI it is possible to assign the appropriate users
to the specific tasks of the plan, the ability to track their
work and process control plan from the involvement
of employees.
ALLERTING - TETA BI allows constant monitoring
of the enterprise; it is a smart tool which is used to inform
relevant staff about the potential, impending or current
abnormalities from the assumptions of those plans,
by sending an additional e-mail messages to specific users
/ decision makers.
Technology implemented in TETA BI permits to use the
system not only in the office but also in the mobile version,
through the use of available web applications or typically
dedicated for mobile devices.
The TETA BI, as previously mentioned includes the ability
to create standard analyses and reports, and of analytical
potential free to use the specific, identified user / financial
managers.
To implement TETA BI solution in the enterprise,
it is necessary to build a data warehouse tailored to the structure
of the enterprise information system in order to enable the most
accurate analysis of individual users of the system. Because
it is an intelligent tool, TETA BI must have in its structure
the knowledge base - in this case warehousing economic
and financial knowledge, necessary for the analysis
and verification of information analysed by the managers.
Knowledge base allows primarily shorten time of financial-
economic knowledge acquisition necessary for the introduction
of formal conducted analyses of data mining.
D. InKOM As A Solution For Financial Managers
The solution on which had been working headquarters
of domain experts, the Intelligent Dashboard for Managers
(InKOM) is an answer for managerial needs of financial
managers. It was created to simplify their everyday work.
Fig. 1 Components of the Intelligent Dashboard for Managers and
their location in the TETA BI system, Source: Korczak J. and others,
InKOM materials
What is new in InKOM? According to fig. 1, access and
ability to view and edit topic maps and ontologies. How is it
different from each other? Topic maps are used to illustrate the
concepts of semantics occurring between specific notions -
which allows managers to read the economic and financial
terminology and accelerates the process of learning. In contrast,
an ontology is a formal model that describes the specific area of
economic and financial expertise. The ontology can find not
only defined herein before concepts and the relationships
between them, but also can specify here the instances of
concepts used functions and logical expressions. Ontology is
built with a view to ease of access and use of knowledge
accumulated in it. [8]
Both the ontology and topic map concepts can serve
managers, such as a preview to define the objectives
of the analyses, which are used for data mining algorithms,
which really helps to avoid logical errors and facilitates the
work, which may shorten the working time of managers and to
improve the effectiveness of their analyses.
The latest innovation in InKOM is the ability to search deep
knowledge of the Internet. It means, managers wishing
to conduct particular analysis can use not only basic data
available in the internal transaction systems, but for example
for buying access to the thematic bases, or to specific internet
sources, it is possible to use needed data and information
as a standard search engine. Such system ability ensures more
reliable data, which affects the quality of subsequent
simulations.
The main motivation of creation InKOM, which was
previously mentioned, was the need for such tool of financial
managers and the willingness of the SME sector organizations
to conduct multivariate analyses with the support of intelligent
solutions to support the decision making process for smaller
funds and the possibility to adapt the functionality of Business
Intelligence technology to the needs of the organization
indicated.
How does the Business Intelligence Process Model run
in the company after the implementation of InKOM? To explain
it briefly, authors use fig. 2 as a support.
External data
sources
(including deep
Internet)
Other internal
separate data
sources
ERP TETA
Constellation
TETA BI data transformation services
(ETL tools)
InKoM
database
Data
warehouse
(SQL
database)
TETA BI
(SQL
database)
Collecting and storing the data from
transaction systems and
from the other separate internal and
external data sources via ETL tools
Mechanisms of
knowledge
exploration in deep
Internet
Analysis Services
database (cubes)
Economic and financial
knowledge database
Collecting and
storing the
planning data
Creating of multidimentional
data structures (cubes)
and structures of economic
and financial knowledge
TETA BI system
InKoM system
Standard analytical
queries and reports
Economic and financial
ontology, topic maps, visual
exploration of economic
and financial knowledge
Business analytics
Managers
Fig. 2 Flow diagram illustrating the operation of the Intelligent
Dashboard for Managers and its interoperability with TETA BI system
Source: Korczak J. and others, InKOM materials
With transactional systems, database systems, or any
heterogeneous, autonomous or integrated with each other
repositories of data, it is possible to build on the basis of data
warehouse, which is the foundation of the InKOM’s
functioning. Before the data is entered into the warehouse, must
go through the process of extraction, transformation and
loading to the warehouse, where on the basis of properly
constructed business queries by users (managers / analysts)
with various departments (eg, marketing, finance, distribution,
storage, etc.) it is possible to obtain detailed and reliable
answers. Indicated by the user query, and actually the results
and patterns of queries can be stored in the so-called. Thematic
data stores (data marts), which on the one hand simplifies
the user navigate through the system and allows you to keep
information governance and structural. Answers to questions
generated business may also be further described by their
creators, in order to facilitate understanding the prepared
reception reports. Ready document (forecasts, analyses, plans,
balance sheets, etc.) after sending to the relevant decision-
makers, when is positively or negatively verified (decision will
be made), the InKOM individual users are informed of these
provisions automatically, just as soon as it will be clicked
by the decision-maker approve button. What really allows
to streamline the flow of information between the different
levels of management in different directions - which is desirable
from the point of view of behaviour organizational governance
and consistency of the company.
IV. PROPOSALS OF KNOWLEDGE MANAGEMENT IN INKOM
PORTAL
Knowledge resources included to the final solution of TETA
BI belong to many categories. Considering all information
categories the following “knowledge granules” are important:
description of economic terms, processes leading to generate
solutions, generated knowledge bases, parameters
of managerial tasks performing and many others.
A. Knowledge Management in InKOM
The key problem of the Wrocław University of Economics
research group was to solve issues related to the transformation
of data mining methods to ontology. Together with other
authors "have proposed a new method to describe
the knowledge of mining databases containing on the one hand
and taxonomic declarative knowledge about the processes
and typical algorithms, data mining, and other procedural
knowledge about the process of exploration. One of the main
objectives of the project wad to create a platform InKOM assist
the manager in the search for knowledge, patterns, relationships
in databases and to control the sequence of tasks and stages
of exploration. We assume that as a result of the proposed
platform enables semi-automatic implementation of complex
tasks of exploration using domain-ontology (in our case,
financial) and an ontology of data mining”[10].
B. Knowledge Transformation and Validation
The aim of the authors of this article was to develop
the concept of knowledge management module, with the main
emphasis on the concept of operation of the rules generator.
In short, analytics in InKOM, to conduct their simulations
are using methods of data mining, like: decision trees
algorithms, association rules, multilayer perceptron, k-means,
self-organizing networks. Module of data exploration as final
result of providing simulation are generated reports and needed
visualizations. This is what user can see after analysis. But,
all algorithms in InKOM as a result also generates lists of rules
explaining reached model. Those lists of rules are the entrance
data for rule generator in knowledge management module.
The authors found that the functioning of the rules generator
is a list of rules created in the process of data mining.
The generator is assumed to work in two stages: verification
produced a list of rules and a transformation of the revised list
of rules to a fixed scheme, in order to save the final list
of the correct list of rules in the knowledge base for exploration.
When the process of analysis meets the end, user before save
generated model should conduct the verification process
of the conducted activities. In InKOM it is possible be clicking
the “verify model” button.
The verification process of generated lists of rules is
important from the point of view of both the issue of the
accuracy of the analyses, as well as on the future operation of
the system, which should generate reliable solutions to defined
problems. In our case, the verification rules must refer first to
the existing knowledge base economy, resulting from the
developed ontology concepts. This action will help to determine
whether the analysis conducted are not implemented in conflict
with the accepted norms and principles of economic, which in
fact is no guarantee of correct preparation solutions from
the perspective of a general domain knowledge. This action
is the first step to check the credibility conducted by analyst
solutions, in order to get the best solution for a specific business
problem. Diagram of this action is shown in Figure 3.
Fig. 3 Rules verification in economical knowledge base.
This process can be completed in two ways.
1. When the message "model inconsistent with economic
knowledge base" - which means that the survey carried out
by the user is in contradiction with the assumptions
contained in the ontology. Window appears with
the message should also contain a button to preview
the contradictions that invented the generator, so that
the user does not commit another error, when constructing
new models. This error should therefore force the user
to edit the analysis.
2. Message "verification process of economic knowledge
base was successful" - which means that the model
constructed by the user is consistent with the principles
contained in the ontology, and additionally, he generated
new rules, which result from the data analysed by him.
The dialog box should contain, therefore, the "save a list
of rules for the operational knowledge base", allowing
subsequent use of these rules for the construction of models
that solve business related queries.
C. Rule Generator Functioning
Rule generator is a component of sight of the user,
but running in the background of the rules to the knowledge
base for operation. Because the process may take some time,
it is necessary that the user was informed the appropriate
display graphics or text message about the recording process ca
rules in the knowledge base of exploratory. Concept
of functioning of rule generator is presented in fig.4.
Fig. 4 Functioning of rule generator.
The results of data mining should be written to particular
form
1. Analysing the content of the rules:
check whether the list of values is used at least in one
part of the conditional rules
Check whether there is a rule relating to the attribute
with a value of not belonging to the set limit value
of an attribute,
check whether the conclusions of the rules does not
correspond to the facts in the premises of other rules.
If the conditions for correctness, granting rule status
of "pre-correct".
If you have not been met, although one condition,
the rule is rejected, and the generator load another rule,
until the exhaustion of the list of rules.
2. Analyzing the relationship between the rules
check whether the rules are contradictory,
check whether the rules are absorbing,
checking that the rules do not contain unnecessary
conditions
check whether the rules form loops.
3. If you have not found any anomaly generator saves this rule
in the form of a decision table (table 1).
TABLE I
RULES TABLE
Years_in_company
RESULTS
1.
-
Not_Granted
2.
>2,5
Granted
4. If the generator detects any anomaly refuses to test
for the rule, then loads the next, until exhaustion
of possibilities.
5. When the generator to check all the possible rules
and create a complete array of rules, stores it in a
knowledge base operating.
D. Rule Transformation Schema
In the description of the tasks of the rules transformer
considered the following tasks:
Selecting a set of rules derived in the previous
algorithm (ie. Generator rules)
Comparison of the components of the rules stored
in the ontology database (collected as input)
Introduction of new components of rules (taken
as the output of this algorithm)
1) Select a set of rules drawn up by Generator
A set of rules constituting an autonomous knowledge base
is used by the method compared. Are considered components
of the rules (page of circumstances and party shares).
It is assumed that the generated rules relate to the concepts
existing in the ontology or require a supplement in case of their
absence. In this context, transformer Rules can be treated as
a class associated with the class Generator Rules (the middle
part of the scheme).
2) Comparison of the rules and concepts of data stored
in the ontology
Each of the components of individual rules (values listed
in the premises and shares rule) is compared with stored
in the ontology. If found in the ontology is equivalent
to the concept - then you should check its consistency with a set
of prepared within Generator Rules. In case of compliance
content go to the next component - if the content is different -
you must complete the tasks described in Section 4 If
a component is missing - you must enter it into the ontology -
creating a list of output data are treated as parameters in the next
phase of the algorithm (p 3). This means that the transformer
Compared rules is a subclass of a more general method
of comparison.
3) Introduction of new data and concepts to the ontology
Created in the previous phase update list ontology is taken into
account when transforming rules. Each of the components
is appropriately localized in the base ontology. This applies
to both conditions and actions occurring in the rules. It should
also take into account the possibility of an appropriate data
aggregation in the process of transformation rules
to the ontology (the relationship of the rule to other treatment
conditions and actions as autonomous category or associated
with other categories). Thus, the process used in the analysis
of the input data transformer ontology rules are a subclass
of the more universal domain input data as output data
generated rules are a subclass of the transformer output.
Message is generated on the concepts introduced.
4) Modify the components in the ontology
Detect a difference in the content of components generated
by the previous algorithm cause is replacing the previous state
of the component description in the form resulting from
the Generator Rules. As before, the input and output
transformer rules relevant classes are subclasses of a more
general nature. It should also take into account the possibility
of removing components treated as obsolete, in which occurs
the use of the appropriate category of type Input and Output.
Message is generated modifications carried out.
The process of transforming the rules of the ontology is
largely automated. The role of the manager is reduced to
intervene in the process of transforming in special situations
in the previous section signalled in the form of messages.
It should therefore be possible to direct the correction
by the manager created content ontology.
E. Elements of knowledge management
As mentioned in the paper, rule generator to proper operation
uses this feature, namely the knowledge base. The rule
generator needs to distinguish between the two knowledge
bases:
1) economic knowledge base based on assumptions stored
in ontologies, which defines the rules are rigid, immutable,
and the user cannot modify them and
2) operational knowledge base, where rules are created
by the user in an indirect way - when setting up the model
in the process of data mining, and directly in the directory where
the knowledge base, the user can create a new file with a list
of rules, which also will be subject to verification of exactly
in the same way as described in the previous section (Fig. 5).
Fig. 5 Functioning of rule generator for manual writing rules by the
user.
Access to both knowledge bases is necessary from the point
of view of the user. It should have full access to view both
bases, in order to quickly find the rules required him to create
such a new model. Knowledge economy, should be made
available in the form of a "read-only" due to that it is created
automatically with the knowledge resulting from the prepared
economic ontology.
Operational knowledge base, in turn, is a set of arrays of rules
generated as a result of a user operation in the data mining
module. By creating a model, the user uses the available data
from the database, where as a result of analysis indicated the
selected algorithms with specific parameters generated a list of
new rules, which are based on actual data. Because the user can
have a better knowledge of both economic and information
technology should be able to manually enter a list of rules in
order to build more reliable models for further.
V. CONCLUSION
Progress in software development is multidirectional. Apart
of extension of functionality advanced software tools
the essential challenge is management of resources used
in the particular packages. The concept of InKOM platform as
a tool BI class addressed for managers of SMEs was reminded
in order to discuss management of knowledge sources
implemented in the package. We focus on knowledge
transformation and validation processes as fundamental
for knowledge management. Different forms and stages
of knowledge bases were considered what made the process
relatively complex and difficult.
The further research will focus on presentation generated
knowledge bases as knowledge granules and explanation of its
interconnections in graphical form. The second direction
of future work is checking integrity of knowledge bases using
heuristic methods.
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Katarzyna Marciniak, MSc degree in field
of Business Informatics (2012), Ph.D. student in the
Department of Business Intelligence Systems at the
University of Economics in Wroclaw, Poland. For
nearly three years leading or co-teaching in the field
of databases, data warehousing and business
intelligence. Author of several international
publications in the field of intelligent ICT
applications in business, knowledge management and
city management in line with the idea of smart city.
Participant of university research groups focused on
business informatics. Coordinator of the Scientific
Club of Information Technologies, Since 2012 member of the Polish Scientific
Society of Business Informatics and Polish Association of Artificial
Intelligence
Mieczyslaw L. Owoc, PhD habilitatus
in Economics, is associate professor of Business
Informatics at Wroclaw University of Economics,
and Head of the Department of Business
Intelligence Systems, with over 30 years
of teaching experience and research in databases
and intelligent systems. He has authored over 120
publications mostly oriented on artificial
intelligence methods and knowledge management
topics. His current research is in modern
information technologies, including cloud and grid
computing with focus on knowledge validation and
knowledge grid. Member of several conference Program Committees,
international journals andscientific association for example: Computer Science
and Information Journal, Informing Science Institute, Business Informatics
Scientific Association and Polish Association of Artificial Intelligence.
... The former is based on common sense, encompassing a variety of phenomena (e.g. the ability to walk or run), roughly what Polanyi referred to as "tacit knowledge", which cannot be captured in language as it is tied to the environment and set in culture and relationships [1]. The interpretation of such knowledge can be subjective [2] (when do you walk slow or fast?, and when do you start running?). On the contrary, the latter has a verbal or written form (e.g. ...
... In this case, "context" is often referred to as environment [7], location [8], or situation [9]. To these three nouns, we can respectively pin the following questions: (1) what resources are you surrounded by?, (2) where are you?, and (3) who are you with, or what are you doing? However, the context can be known or unknown, and if necessary may be identified "manually" by exploiting the expert (or domain knowledge), or "automatically" by using particular types of attributes [10]. ...
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