Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As an organization is a collection of business processes, operations in one business process do influence or have relationship with other business processes. Consequently, from an operational intelligence standpoint, insights from one business process may have their genesis or implications in the performance of some other business process. This paper outlines a framework to sequence insights in the form of performance inferencing across multiple business processes. The framework logically sequences insights across business processes in the form of business rules. The paper illustrates the concepts through a prototype that is implemented in Oracle’s PL/SQL language.
AFFINITY CLUSTERS FOR BUSINESS PROCESS INTELLIGENCEijdms
Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As an organization is a collection of business processes, enhancing business process performance on a continual basis is essential for organizational
success. This paper outlines the concept of affinity clusters that can influence process performance with respect to some success criteria. Affinity refers to the percentage of time certain dimensional factors occur together with respect to some success measure. The paper illustrates the concepts through an adaptation of Oracle E-Business Suite Lead to Forecast business process that is implemented in Oracle’s PL/SQL
language.
Building an effective and extensible data and analytics operating modelJayakumar Rajaretnam
To keep pace with ever-present business and technology change and challenges, organizations need operating models built with a strong data and analytics foundation. Here’s how your organization can build one incorporating a range of key components and best practices to quickly realize your business objectives.
AFFINITY CLUSTERS FOR BUSINESS PROCESS INTELLIGENCEijdms
Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As an organization is a collection of business processes, enhancing business process performance on a continual basis is essential for organizational
success. This paper outlines the concept of affinity clusters that can influence process performance with respect to some success criteria. Affinity refers to the percentage of time certain dimensional factors occur together with respect to some success measure. The paper illustrates the concepts through an adaptation of Oracle E-Business Suite Lead to Forecast business process that is implemented in Oracle’s PL/SQL
language.
Building an effective and extensible data and analytics operating modelJayakumar Rajaretnam
To keep pace with ever-present business and technology change and challenges, organizations need operating models built with a strong data and analytics foundation. Here’s how your organization can build one incorporating a range of key components and best practices to quickly realize your business objectives.
INTEGRATED FRAMEWORK TO MODEL DATA WITH BUSINESS PROCESS AND BUSINESS RULESijdms
Data modeling is an approach to model data by mapping operational tasks iteratively, while associated guidelines are either partly mapped in the data model or expressed through software applications. Since an organization is a collection of business processes, it is essential that data models utilize such processes to facilitate data modeling. Also, data models should incorporate guidelines for completing operational tasks
through the concept of business rules. This paper outlines a unified framework on database modeling and design based on business process concepts that also incorporates business rules impacting business operations. The paper focuses on the relational database and its primary mode of conceptual modeling in the form of an en tity relationship model. Concepts are illustrated through Oracle's database language
PL/SQL and its Web variant PL/SQL Server Pages.
Towards a Software Framework for Automatic Business Process RedesignIDES Editor
A key element to the success of any organization is
the ability to continuously improve its business process
performance. Efficient Business Process Redesign (BPR)
methodologies are needed to allow organizations to face the
changing business conditions. For a long time, practices for
BPR were done case-by-case and were based on the insights
and knowledge of an expert to the organization. It can be
argued that efficiency, however, can further be achieved with
the support of automatic process redesign tools which are few
at the moment. Process mining as a recent approach allows
for the extraction of information from event logs recorded in
different information systems. In this paper we argue that
results driven by process mining techniques can be used to
capture the various types of inefficiencies in the organization
and hence propose efficient redesigns of its business model.
We first give an outline on the current directions towards
automatic BPR followed by a review on the different process
mining techniques and its usage in different applications.
Then, a specific framework of a Software tool that uses process
mining to support automatic BPR is presented.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Managing costs-by-leveraging-procurement-info-intelligentlyRajib Saha
The Chief Procurement Officer's (CPO) primary responsibility today is to create cost-saving strategies. Limited visibility into an organization’s spending in diverse buying categories, however, makes the job challenging.
Read this white paper to learn more about:
Augmenting your enterprise's spend visibility and redefining your procurement strategies
The Inside-Out and the Outside-In views of procurement that give a complete understanding of your cost management landscape
Analysis of Enterprise Resource Planning Systems (ERPs) with Technical aspectszillesubhan
In the past few years, the information
technology has emerged as a key driving force for
growth of business organizations. The trend of
implementing the latest tools and technologies has
reached to maximum extent. The majority of business
organizations has adopted new and innovative tools
to manage their business tasks effectively. In this
scenario, an enterprise resource planning (ERP)
system is a huge information system that
organizations implement to manage their business
tasks. This is a huge information system which links
almost all the business departments and functional
areas. This report presents a detailed analysis of an
enterprise resource planning system. The
implementation of an enterprise resource planning
system requires taking into consideration various
critical factors, which are essential to be considered
in order to make this implementation fruitful. This
report presents a detailed discussion on the
advantages provided by ERPs to business
organizations. The basic purpose of this report is to
analyze critical success factors involved in the
implementation of ERPs. This report also presents
recommendations with every factor that an
organization can follow to make best use of these
systems.
Integrated Analytical Hierarchy Process and Objective Matrix in Balanced Scor...TELKOMNIKA JOURNAL
Measuring organizational performance is pivotal for a comprehensive understanding of strengths,
weaknesses and to improve the quality of any organization’s performance. Balanced Scorecard (BSC) is
the strategic evolution tool that is widely used to measure the organizational performances, and
achievements from various aspects, both financial and non-financial. In this research, BSC was not only a
straight jacket concept but also a high potential tool for measuring and managing tangible and accurate
data through the application of several methods. This research weighted the variables of BSC based on
significance values of Analytical Hierarchy Process (AHP) and Optimization of Measurement with
Objective Matrix (OMAX). Moreover, a recommendation analysis was given based on the cause and effect
analysis of variables and the achievement of Key Performance Indicators (KPIs). The flow of information,
data, and performance measurement processes were designed into Business Intelligence (BI) software
development i.e. BI-MonevDash. The framework and software BI-MonevDash proposed can be used as a
new chosen tool for measuring and monitoring organizational performance. Recommendations could
facilitate the leaders in decision making to improve the organizational performance and reduce risks.
Towards a Software Framework for Automatic Business Process RedesignIDES Editor
A key element to the success of any organization is
the ability to continuously improve its business process
performance. Efficient Business Process Redesign (BPR)
methodologies are needed to allow organizations to face the
changing business conditions. For a long time, practices for
BPR were done case-by-case and were based on the insights
and knowledge of an expert to the organization. It can be
argued that efficiency, however, can further be achieved with
the support of automatic process redesign tools which are few
at the moment. Process mining as a recent approach allows
for the extraction of information from event logs recorded in
different information systems. In this paper we argue that
results driven by process mining techniques can be used to
capture the various types of inefficiencies in the organization
and hence propose efficient redesigns of its business model.
We first give an outline on the current directions towards
automatic BPR followed by a review on the different process
mining techniques and its usage in different applications.
Then, a specific framework of a Software tool that uses process
mining to support automatic BPR is presented.
Presentation on "Data-Aware Business Processes - Formalization and Reasoning Support" at the Dagstuhl Seminar on Verifiably Secure Process-Aware Information Systems.
Original definition Predictive Analytics SPSS Jan 15, 2003 Intriduction SlidesJaap Vink
In 2003 the SPSS Senior Management & Marketing under the leadership of Jack Noonan, Dyke Hensen & Matt Cutler coined the phrase "Predictive Analytics" to explain to the market and to the analysts how SPSS differed from BI companies like BO and Cognos. This file contains the presentation by Matt Cutler introducing PA and the definition to the SPSS employees on January 15 2003
SUCCESS-DRIVING BUSINESS MODEL CHARACTERISTICS OF IAAS PROVIDERS – TOWARDS A ...ijccsa
Despite the highly competitive situation within the Infrastructure as a Service (IaaS) market and the
resulting pressure and uncertainty for the involved providers, only little knowledge is available about
business model characteristics (BMCs) related to success. Merely few qualitative studies are existing that
propose hypotheses on success-driving business model characteristics (SDBMCs), however, a general
and comparative quantitative evaluation and thus an evidence for their impact on business success is still
missing. But this knowledge is essential for IaaS providers as it would allow them to focus their limited
resources and efforts on the truly decisive BMCs and, at the same time, save costs by avoiding activities
and investments of minor importance. Aiming to reduce this gap, a web-based survey was carried out, in
which representatives of IaaS providers of different size rated the level of relevance of the proposed
SDBMCs. As this study is still going on, this paper focuses on presenting the study design and an analysis
of the data collected so far. As a preliminary result, nearly 80 % of the SDBMCs were rated as extremely
important or important, meaning that the existing qualitative research results were confirmed to a high
degree. The relevance of the individual SDBMCs varies greatly depending on the IaaS provider’s size.
This research proposes a maturity model that focuses on providing organizations a holistic view of the alignment between BPM/SOA in their current situation and in relation to their desired state. As such, it supports the organization in evolving towards BPM/SOA alignment, striving for a truly agile and flexible business-driven service-oriented enterprise that is highly responsive to the market dynamics in collaboration with their partners, customers and stakeholders within the ecosystem.
Measuring Enterprise Resource Planning (ERP) Systems Effectiveness in IndonesiaTELKOMNIKA JOURNAL
Refining DeLone and McLean’s (D&M) information system model and technology-organisationenvironment
(TOE) framework, this research identifies the prominent factors that determine ERP system
success. Hypotheses are also drawn based on supporting theories to evaluate the causal relationship
between the success determinants. The level of achievement is measured by system quality, information
quality, service quality, external quality and top management support, which intermediated by perceived
usefulness and user satisfaction towards business benefits. To provide empirical evidence, 86 valid
samples out of 156 were collected using a web survey that targeted ERP users in Indonesia. Furthermore,
Partial Least Squares–Structural Equation Modelling (PLS-SEM) algorithm were applied to check the
proposed hypotheses. The results suggest system quality, information quality and service quality
significantly affect user satisfaction, whereas they moderately impact on perceived usefulness.
Interestingly, external pressures were reported as being the biggest influence on user satisfaction and
positively impacted on perceived usefulness. Despite being fairly predictive to perceived usefulness, top
management support along with general perceptual factors ultimately promote system success by
elevating business benefits.
Business process management (BPM) is the discipline of improving a business process from end to end by analyzing it, modelling how it works in different scenarios, executing improvements, monitoring the improved process and continually optimizing it.
INTEGRATED FRAMEWORK TO MODEL DATA WITH BUSINESS PROCESS AND BUSINESS RULESijdms
Data modeling is an approach to model data by mapping operational tasks iteratively, while associated guidelines are either partly mapped in the data model or expressed through software applications. Since an organization is a collection of business processes, it is essential that data models utilize such processes to facilitate data modeling. Also, data models should incorporate guidelines for completing operational tasks
through the concept of business rules. This paper outlines a unified framework on database modeling and design based on business process concepts that also incorporates business rules impacting business operations. The paper focuses on the relational database and its primary mode of conceptual modeling in the form of an en tity relationship model. Concepts are illustrated through Oracle's database language
PL/SQL and its Web variant PL/SQL Server Pages.
Towards a Software Framework for Automatic Business Process RedesignIDES Editor
A key element to the success of any organization is
the ability to continuously improve its business process
performance. Efficient Business Process Redesign (BPR)
methodologies are needed to allow organizations to face the
changing business conditions. For a long time, practices for
BPR were done case-by-case and were based on the insights
and knowledge of an expert to the organization. It can be
argued that efficiency, however, can further be achieved with
the support of automatic process redesign tools which are few
at the moment. Process mining as a recent approach allows
for the extraction of information from event logs recorded in
different information systems. In this paper we argue that
results driven by process mining techniques can be used to
capture the various types of inefficiencies in the organization
and hence propose efficient redesigns of its business model.
We first give an outline on the current directions towards
automatic BPR followed by a review on the different process
mining techniques and its usage in different applications.
Then, a specific framework of a Software tool that uses process
mining to support automatic BPR is presented.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Managing costs-by-leveraging-procurement-info-intelligentlyRajib Saha
The Chief Procurement Officer's (CPO) primary responsibility today is to create cost-saving strategies. Limited visibility into an organization’s spending in diverse buying categories, however, makes the job challenging.
Read this white paper to learn more about:
Augmenting your enterprise's spend visibility and redefining your procurement strategies
The Inside-Out and the Outside-In views of procurement that give a complete understanding of your cost management landscape
Analysis of Enterprise Resource Planning Systems (ERPs) with Technical aspectszillesubhan
In the past few years, the information
technology has emerged as a key driving force for
growth of business organizations. The trend of
implementing the latest tools and technologies has
reached to maximum extent. The majority of business
organizations has adopted new and innovative tools
to manage their business tasks effectively. In this
scenario, an enterprise resource planning (ERP)
system is a huge information system that
organizations implement to manage their business
tasks. This is a huge information system which links
almost all the business departments and functional
areas. This report presents a detailed analysis of an
enterprise resource planning system. The
implementation of an enterprise resource planning
system requires taking into consideration various
critical factors, which are essential to be considered
in order to make this implementation fruitful. This
report presents a detailed discussion on the
advantages provided by ERPs to business
organizations. The basic purpose of this report is to
analyze critical success factors involved in the
implementation of ERPs. This report also presents
recommendations with every factor that an
organization can follow to make best use of these
systems.
Integrated Analytical Hierarchy Process and Objective Matrix in Balanced Scor...TELKOMNIKA JOURNAL
Measuring organizational performance is pivotal for a comprehensive understanding of strengths,
weaknesses and to improve the quality of any organization’s performance. Balanced Scorecard (BSC) is
the strategic evolution tool that is widely used to measure the organizational performances, and
achievements from various aspects, both financial and non-financial. In this research, BSC was not only a
straight jacket concept but also a high potential tool for measuring and managing tangible and accurate
data through the application of several methods. This research weighted the variables of BSC based on
significance values of Analytical Hierarchy Process (AHP) and Optimization of Measurement with
Objective Matrix (OMAX). Moreover, a recommendation analysis was given based on the cause and effect
analysis of variables and the achievement of Key Performance Indicators (KPIs). The flow of information,
data, and performance measurement processes were designed into Business Intelligence (BI) software
development i.e. BI-MonevDash. The framework and software BI-MonevDash proposed can be used as a
new chosen tool for measuring and monitoring organizational performance. Recommendations could
facilitate the leaders in decision making to improve the organizational performance and reduce risks.
Towards a Software Framework for Automatic Business Process RedesignIDES Editor
A key element to the success of any organization is
the ability to continuously improve its business process
performance. Efficient Business Process Redesign (BPR)
methodologies are needed to allow organizations to face the
changing business conditions. For a long time, practices for
BPR were done case-by-case and were based on the insights
and knowledge of an expert to the organization. It can be
argued that efficiency, however, can further be achieved with
the support of automatic process redesign tools which are few
at the moment. Process mining as a recent approach allows
for the extraction of information from event logs recorded in
different information systems. In this paper we argue that
results driven by process mining techniques can be used to
capture the various types of inefficiencies in the organization
and hence propose efficient redesigns of its business model.
We first give an outline on the current directions towards
automatic BPR followed by a review on the different process
mining techniques and its usage in different applications.
Then, a specific framework of a Software tool that uses process
mining to support automatic BPR is presented.
Presentation on "Data-Aware Business Processes - Formalization and Reasoning Support" at the Dagstuhl Seminar on Verifiably Secure Process-Aware Information Systems.
Original definition Predictive Analytics SPSS Jan 15, 2003 Intriduction SlidesJaap Vink
In 2003 the SPSS Senior Management & Marketing under the leadership of Jack Noonan, Dyke Hensen & Matt Cutler coined the phrase "Predictive Analytics" to explain to the market and to the analysts how SPSS differed from BI companies like BO and Cognos. This file contains the presentation by Matt Cutler introducing PA and the definition to the SPSS employees on January 15 2003
SUCCESS-DRIVING BUSINESS MODEL CHARACTERISTICS OF IAAS PROVIDERS – TOWARDS A ...ijccsa
Despite the highly competitive situation within the Infrastructure as a Service (IaaS) market and the
resulting pressure and uncertainty for the involved providers, only little knowledge is available about
business model characteristics (BMCs) related to success. Merely few qualitative studies are existing that
propose hypotheses on success-driving business model characteristics (SDBMCs), however, a general
and comparative quantitative evaluation and thus an evidence for their impact on business success is still
missing. But this knowledge is essential for IaaS providers as it would allow them to focus their limited
resources and efforts on the truly decisive BMCs and, at the same time, save costs by avoiding activities
and investments of minor importance. Aiming to reduce this gap, a web-based survey was carried out, in
which representatives of IaaS providers of different size rated the level of relevance of the proposed
SDBMCs. As this study is still going on, this paper focuses on presenting the study design and an analysis
of the data collected so far. As a preliminary result, nearly 80 % of the SDBMCs were rated as extremely
important or important, meaning that the existing qualitative research results were confirmed to a high
degree. The relevance of the individual SDBMCs varies greatly depending on the IaaS provider’s size.
This research proposes a maturity model that focuses on providing organizations a holistic view of the alignment between BPM/SOA in their current situation and in relation to their desired state. As such, it supports the organization in evolving towards BPM/SOA alignment, striving for a truly agile and flexible business-driven service-oriented enterprise that is highly responsive to the market dynamics in collaboration with their partners, customers and stakeholders within the ecosystem.
Measuring Enterprise Resource Planning (ERP) Systems Effectiveness in IndonesiaTELKOMNIKA JOURNAL
Refining DeLone and McLean’s (D&M) information system model and technology-organisationenvironment
(TOE) framework, this research identifies the prominent factors that determine ERP system
success. Hypotheses are also drawn based on supporting theories to evaluate the causal relationship
between the success determinants. The level of achievement is measured by system quality, information
quality, service quality, external quality and top management support, which intermediated by perceived
usefulness and user satisfaction towards business benefits. To provide empirical evidence, 86 valid
samples out of 156 were collected using a web survey that targeted ERP users in Indonesia. Furthermore,
Partial Least Squares–Structural Equation Modelling (PLS-SEM) algorithm were applied to check the
proposed hypotheses. The results suggest system quality, information quality and service quality
significantly affect user satisfaction, whereas they moderately impact on perceived usefulness.
Interestingly, external pressures were reported as being the biggest influence on user satisfaction and
positively impacted on perceived usefulness. Despite being fairly predictive to perceived usefulness, top
management support along with general perceptual factors ultimately promote system success by
elevating business benefits.
Business process management (BPM) is the discipline of improving a business process from end to end by analyzing it, modelling how it works in different scenarios, executing improvements, monitoring the improved process and continually optimizing it.
Suggest an intelligent framework for building business process management [ p...ijseajournal
As companies enter into the digital world, information technology is playing a major role in bringing
process improvements to the forefront of business management. In the recent decades, many organizations
have struggled to redesign and improve their business processes to reduce their total cost. The main
contribution of this research study is to propose an intelligent framework that possesses the ability to
employ a database of best practices, business standards, and business activity history in order to permit the
manager to analyze and improve the design of the business processes.
In addition, the other objective of this research is to build a business process or workflow directly from its
process design logic in order to enable rapid process development and deployment. This procedure
requires some technical improvements of the business design, as it is mainly based on building the business
process using Microsoft Office Visio, which communicates the defined business process to the business
process management engine.
Building an Effective & Extensible Data & Analytics Operating ModelCognizant
Building an effective and scalable operating model requires a strong basis in data and analytics management. Creating such an operating model is a step-by-step process, as outlined here.
BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICES ijwscjournal
Making a business rule extraction more dynamic is an open issue, and we think it is feasible if we decompose the business process structure in a set of rules, each of them representing a transition of the business process. As a consequence the business process engine can be realized by reusing and integrating an existing Rule Engine. We are proposing a way for extracting the business rules and then to modify it at the runtime. Business rules specifies the constraints that affect the behaviors and also specifies the derivation of conditions that affect the execution flow. The rules can be extracted from use
cases, specifications or system code. But since not many enterprises capture their business rules in a structured, explicit form like documents or implicit software codes, they need to be identified first, before being captured and managed. These rules change more often than the processes themselves, but changing and managing business rules is a complex task beyond the abilities of most business analysts. The capturing process focuses on the identification of the potential business rules sources. As business logic requirements change, business analysts can update the business logic without enlisting the aid of the IT staff. The new logic is immediately available to all client applications. In current trend the rules are modified or changed in the static time phase. But this paper provides to change the rules at the run time. Here the rules are extracted from the services and can be a changed dynamically. The existing
rules are modified and attached to source code without hindering service to the end user which can be achieved with source control systems. When the rules are revised, it provides a path in budding new business logic. This new business logic can be adopted for the efficient software development.
BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICESijwscjournal
Making a business rule extraction more dynamic is an open issue, and we think it is feasible if we decompose the business process structure in a set of rules, each of them representing a transition of the business process. As a consequence the business process engine can be realized by reusing and integrating an existing Rule Engine. We are proposing a way for extracting the business rules and then to modify it at the runtime. Business rules specifies the constraints that affect the behaviors and also specifies the derivation of conditions that affect the execution flow. The rules can be extracted from use cases, specifications or system code. But since not many enterprises capture their business rules in a structured, explicit form like documents or implicit software codes, they need to be identified first, before being captured and managed. These rules change more often than the processes themselves, but changing and managing business rules is a complex task beyond the abilities of most business analysts. The capturing process focuses on the identification of the potential business rules sources. As business logic requirements change, business analysts can update the business logic without enlisting the aid of the IT staff. The new logic is immediately available to all client applications. In current trend the rules are modified or changed in the static time phase. But this paper provides to change the rules at the run time. Here the rules are extracted from the services and can be a changed dynamically. The existing rules are modified and attached to source code without hindering service to the end user which can be achieved with source control systems. When the rules are revised, it provides a path in budding new business logic. This new business logic can be adopted for the efficient software development.
BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICESijwscjournal
Making a business rule extraction more dynamic is an open issue, and we think it is feasible if
we decompose the business process structure in a set of rules, each of them representing a transition of
the business process. As a consequence the business process engine can be realized by reusing and
integrating an existing Rule Engine. We are proposing a way for extracting the business rules and then
to modify it at the runtime. Business rules specifies the constraints that affect the behaviors and also
specifies the derivation of conditions that affect the execution flow
BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICESijwscjournal
Making a business rule extraction more dynamic is an open issue, and we think it is feasible if we decompose the business process structure in a set of rules, each of them representing a transition of the business process. As a consequence the business process engine can be realized by reusing and integrating an existing Rule Engine. We are proposing a way for extracting the business rules and then to modify it at the runtime. Business rules specifies the constraints that affect the behaviors and also specifies the derivation of conditions that affect the execution flow. The rules can be extracted from use cases, specifications or system code. But since not many enterprises capture their business rules in a structured, explicit form like documents or implicit software codes, they need to be identified first, before being captured and managed. These rules change more often than the processes themselves, but changing and managing business rules is a complex task beyond the abilities of most business analysts. The capturing process focuses on the identification of the potential business rules sources. As business logic requirements change, business analysts can update the business logic without enlisting the aid of the IT staff. The new logic is immediately available to all client applications. In current trend the rules are modified or changed in the static time phase. But this paper provides to change the rules at the run time. Here the rules are extracted from the services and can be a changed dynamically. The existing rules are modified and attached to source code without hindering service to the end user which can be achieved with source control systems. When the rules are revised, it provides a path in budding new business logic. This new business logic can be adopted for the efficient software development.
11.Business Process Agility S.NRelatedStudyTopicRelatedStudyAuthor.docxaulasnilda
11.Business Process Agility S.NRelatedStudyTopicRelatedStudyAuthorType of StudyConceptResult1An Empirical Analysis Of Business Process Agility: Examining The Relationship Of It On Business Process Agility And The Effects Of Business Process Agility On Process OutcomesRobyn L. Raschke, A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy, ARIZONA STATE UNIVERSITY, August 2007"EmpiricalThis research contributes to the understanding of agility for both purchasing and order fulfillment processes. Secondly, this research contributes to an increased understanding of the dynamic capabilities perspective from a business process perspective. And thirdly, this research contributes to the method of testing structurally different theoretical models derived from similar theory. Understanding how IT affects business process agility and the effects of business process agility on process outcomes allows anagement to focus IT investments for specific processes which can affect process outcomes. This allows management to identify which processes should be exploited, developed, and protected (Ray et al. 2004).The results of the validation of the Business Process Agility construct suggest, at least for spanning processes such as order fulfillment and purchasing, that responsiveness, reconfigurability, employee adaptability, and a process centric view are important components of Business Process Agility. Theoretically, agile manufacturing evolved from flexible manufacturing, it is unclear at this time if and how flexibility can be differentiated from reconfigurability. Perhaps better measures for flexibility need to be developed and tested to further understand how these two theoretical concepts are differentiated. IT enables Business Process Agility. For the data collected, the two theoretical models showed relatively good fit with the data; however, further examination and tests revealed that the data in this study had a high probability of fitting the true model which was deemed as model 1: IT as an enabler to agility. Business Process Agility is positively associated with both efficiency and quality Process Outcomes. A smaller subset of the sample was used to further test the effect on the true model, if any; relative financial process outcomes would have on the goodness of fit of the theoretical model. This additional analysis provides additional support that IT enables agility; however, additional research is needed on determining adequate relative financial process outcomes.2Influence of ERP systems on business process Agility Ravi Seethamraju, Diatha Krishna Sundar, The university of Sydney Business school, Sydney Australia , Indian InstitConceptualEnterprise system environment affects on agility. Features of critical business capability. Relationship between agility and It. Process agility affect on orgnaisational outcomes. Post - Rep Implementation effect on agility.According to this study contributes t ...
VALUE-CHAIN ORIENTED IDENTIFICATION OF INDICATORS TO ESTABLISH A COMPREHENSIV...ijmvsc
The process development and optimization potential needs to be driven by the individial coporate value chain.
• The identification of this specific value chain and the related indicators is essential to limit the scope of any analysis and optimization to the core business
• The process framework consisting of clearly defined value chain, the related processes and the corresponding indicators is a pre-requisite for a meaningful and efficient process analysis and continuous process optimization.
This presentation provides a high-level overview of BPM and where it is today.
It also touches on some of the core technologies and standards.
Its focus is on the four specific “Challenges” facing BPM and they are aligned to the four phases of the typical application development life cycle.
1. Discovery
2. Design
3. Development
4. Deployment
LEAN LEVEL OF AN ORGANIZATION ASSESSED BASED ON FUZZY LOGIC csandit
To determine the lean level of an organization a methodology was developed. It was based on a
qualitative assessment approach, including quantitative basis, whose development was
supported using fuzzy logic. Recourse to the use of fuzzy logic is justified by its ability to cope
with uncertainty and imprecision on the input data, as well as, could be applied to the analysis
of qualitative variables of a system, turning them into quantitative values. A major advantage of
the developed approach is that it can be adjusted to any organization regardless of their nature,
size, strategy and market positioning. Furthermore, the proposed methodology allows the
systematically identification of constraint factors existing in an organization and, thus, provide
the necessary information to the manager to develop a holistic plan for continuous
improvement. To assess the robustness of the proposed approach, the methodology was applied
to a maintenance and manufacturing aeronautical organization.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Student information management system project report ii.pdf
International Journal of Database Management Systems (IJDMS)
1. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
DOI : 10.5121/ijdms.2020.12101 1
OPERATIONAL INTELLIGENCE PERFORMANCE
INFERENCING ACROSS BUSINESS PROCESSES
Rajeev Kaula
Department of Information Technology and Cybersecurity, College of Business,
Missouri State University, Springfield, MO, USA
ABSTRACT
Business process intelligence improves operational efficiency that is essential for achieving business
objectives, besides facilitating competitive advantage. As an organization is a collection of business
processes, operations in one business process do influence or have relationship with other business
processes. Consequently, from an operational intelligence standpoint, insights from one business process
may have their genesis or implications in the performance of some other business process. This paper
outlines a framework to sequence insights in the form of performance inferencing across multiple
business processes. The framework logically sequences insights across business processes in the form of
business rules. The paper illustrates the concepts through a prototype that is implemented in Oracle’s
PL/SQL language.
KEYWORDS
Business Intelligence, Process Intelligence, Business Process, Oracle, PL/SQL
1. INTRODUCTION
Business intelligence (BI) is a set of techniques that transform data into information to generate
insights on business operations and competitive environment [11,12,35,41]. While the role of BI
in discovering new business opportunities has gained a lot of attention [10,34,48], the utilization
of its concepts to enhance business process insights through operational intelligence is evolving
[5,15,16,20,22,24,27,28,29,31,32,38,39,42]. As organizations operate through inter-connected
business processes, insights into their process performance through operational intelligence is
essential to achieve business objectives, besides facilitating competitive advantage.
The traditional approach in business intelligence is to first model data in a data warehouse in the
form of multi-dimensional models through an analysis of business operations involving business
activities or business processes [36]. Thereafter, BI generates insights through online analytical
processing (OLAP) analytics with multi-dimensional models in the form of star schema or its
variants [1,25,26,43,30,49]. Such analytics provide information on what combination of
dimension factors are associated with various measure values or its aggregations. Even though
OLAP analytics are important as it allows an organization to make sense of data by providing
insights into business process operations, such insights are essentially a snapshot on some
aspect of these operations.
As an organization is a collection of business processes, operations in one business process do
impact or have relationship with other business processes. Consequently, from an operational
intelligence standpoint, insights from one business process may have their genesis or
implications in the performance of some other business process. This can become evident
through a logical sequencing of individual insights across business processes.
2. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
2
For example, consider three business processes sales, customer service, and shipping that often
exist in many businesses. Let’s say the sales business process analytics generates an insight that
indicates that during the third quarter, sales units are below the success metric in the eastern
region primarily because sales of product Z dropped in the eastern region. Separately, the
customer service business process analytics generates an insight that customer complaints have
increased beyond a minimum threshold for product Z also in the third quarter. Separately too,
the shipping business process analytics generates an insight that during the third quarter late
deliveries went up for product Z. By itself these individual insights have limited scope. So, if
shipping recognizes late deliveries of product Z, they may not fully be aware that customers are
complaining about product Z due to late deliveries. Besides, customer complaints on one
product may create a negative impression about other company product or services. Similarly, if
customer service notices increased product Z complaints it may pass it on to sales or other
business process, but its impact on overall company sales may not be obvious till sales analytic
insights emerges. But if these individual business process insights are chained or sequenced the
scope of the problem affecting business performance becomes much clearer.
One way to logically sequence individual insights across business processes is to (i) identify and
standardize on dimension names across business processes, (ii) facilitate linking of similar
dimensions across business processes during analysis, and (iii) develop a framework to logically
sequence or chain the insights across individual business processes for deeper inferencing.
Identification of dimensions within a business process can be facilitated through a dimension
flow model [23] which aligns business process activities to dimensional information. This
facilitates closer mapping of analytics and its inferencing to a business process. Moreover, as
business process activities are flow-oriented, modeling of dimensional information in a way that
reconciles with the fluidity in process operations is essential.
Linking of similar dimensions across business processes can be facilitated through an enterprise
wide dimension dictionary. Such dictionary can then be utilized by individual business process
analytics to invoke other business processes analytics dealing with similar dimensions.
Logical sequencing of insights can be expressed through the business rules concept [18,37].
Business rules are typically expressed declaratively in condition-action terminology represented
as IF condition THEN action format. A condition is some constraint, while the action clause
reflects the decision or advice. From a business intelligence perspective business rules can also
be utilized to express meaningful insights from OLAP analytics like specification of purposeful
key performance indicators (KPIs), or suggest problem remedies [6,21,22,24]. Such business
intelligence based business rules are referred in the paper as analytic business rules. Below is an
example of an analytic business rule that describes a set of dimension factors that influence Win
probability success factor in a Lead to Forecast business process.
IF Party Type = Organization AND
Sales Channel = Indirect AND
Contact Role = Functional User AND
Product Category = Desktop
THEN Win Probability > 70
There have been attempts at operational intelligence in the form of process monitoring, process
analysis, process discovery, conformance checking, prediction and optimizations [9,17].
Besides, utilization of business rules for business process intelligence has also been explored
[4,13,22,33]. However, these approaches either tie business rules to measures that are defined a
priori through existing policies without much emphasis on database analysis or outline business
rules for specific performance metrics. Technically OLAP analytics through constellation
3. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
3
schema can pool in dimensions across one or more business areas. But when constellation
schema is utilized it may be difficult to know which business process or its activity is involved.
Constellation schema in general is business process agnostic.
This paper in nutshell will outline a framework to accomplish performance inferencing across
multiple business processes. The framework logically sequences insights in the form of business
rules. The paper illustrates the concepts through a prototype that is implemented in Oracle’s
PL/SQL language. Relevant operational intelligence research and dimension flow modeling is
reviewed next. This is followed by an explanation of the framework structure and components.
The paper concludes with an Oracle based prototype that illustrates the implementation of the
framework.
2. RELATED WORK
Operational intelligence analyzes business processes to ensure that operational workflow is
efficient and consistent with their stated objectives. The goal is to optimize such processes for
successful performance. There have been four approaches towards the utilization of BI concepts
for business process based operational analytics. The first approach occurs in three variations in
the form of (i) using BI concepts for dynamic process performance evaluation
[8,22,24,40,44,45,47], (ii) analyze event logs to improve the quality of business processes
[2,3,14], and (iii) monitor process instances to inform users about unusual or undesired
situations [17]. These variations are either short on implementation or apply BI analytics to
individual business processes for discrete performance assessment associated with business
process activities; but there is no emphasis on concepts like performance inferencing across
business processes.
The second approach emphasizes analytics on selected business process activities within the
modeling process [7]. It shows reference to analytic information during business process
modeling as a way to incorporate BI. The approach emphasizes use case scenarios but is short
on implementation details on how to evaluate performance.
The third approach focuses on utilizing BI to reduce redundant specifications of recurrent
business functions when modeling business processes [46]. It fosters reuse of business function
specifications and helps to improve the quality and comparability of business process models.
This approach is focused on the modeling for individual business process only.
The fourth approach [19] outlines a framework to reengineer business processes structure
through data analytics on external data. The approach lacks implementation details and does not
consider concepts like performance inferencing across business processes.
3. DIMENSION FLOW MODEL
Dimension flow model [23] is a graphical conceptual method to identify dimensional (analytic)
information that can be considered as relevant for analyzing business process activities.
Dimension flow modeling is based on information flow modeling concepts [22,30] and is
valuable because it provides a basis for separating information from transactional processing for
analytical processing. Development of dimension flow can be beneficial for (i) understanding
the nature of analytic information without the complexities of data storage, and (ii)
comprehending how business process activities are affected by the such information. Figure 1
shows the generic outline of a dimension flow model.
4. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
4
Figure 1. Dimension Flow Model
In Figure 1, the business process model consists of various activities labeled as Process Activity
1, Process Activity 2, and so on. Each process activity's utilization of dimensional information is
represented through various dimensional entity types like Dim Entity - 1, Dim Entity - 2, and so
on. It is possible that the same dimensional entity type may be utilized by other process
activities, like Dim Entity - 2 interacts with Process Activity 1 and Process Activity 2, while
Dim Entity - 3 interacts with Process Activity - 2 and Process Activity - n.
The dimensional entity types of the dimension flow model are derived from the transactional
entity relationship model (ERD) of the business process. Each dimensional entity type structure
may include some or all the attributes of the associated transactional entity type that are
essential for the purpose of analysis. Unlike a transactional ERD data model the dimensional
entity types are standalone entity types which can later be transformed as dimensions in
associated multi-dimensional models.
Figure 2 shows an example of a dimension flow model adapted from Oracle's Order to Pay
business process. The diagram is a simplification of a similar business process as outlined by
Oracle E-Business Suite (ERP) software. It can be categorized into five stages: (i) configure
sales order, (ii) plan and prepare for order shipment, (iii) ship order and logistics, (iv) invoice
customer on the order shipment, and (v) process order payment.
Figure 2. Order to Pay Dimension Flow Model
5. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
5
4. PERFORMANCE INFERENCING FRAMEWORK
The performance inferencing framework as shown in Figure 3 spans multiple business process.
Each business process will have four intrinsic components referred as Business Process
Analytics, Analytic Business Rules, Analytic Analyzer, and Generate Rationale. Two additional
components Dimension Dictionary and Business Process Insights will be shared among
business processes. The framework components are explained now.
Figure 3. Performance Inferencing Framework
Business Process Analytic component would be star schema analytics to derive information on
the performance of the business process based on some success metric or measure. The results
of the star schema analysis would be stored as business rules in the next component Analytic
Business Rules as a database table. Essentially, each business process will have its star schema
analytics to derive information on the performance of their respective business process based on
some success metric, and then have their analytic results stored as business rules in their
respective analytic business rules dictionary.
The analytic business rules component database table structure is shown in Table 1. The table
attributes are as follows: ID is the primary key. IF Dimension1, IF Dimension2, and so on are
dimension attributes. THEN Measure1, THEN Measure2, and so on are star schema fact
measures. The THEN Flag is the status of the business rule with respect to the success metric.
Table 1. Analytic Business Rules Component
ID IF
Dimension1
IF
Dimension2
. . . THEN
Measure1
THEN
Measure2
. . . THEN
Flag
. . . . . . . . . . . . . . . . . . . . . . . .
To gain deeper insight for performance inferencing each business process will also have one or
more Analytic Analyzer component that will analyze the stored Analytic Business Rules
database table to determine the dimension factors that are affecting the business process success
and store the results in Business Process Insights component as a database table. For example,
one analytic analyzer may count those dimensions that are associated more often with low or
high success metric, while another analytic analyzer may look at combinations of dimension
attributes that are associated more often with low or high success metric.
As business processes in organizations are inter-connected, the Analytic Analyzer will also
explore the implications of its analysis with other business processes through (i) a dimension
6. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
6
dictionary component to determine what other business processes have similar named
dimensions and then (ii) call those other business processes’ Analytic Analyzers for processing
their respective Analytic Business Rules and store their respective insights in Business Process
Insights component database table. From the perspective of the framework’s operation,
whichever business process analytic analyzer starts the analysis thereafter will call the analytic
analyzer of other business processes utilizing the dimension dictionary.
The dimension dictionary lists dimensions associated with each business process. Similar
named dimensions across other business process models may or may not have similar attributes
for analysis. This requires that there should be some standardization on dimension names
pertaining to various analytic entity types in the organization. The structure of dimension
dictionary is shown in Table 2.
Table 2. Dimension Dictionary Component
Dimension Business Process
Dimension X Business Process 1
Dimension X Business Process 2
Dimension Z Business Process 1
Dimension Y Business Process 1
Dimension Y Business Process 2
Dimension K Business Process 2
. . . . . .
The business process insights component database table structure is shown in Table 3. The table
attributes are as follows: ID is the primary key. BP Measure is the business process measure
name. Measure status is the value of the business process measure. Dim1, Dim2, and so on are
dimension names, while Dim1 Value, Dim2 Value, and so on are their associated dimension
values.
Table 3. Business Process Insights Component
ID BP Measure Measure Status Dim1 Dim1 Value Dim2 Dim2
Value
. . .
. . . . . . . . . . . . . . . . . . . . . . . .
Once all the business processes analytic analyzers associated with the initial business process
analytics analyzer have completed their processing, the Generate Rationale component will
interact with Business Process Insights table to output the insights in the form of performance
inferencing sequence analytic business rules. The inferencing sequence insight will be based on
which business process initiates the generation of insight rationale.
The nature of processing performed by Business Process Analytics component, Analytic
Analyzer component, and Generate Rationale component is explained through the prototype
implementation.
5. PERFORMANCE INFERENCING FRAMEWORK IMPLEMENTATION
The performance inferencing framework implementation is demonstrated through a prototype
that utilizes three hypothetical business processes: Sales, Customer Service, and Shipping. Their
respective star schema structure and associated tables are outlined now followed by the logic of
performance inferencing framework components. The dimension structures are not hierarchical.
7. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
7
The prototype is implemented in Oracle through PL/SQL language. The implementation is PC
based. For the sake of simplicity, the number of dimensions and their structure is limited.
5.1. Business Process Star Schema Structure
Sales business process star schema structure is shown in Figure 4. Its success metric (or the fact
measure) is the number of units sold (SalesUnits). Customers (Sales_Customer), product
(Sales_Product), and location (Sales_Location) are the dimensions. The table structure of the
sales business process star schema dimensions and fact measure is listed after Figure 4 from
Table 4 to Table 7.
Figure 4. Sales Business Process Star Schema
Table 4. Sales_Customer
CUSTOMER_ID CUSTOMER_TYPE CONTACT_TYPE
101 Retail Direct
102 Education Indirect
103 Individual Direct
Table 5. Sales_Product
PRODUCT_ID PRODUCT_NAME PROD_GROUP
1001 iPhone X Mobile
1002 Galaxy S9 Mobile
1003 Galaxy S8 Mobile
1004 Surface Pro 6 Laptop
1005 Spectre x360 Laptop
Table 6. Sales_Location
LOCATION_ID STATE COUNTY CITY
101 MO Pulaski Rolla
102 MO Webster Kansas City
103 MO Greene Springfield
8. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
8
Table 7. Sales
ALES_ID SALESUNITS PRODUCT_ID LOCATION_ID CUSTOMER_ID
1 25 1001 101 101
2 15 1002 102 102
3 10 1003 103 103
4 10 1004 101 101
5 5 1005 101 101
6 20 1001 103 103
7 20 1002 102 101
8 10 1003 103 102
9 5 1004 101 102
10 30 1002 102 101
11 15 1001 102 102
12 15 1002 103 103
13 25 1003 102 101
14 10 1004 103 102
15 5 1005 101 103
Customer service business process star schema structure is shown in Figure 5. Its success metric
(or the fact measure) is the time duration of each call (Call_Length). Customers who initiate
contact (Serv_Customer), product covered in the call (Serv_Product), and calls status over time
(Serv_Call_Status) are the dimensions. The table structure of the customer service business
process star schema dimensions and fact measure are listed after Figure 5 from Table 8 to Table
11.
Figure 1. Customer Service Business Process Star Schema
Table 8. Serv_Customer
CUSTOMER_ID
CUSTOMER
_TYPE NUMBER_CALLS
CUST
_STATUS
CUST
_CATEGORY
101 Retail 10 Active Upset
102 Education 2 Inactive Normal
103 Individual 8 Active Upset
9. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
9
Table 9. Serv_Product
PRODUCT_ID PRODUCT_NAME PROD_PROBLEM
1001 iPhone X Not Working
1002 Galaxy S9 Slow
1003 Galaxy S8 Slow
1004 Surface Pro 6 Technical
1005 Spectre x360 Technical
Table 10. Serv_Call_Status
STATUS_ID INIT_STATUS FINAL_STATUS
1001 Completed
1002 Elevated Completed
1003 Elevated Pending
Table 11. Service
CID CALL_LENGTH CUSTOMER_ID PRODUCT_ID STATUS_ID
1 5 101 1001 1001
2 2 101 1005 1003
3 10 103 1005 1003
4 4 101 1001 1001
5 2 103 1005 1002
6 5 101 1005 1002
7 5 101 1005 1003
8 10 103 1005 1002
9 8 101 1005 1002
Shipping business process star schema structure is shown in Figure 6. Its success metric (or the
fact measure) is the number of units shipped (Units_Shipped) and number of units delayed
(Units_Delayed). Supplier of the product (Serv_Supplier), product that is being shipped
(Ship_Product), shipping location (Ship_Location), and carrier for delivery (Ship_Delivery) are
the dimensions. The table structure of the shipping business process star schema dimensions and
fact measures are listed after Figure 6 from Table 12 to Table 16.
10. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
10
Figure 2. Shipping Business Process Star Schema
Table 12. Ship_Supplier
SUPPLIER_ID NAME RELIABILITY
1 Apple Excellent
2 Samsung Good
3 Microsoft Excellent
4 HP Good
Table 13. Ship_Product
PRODUCT_ID PRODUCT_NAME PROD_CONDITION
1001 iPhone X Good
1002 Galaxy S9 Good
1003 Galaxy S8 Fair
1004 Surface Pro 6 Good
1005 Spectre x360 Fair
Table 14. Ship_Location
LOCATION_ID STATE COUNTY CITY
101 MO Pulaski Rolla
102 MO Webster Kansas City
103 MO Greene Springfield
Table 15. Ship_Delivery
DELIVERY_ID CARRIER
1 UPS
2 FedEx
3 USPS
11. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
11
Table 16. Shipping
SHIP
_ID
UNITS
_SHIPPED
UNITS
_DELAYED
LOCATION
_ID
PRODUCT
_ID
SUPPLIER
_ID
DELIVERY
_ID
1 5 0 102 1002 2 1
2 4 2 101 1005 4 3
3 5 3 101 1005 4 3
4 5 1 103 1004 3 2
5 3 2 102 1005 4 1
6 10 1 103 1001 1 2
7 5 2 102 1002 2 3
8 4 3 101 1005 4 2
9 2 0 103 1003 2 1
10 2 3 101 1005 4 3
5.2. Dimension Dictionary Contents
Table 17 is the dimension dictionary associated with the three business processes OLAP
schemas.
Table 17. Dimension Dictionary
Dimension Business Process
Customer Sales
Customer Customer Service
Product Sales
Product Customer Service
Product Shipping
Location Sales
Location Shipping
Supplier Shipping
Delivery Shipping
5.3. Performance Inferencing Logic
Performance inferencing logic is outlined now in three steps.
Step 1: Business Process Analytics component
Individual business processes will have their business process analytics component run on some
routine schedule. Each business process analytics component is a database procedure. All
business processes will categorize their analytics outcome with respect to their success metric as
high, low, or normal.
In the prototype sales business process analytics component is based on unit sales success
metric. Unit sales below 5 are considered low. Unit sales above 5 are considered normal. The
results of analytics in the form of combination of dimension factors with respect to the success
metric are stored in analytic business rule table categorized with status as “low” or “normal”.
The following query yields low status.
select product_name,county,customer_type,salesunits
from sales, sales_product, sales_customer, sales_location
where sales.product_id = sales_product.product_id and
sales.customer_id = sales_customer.customer_id and
12. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
12
sales.location_id = sales_location.location_id and
salesunits <= (select min(salesunits) from sales);
The analytic business rule table for sales business process is shown in Table 18. Appendix A
lists the database procedure so_analytics for sales business process analytics.
Table 18. Sales Business Process Analytic Business Rules
SOA_ID
PRODUCT
_NAME
SALES
_COUNTY
CUSTOMER
_TYPE SALESUNITS
SALES
_FLAG
1 Spectre x360 Pulaski Retail 5 Low
2 Surface Pro 6 Pulaski Education 5 Low
3 Spectre x360 Pulaski Individual 5 Low
4 iPhone X Pulaski Retail 25 Normal
5 Galaxy S9 Webster Education 15 Normal
6 iPhone X Greene Individual 20 Normal
7 Galaxy S9 Webster Retail 20 Normal
8 Galaxy S9 Webster Retail 30 Normal
9 iPhone X Webster Education 15 Normal
10 Galaxy S9 Greene Individual 15 Normal
11 Galaxy S8 Webster Retail 25 Normal
Each row in the above table is an analytic business rule. For example, the business rule
pertaining to soa_id value 1 is as follows:
IF Product_Name = Spectre x360 AND
Sales_County = Pulaski AND
Customer_Type = Retail
THEN Salesunits = 5 AND
Sales_Flag = Low
Customer service business process analytics component is based on the success metric of
number of calls. More than 3 calls reflect deeper concern with the product and are categorized
as high. Calls less than 3 are considered low. The results of analytics in the form of combination
of dimension factors with respect to the success metric are stored in analytic business rule table
categorized with status as “high” or “low”. The following query yields high status.
select product_name,customer_type,init_status,count(*) as complaints_no
from service, serv_product, serv_customer, serv_call_status
where service.product_id = serv_product.product_id and
service.customer_id = serv_customer.customer_id and
service.status_id = serv_call_status.status_id and
init_status = 'Elevated'
group by product_name,customer_type,init_status
having count(*) >= 3;
The analytic business rule table for customer service business process is shown in Table 19.
Database procedure for customer service business process analytics is logically similar to the
sales business process analytics procedure in Appendix A.
13. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
13
Table 19. Customer Service Business Process Analytic Business Rules
CSA_ID
PRODUCT
_NAME
CUSTOMER
_TYPE
INIT
_STATUS
CALLS
_UNITS
CALLS
_FLAG
1 Spectre x360 Retail Elevated 4 High
2 Spectre x360 Individual Elevated 3 High
Each row in the above table is an analytic business rule. For example, the business rule
pertaining to csa_id value 1 is as follows:
IF Product_Name = Spectre x360 AND
Customer_Type = Retail AND
Init_Status = Elevated
THEN Calls_Units = 4 AND
Calls_Flag = High
Shipping business process analytics component is based on the success metric of shipping
delays. If there are more than 3 delays in product shipment it reflects deeper concern with
shipment and categorized as high. Delivery less than 3 are considered low. The results of
analytics in the form of combination of dimension factors with respect to the success metric are
stored in analytic business rule table categorized with status as “high” or “low”. The following
query yields high status.
select carrier, county, product_name, name, sum(units_delayed) as delayed_no
from shipping, ship_delivery, ship_location, ship_product, ship_supplier
where shipping.product_id = ship_product.product_id and
shipping.delivery_id = ship_delivery.delivery_id and
shipping.location_id = ship_location.location_id and
shipping.supplier_id = ship_supplier.supplier_id
group by carrier,county, product_name,name
having sum(units_delayed) > 2;
The analytic business rule table for shipping business process is shown in Table 20. Database
procedure for shipping business process analytics is logically similar to the sales business
process analytics procedure in Appendix A.
Table 20. Shipping Business Process Analytic Business Rules
SHIP_
ANAL_ID CARRIER COUNTY
PRODUCT
_NAME NAME
DELAYED
_NO
DELAY
_FLAG
1 FedEx Pulaski Spectre x360 HP 3 High
2 USPS Pulaski Spectre x360 HP 8 High
3 FedEx Greene iPhone X Apple 1 Low
4 UPS Webster Spectre x360 HP 2 Low
5 FedEx Greene Surface Pro 6 Microsoft 1 Low
6 UPS Greene Galaxy S8 Samsung 0 Low
7 UPS Webster Galaxy S9 Samsung 0 Low
8 USPS Webster Galaxy S9 Samsung 2 Low
14. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
14
Each row in the above table is an analytic business rule. For example, the business rule
pertaining to ship_anal_id value 1 is as follows:
IF Carrier = FedEx AND
County = Pulaski AND
Product_Name = Spectre x360 AND
Name = HP
THEN Delayed_No = 3 AND
Delay_Flag = High
Step 2: Analytics Analyzer component
The general logic of analytics analyzer component is outlined in Figure 7. Appendix B lists the
database procedure analytic_analyzer_so for sales analytics analyzer.
Figure 3. Analytic Analyzer Logic
Each business process will have many analytics analyser database procedures with varying
input parameters. In the prototype, as the sales business process is the starting point for further
analysis, inferencing sequence of business insights will commence with sales.
The sales business process analytics analyzer logic counts how many times each dimension
attribute value has appeared in the sales business process analytics table. If the dimension
attribute has appeared more than once (or whatever be the threshold) then:
a. the procedure inserts the dimension attribute value into business process insights table
along with all other dimension attribute values that exceed the threshold value. It is
possible that an organization may decide to ignore one-time dip, but when a dimension
15. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
15
attribute is having a dip multiple times then it may require also checking beyond the
existing business process with other business processes. In the prototype, product
“Spectre x360” and location “Pulaski” cross the threshold and so are inserted in the
business process insight table.
b. the procedure checks with dimension dictionary for another business process with
similar dimension names and individually call the analytic analyzer for those business
processes. In the dimension dictionary, product dimension is part of customer service
business process OLAP schema, while product and customer dimensions are part of
shipping business process OLAP schemas. So, the sales order analytics analyzer
procedure now calls the analytics analyzer component of customer service and shipping
with product_name and cust_type as input parameter. It is possible to have many
analytics analyzer procedures with different input parameters based on what
combination of dimension attributes have to be checked.
c. The analytics analyzer of customer service counts how many times each product
dimension attribute value has appeared in customer service calls flag value of “High”.
In the prototype product “Spectre x360” has high complaints, so this information is
inserted in the business process insights table. Similarly, the analytics analyzer of
shipping counts how many times each product and location dimension attribute values
has crossed the threshold in shipping business process analytics table with delay flag
value of “High”. In the prototype product “Spectre x360” and location “Pulaski” have
high shipping delays, so this information is inserted in the business process insights
table.
Step 3: Generate Rationale
Once all the additional analytics analyzers have finished their analysis, then the analytics
analyzer that started the inferencing sequence will display the insights stored in the Business
Process Insights table. In the prototype the sales business process analytics analyzer calls the
generate_rationale procedure to display the inferencing chain. Below is the inference sequence
(or chain) generated by the Generate Rationale component.
Sales low at Location Pulaski for Product Spectre x360
Complaints high because Product Spectre x360
Shipping Delay high for Product Spectre x360 for Location Pulaski
Once the performance insights from the three business processes are outlined in the form of
inferencing sequence, the dimension flow model can be utilized to focus on business process
activities affected by the insight. In the prototype the inferencing sequence suggests that the
sales of product Spectre x360 are low and the product has more complaints due to shipping
delays. Accordingly, the shipping business process activity associated with product supplier
needs to be investigated for solution and performance improvement.
6. CONCLUSIONS
This paper proposes an extension on the nature of insights provided by traditional business
intelligence analytics that goes beyond an individual business process. Such deeper insights in
the form of inferencing sequence across multiple business processes provides a richer
assessment on the direction of business performance thereby making an organization more
effective and competitive.Further research is ongoing to enhance the approach by embedding
more complexity in analytic analyzer and dimension dictionary component to further improve
16. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
16
the inferencing sequence. Another area of research is to align inferencing sequence with
quantifiable business objectives with respect to multiple business processes.
REFERENCES
[1] Agrawal, R., Gupta, A. and S. Sarawagi, (1997) “Modeling Multidimensional Databases,”
Proceedings of the Thirteenth International Conference on Data Engineering, pp. 232–243.
[2] Aalst, W. M. P. van der, Reijers, H. A., Weijters, A. J. M. M., Dongen, B. F. van, Alves, Song, A.
K., M de Medeiros and Verbeek, H. M. W. (2007) “Business process mining: An industrial
application,” Information Systems, Vol. 32, No. 1, pp. 713 - 732.
[3] Aalst, Wil van der (2018) “Spreadsheets for business process management: Using process mining to
deal with “events” rather than “numbers”?”, Business Process Management Journal, Vol. 24, No. 1,
pp.105-127.
[4] Arigliano, F., Ceravolo, P., Fugazza, C. and Storelli, D. (2008) “Business Metrics Discovery by
Business Rules,” in M. D. Lytras, J. M. Carroll, E. Damiani (editors), Emerging Technologies and
Information Systems for the Knowledge Society: Lecture Notes in Computer Science, Vol. 5288,
pp. 395-402.
[5] Baars, H., Felden, C., Gluchowski, P., Hilbert, A., Kemper, H. G. and Olbrich, S. (2014) “Shaping
the next incarnation of business intelligenc” Business & Information Systems Engineering, Vol. 6,
No. 1, pp. 11-16.
[6] Blasum, R. (April 2007) “Business Rules and Business Intelligence,” Information Management
Magazine.
[7] Bucher, T., Gericke, A. and Sigg, S. (2009) “Process-centric business intelligence,” Business
Process Management Journal, Vol. 15, No. 3, pp. 408-429.
[8] Castellanos, M., Casati, F., Sayal, M. and Dayal, U. (2005) “Challenges in business process
analysis and optimization,” in C. Bussler and M. C. Shan (Eds.), Technologies for E-Services, 6th
International Workshop, TES 2005, Trondheim, Norway, September 2-3, 2005, Revised Selected
Papers, volume 3811 of Lecture Notes in Computer Science, Springer-Verlag, pp. 1-10.
[9] Castellanos, M., Medeiros, A. Alves de, Mendling, J., Weber, B., and Weijters, A. (2009) “Business
Process Intelligence,” in J. Cardoso and W. van der Aalst (Eds.) Handbook of Research on Business
Process Modeling, IGI: Hershey, PA, pp. 456 - 480 .
[10] Chen, H., Chiang, H. L. Roger and Storey, Veda C. (2012) “Business Intelligence and Analytics:
from Big Data to Big Impact” MIS Quarterly, Vol. 36, No. 4, pp. 1165-1188.
[11] Cody, W. F., Kreulen, J. T., Krishna, V. and Spangler, W. S. (2002) “The integration of business
intelligence and knowledge management,” IBM Systems Journal, Vol. 41, No. 4, pp. 697-713.
[12] Dayal, U., Wilkinson, K., Simitsis, A. and Castellanos, M. (2009) “Business Processes Meet
Operational Business Intelligence,” Bulletin of the Technical Committee on Data Engineering, Vol.
32, No. 3, pp. 35-41.
[13] Debevoise, T. (2005) Process Management with a Business Rules Approach: Implementing The
Service Oriented Architecture, Business Knowledge Architects, Roanoke.
[14] Dumas, M., Rosa, M. La, Mendling, J. and Reijers, H. A. (2013) “Process intelligence,” in
Fundamentals of Business Process Management, Springer: Berlin Heidelberg, pp. 353-383.
[15] Elbashira, M. Z., Collierb, P. A. and Davernb, M. J. (2008) “Measuring the effects of business
intelligence systems: The relationship between business process and organizational performance,”
International Journal of Accounting Information Systems, vol. 9, no. 3, pp. 135-153.
[16] Golfarelli, M., Rizzi, S. and Cella, I. (2004) “Beyond data warehousing: what's next in business
intelligence?,” Proceedings of the 7th ACM international workshop on Data warehousing and
OLAP, pp. 1-6.
[17] Grigori, D., Casati, F., Castellanos, M., Dayal, U. , Sayal, M. and Shan, M. (2004) “Business
process intelligence,” Computers in Industry, Vol. 53, No. 3, pp. 321-343.
[18] Halle, B. V. (2002) Business Rules Applied, John Wiley & Sons, New York, NY.
[19] Jha, M., Jha, S. and O'Brien, L. (2016) “Combining big data analytics with business process using
reengineering,” Proceedings of the IEEE Tenth International Conference on Research Challenges in
Information Science, Grenoble, France.
[20] Kakhki, M.D. and Palvia. P. (2016) “Effect of Business Intelligence and Analytics on Business
Performance,” Proceedings of the Twenty-second Americas Conference on Information Systems,
San Diego, pp. 1-10.
17. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
17
[21] Kaula, R. (2009) “Business Rules for Data Warehouse,” World Academy of Science, Engineering
and Technology, Vol. 59, pp. 951- 959.
[22] Kaula, R. (2012) “Business Intelligence Process Metrics Specification: An Information Flow
Approach,” International Journal of Computers and Their Applications, Vol. 19, No. 4, pp. 262-272.
[23] Kaula, R (2017) “Operational Intelligence through Performance Trends: An Oracle Prototype,”
International Journal of Business Intelligence and Systems Engineering, Vol. 1, No. 2, pp. 140-165.
[24] Kaula, R (2018) Affinity Clusters For Business Process Intelligence, International Journal of
Database Management Systems (IJDMS), Vol.10, No.5, October 2018.
[25] Kimball, R. and Ross, M. (2002) The Data Warehouse Toolkit: The Complete Guide to
Dimensional Modeling, John Wiley & Sons, New York.
[26] Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. and Becker, B. (2008) The Data Warehouse
Lifecycle Toolkit, John Wiley & Sons, New York.
[27] Larson, D. and Chang, V. (2016) “A review and future direction of agile, business intelligence,
analytics and data science, International Journal of Information Management,” Vol. 36, No. 5, pp.
700-710.
[28] Linden, M., Felden, C., and Chamoni, P. (2010) “Dimensions of business process intelligence,”
Proceedings of the International Conference on Business Process Management, pp. 208-213.
[29] Lönnqvist, A. and Pirttimäki, V. (2006) “The Measurement of Business Intelligence,” Information
Systems Management, Vol. 23, No. 1, pp. 32-40
[30] Loshin, D. (2003) Business Intelligence: The Savvy Manager’s Guide, San Francisco: Morgan
Kaufman.
[31] Marjanovic, O. (2007) “The Next Stage of Operational Business Intelligence: Creating New
Challenges for Business Process Management,” Proceedings of the 40th Annual Hawaii
International Conference on System Sciences, pp. 215c-215c.
[32] Marjanovic, O. (2010) “Business Value Creation through Business Processes Management and
Operational Business Intelligence Integration,” 43rd Hawaii International Conference on System
Sciences (HICSS), pp. 1-10.
[33] Mircea, M. and Andreescu, A. (2009) “Using Business Rules in Business Intelligence” Journal of
Applied Quantitative Methods, vol. 4, no. 3, pp. 382-393.
[34] Olivia, R. (2009) Business Intelligence Success Factors: Tools for Aligning Your Business in the
Global Economy, Wiley & Sons, Hoboken, N.J.
[35] Olszak, C. M. and Ziemba, E. (2007) “Approach to Building and Implementing Business
Intelligence Systems,” Interdisciplinary Journal of Information, Knowledge, and Management, vol.
2, pp. 135-148.
[36] Ponniah, P. (2010) Data Warehousing Fundamentals for IT Professionals (2nd Edition), New York:
John Wiley & Sons.
[37] Ross, R. (2003) Principles of the Business Rule Approach, Addison-Wesley, Boston.
[38] Richards, R., Yeoh, W., Chong, A.Y.L. and Popovič, A. (2019) “Business Intelligence Effectiveness
and Corporate Performance Management: An Empirical Analysis,” Journal of Computer
Information Systems, Vol. 59, No. 2, pp. 188-196.
[39] Rudometkina, M. N. and Spitsyn, V. G. (2014) “Detection of processing model basic elements in
intellectual analysis of flexible processes through business process intelligence,” Proceedings of the
9th International Forum on Strategic Technology (IFOST), pp. 97-101.
[40] Sa, J. O. and Santos, M. Y. (2017) “Process-driven data analytics supported by a data warehouse
model,” International Journal of Business Intelligence and Data Mining, Vol.12, No.4, pp.383-405.
[41] Sharda, R., Dalen, D. and Turban, E. (2013) Business Intelligence: A Managerial Perspective on
Analytics, Prentice-Hall, Upper Saddle River.
[42] Shollo, A. and Galliers, R.D. (2016) “Towards an understanding of the role of business intelligence
systems in organisational knowing,” Information Systems Journal, Vol. 26, No. 4, pp. 339-367.
[43] Sen, A. and Sinha, A. (2005) “A comparison of data warehousing methodologies,” Communications
of the ACM, vol. 48, no. 3, pp. 79-84.
[44] Sohail, A. and Dominic, P. D. D. (2012) “A gap between Business Process Intelligence and redesign
process,” Proceedings of the 2012 International Conference on Computer & Information Science
(ICCIS), pp. 136-142.
[45] Tan, W., Shen, J.W., Xu, L., Zhou, B. and Li, L. (2008) “A Business Process Intelligence System
for Enterprise Process Performance Management,” IEEE Transactions on Systems, Man, and
Cybernetics, vol. 38, no. 6, pp. 745-756.
18. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
18
[46] Thom, L.H., Reichert, M., Chiao, C.M., Iochpe, C. and Hess, G.N. (2008) “Inventing Less, Reusing
More, and Adding Intelligence to Business Process Modeling,” in S.S. Bhowmick, J. Küng, R.
Wagner (editors), Database and Expert Systems Applications: Book Series Title: Lecture Notes in
Computer Science, vol. 5181, pp. 837-850.
[47] Torres, R., Sidorova, A. and Jones, M.C. (2018) “Enabling firm performance through business
intelligence and analytics: A dynamic capabilities perspective,” Information & Management,
Vol.55, No. 7, pp. 822-839.
[48] Watson, H. J. and Wixom, B.H. (2007) “The Current State of Business Intelligence,” Computer,
vol. 40, no. 9, pp. 96-99.
[49] Wrembel, R. and Koncilia, C. (2007) Data warehouses and OLAP: concepts, architectures, and
solutions, Hershey: Idea Group Inc..Lee, S.hyun. & Kim Mi Na, (2008) “This is my paper”, ABC
Transactions on ECE, Vol. 10, No. 5, pp120-122.
APPENDIX A
create or replace procedure so_analytics as
cursor so_low is
select product_name,county,customer_type,salesunits
from sales, sales_product, sales_customer, sales_location
where sales.product_id = sales_product.product_id and
sales.customer_id = sales_customer.customer_id and
sales.location_id = sales_location.location_id and
salesunits <= (select min(salesunits) from sales);
so_low_row so_low%rowtype;
cursor so_high is
select product_name,county,customer_type,salesunits
from sales, sales_product, sales_customer, sales_location
where sales.product_id = sales_product.product_id and
sales.customer_id = sales_customer.customer_id and
sales.location_id = sales_location.location_id and
salesunits >= (select avg(salesunits) from sales);
so_high_row so_high%rowtype;
begin
for so_low_row in so_low loop
insert into sales_ord_analytics values
(soa_seq.nextval,so_low_row.product_name,so_low_row.county,so_low_row.customer_Type,so_low_ro
w.salesunits,'Low');
end loop;
for so_high_row in so_high loop
insert into sales_ord_analytics values
(soa_seq.nextval,so_high_row.product_name,so_high_row.county,so_high_row.customer_Type,so_high_
row.salesunits,'Normal');
end loop;
end;
APPENDIX B
create or replace procedure analytic_analyzer_so is
cursor cur1 is
select sales_county, count(*) sales_county_ctr from sales_ord_analytics where sales_flag = 'Low' group
by sales_county having count(*) > 1
order by sales_county_ctr desc;
cur1_row cur1%rowtype;
cursor cur1_ext is
select dim,bp from dim_dict where dim = 'Location' and bp <> 'Sales';
19. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
19
cur1_ext_row cur1_ext%rowtype;
cursor cur2 is
select product_name, count(*) product_name_ctr from sales_ord_analytics
where sales_flag = 'Low' group by product_name order by product_name_ctr desc;
cur2_row cur2%rowtype;
cursor cur2_ext is
select dim,bp from dim_dict where dim = 'Product' and bp <> 'Sales';
cur2_ext_row cur2_ext%rowtype;
cursor cur3 is
select customer_type, count(*) customer_type_ctr from sales_ord_analytics
where sales_flag = 'Low' group by customer_type having count(*) > 1
order by customer_type_ctr desc;
cur3_row cur3%rowtype;
cursor cur3_ext is
select dim,bp from dim_dict where dim = 'Customer' and bp <> 'Sales';
cur3_ext_row cur3_ext%rowtype;
tlocation varchar2(20); tproduct varchar2(20); tcustomer varchar2(20);
flag_cs_loc varchar2(3) := 'off'; flag_cs_prod varchar2(3) := 'off';
flag_cs_cust varchar2(3) := 'off'; flag_ship_loc varchar2(3) := 'off';
flag_ship_prod varchar2(3) := 'off'; flag_ship_cust varchar2(3) := 'off';
begin
open cur1;
fetch cur1 into cur1_row;
if cur1%found then
if cur1_row.sales_county_ctr > 1 then
tlocation := cur1_row.sales_county;
dbms_output.put_line('tlocation '||tlocation);
for cur1_ext_row in cur1_ext
loop
if cur1_ext%found then
if cur1_ext_row.bp = 'Customer Service' then
flag_cs_loc := 'on'; --analytic_analyzer_cs;
elsif cur1_ext_row.bp = 'Shipping' then
flag_ship_loc := 'on'; --analytic_analyzer_ship;
end if;
end if;
end loop;
end if;
end if;
open cur2;
fetch cur2 into cur2_row;
if cur2%found then
if cur2_row.product_name_ctr > 1 then
tproduct := cur2_row.product_name;
dbms_output.put_line('tproduct '||tproduct);
for cur2_ext_row in cur2_ext
loop
if cur2_ext%found then
if cur2_ext_row.bp = 'Customer Service' then
flag_cs_prod := 'on'; --analytic_analyzer_cs;
elsif
20. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
20
cur2_ext_row.bp = 'Shipping' then
flag_ship_prod := 'on'; --analytic_analyzer_ship;
end if;
end if;
end loop;
end if;
end if;
open cur3;
fetch cur3 into cur3_row;
if cur3%found then
if cur3_row.customer_type_ctr > 1 then
tcustomer := cur3_row.customer_type;
dbms_output.put_line('tcustomer '||tcustomer);
for cur3_ext_row in cur3_ext
loop
if cur3_ext%found then
if cur3_ext_row.bp = 'Customer Service' then
flag_cs_cust := 'on'; --analytic_analyzer_cs;
elsif
cur3_ext_row.bp = 'Shipping' then
flag_ship_cust := 'on'; --analytic_analyzer_ship;
end if;
end if;
end loop;
end if;
end if;
-- insert into business process insights table
if (tlocation is not null) and (tproduct is not null) then
insert into bp_insights values(rational_seq.nextval,'Sales
','low','Location',tlocation,'Product',tproduct,null,null,null,null);
end if;
if (tlocation is not null) and (tproduct is not null) and (tcustomer is not null) then
insert into bp_insights values(rational_seq.nextval,'Sales
','low','Location',tlocation,'Product',tproduct,'Customer',tcustomer,null,null);
end if;
-- call other BP analytic analyzers
-- So there could be multiple procedures based on what dimension attribute is null
if (flag_cs_loc = 'on') and (flag_cs_prod = 'on') and (flag_cs_cust = 'on') then
--analytic_analyzer_cs('Sales',tproduct,tlocation,tcustomer);
end if;
if (flag_cs_loc = 'on') and (flag_cs_prod = 'on') and (flag_cs_cust = 'off') then
--analytic_analyzer_cs('Sales',tproduct,tlocation);
end if;
if (flag_cs_loc = 'off') and (flag_cs_prod = 'on') and (flag_cs_cust = 'off') then
analytic_analyzer_cs('Sales',tproduct);
end if;
if (flag_ship_loc = 'on') and (flag_ship_prod = 'on') and (flag_ship_cust = 'on') then
--analytic_analyzer_ship('Sales',tproduct,tlocation,tcustomer);
end if;
if (flag_ship_loc = 'on') and (flag_ship_prod = 'on') and (flag_ship_cust = 'off') then
analytic_analyzer_ship('Sales',tproduct,tlocation);
21. International Journal of Database Management Systems (IJDMS) Vol.12, No.1, February 2020
21
end if;
if (flag_ship_loc = 'on') and (flag_ship_prod = 'off') and (flag_ship_cust = 'off') then
--analytic_analyzer_ship('Sales',tlocation);
end if;
--display inferencing sequence
infer_seq;
end;