International Journal of Intelligent Networks 1 (2020) 76–84
Contents lists available at ScienceDirect
International Journal of Intelligent Networks
journal homepage: www.keaipublishing.com/en/journals/
international-journal-of-intelligent-networks
A survey on various applications of prescriptive analytics
S. Poornima *, M. Pushpalatha
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India
A R T I C L E I N F O
A B S T R A C T
Keywords:
Prescriptive analysis
Data analysis
Big data analytics
Decision making needs more support to gain momentum with increasing trend in big data analytics. Conventionally, descriptive and predictive analytics supported the decision making, however, the outcomes of the
resultant analytics is of a potentially lower one. Hence, to improve the decision making support in big data analytics, a newer technique is adopted, namely prescriptive analytics, which offers an improved advancement in
predicting the probable consequences and its outcome. Best outcome is achieved using optimization techniques in
prescriptive analytics that identifies the uncertainties in making the decisions better. Since optimization improves
the effectiveness of prescriptive analytics w.r.t varied applications. In order to address the prescriptive analytics
technique over various application and to improve the effectiveness of research over prescriptive analytics, in this
paper a survey on the applications of prescriptive analytics over big data analytics is addressed. Further, the key
issues in prescriptive analytics and comparisons between several applications are as of prescriptive analytics is
discussed in terms of the respective techniques and evaluation over it are discussed.
1. Introduction
recommendations of predictions [35]. This analytics is used to predict
multiple future predictions and makes the company to use the possible
outcome for their future [36].
The prescriptive analytics combines both the tools and techniques
and that includes algorithms, business rules, computational modelling
and machine learning procedures. Such techniques are applied over
different datasets: both transactional and historical, big data and real
time data. Though it might have many advantageous, it is very complex
to administer and companies are avoiding it on its daily course. However,
imbibing it over the business leads to better decision making on the
bottom line of company. To successfully optimize the production, inventory and scheduling in large companies, prescriptive analytics is used
and that delivers better products for optimising the experience of the
customers [41]. The usage of such analytics over different companies as
value added service is shown in Fig. 1.
Prescriptive analytics is the highest capability of analytics today that
decides in improving the firms through the predictive and descriptive
analytics. The prescriptive analytics assists the users in providing optimal
solution to the problem and chooses best decision among different alternatives. Unlike predictive and descriptive solutions, the prescriptive
analytics provides optimal solution to the problem. Here, the operational
research is the core model in prescriptive analytics [42] and this can be
applied over many companies for optimal outcome.
The analytical option is been an overwhelming task and it is
Big Data essentially means huge volumes of data that is unable to be
processed efficiently using conventional database management techniques. Big Data can be defined as the quantity of information that is
beyond technology’s capacity for storage, management or processing in
an efficient manner. The limitations are found solely through robust
analyses of the data, explicit processing requirements as well as capacities of tools utilized to analyse it [1]. With increasing data-driven
business environment, the analytical decision is considered critical due
to increased size of data. The executives with very limited expertise
makes poor analytics and this paved the way for automated analytics and
better decision making [34]. Each time a novel storage medium is
developed that provides easier access to data volume explosion. Since,
massive data is in unstructured form of image or text. The past research
concentrates mainly on structuring the unstructured data. Notion of Big
Data analytics is to improve the efficiency, effectiveness and profitability
of documented enterprise [1].
The prescriptive analytics makes the users to prescribe the possible
outcomes to attain a better solution or it provides a better advice [3]. This
attempts to enumerate the future decisions before plunging into some
decisions [24]. This analytics not only predicts on what is about to
happen but why and how the problem exist and provides possible recommendations based upon the actions and provides a better
* Corresponding author.
E-mail addresses: srmpoornimacse@yahoo.com (S. Poornima), pushpalatha.m@ktr.srmuniv.ac.in (M. Pushpalatha).
https://doi.org/10.1016/j.ijin.2020.07.001
Received 14 February 2020; Received in revised form 8 May 2020; Accepted 7 July 2020
2666-6030/© 2020 The Author(s). Published by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
S. Poornima, M. Pushpalatha
International Journal of Intelligent Networks 1 (2020) 76–84
Fig. 1. Usage of different analytics by companies [42].
programming design, since the idea is applied over the model, which is
easier to implement over the programming language. Once the reflective
information property is changed or adjusted, the system design is
changed and this can add or delete the system components and provides
interaction between the required components. The English language
plays a major part, however, the language part does not play a major part,
refer [39].
categorised into three distinct types. The three analytical options are
considered complement of each other and it is no longer a better option
than the other analytics. To provide a holistic view on business market
and to compete with the business market, the robust analytics include:
descriptive analytics, predictive analytics and prescriptive analytics.
Here, the descriptive uses data aggregation and mining task to provide
results based on the past values. The predictive model uses statistics and
the answers are predicted based upon future values. Prescriptive analytics uses optimization algorithms to attain possible outcome on what
has to be done to attain better output at the future. The prescriptive
analytics provides better advice[3] on the possibilities for better
outcome.
Hence, the main aim of the paper is to provide a survey on available
prescriptive analytic solutions applied to overcome the issues over
various applications. The main contribution of the work is to show how
various applications is applied with prescriptive analytics technique. To
the best of our knowledge this is the first survey to be carried out in the
field of prescriptive analytics and on its applications.
In the present study, applications of prescriptive analytics is explored
with comparative study that show how the analytics is efficient in Big
Data environment. Here, various literatures are reviewed for the purpose
of surveying, however, the literature required to survey the prescriptive
analytics is very few. Since, the research is moving forward their interest
in this analytics and it is been a new emergent field in mining of big data
analytics. Henceforth, this paper would be a starting point for the future
researches in exploring the prescriptive analytics techniques used previously by the researches.
2.2. The value of prescriptive aspects
There are multiple ways to show that prescriptive analytics is valuable, include:
1. First, the analytics is designed by a developer in a function manner,
which can be applied over non-functional updates and this makes the
design easier.
2. Secondly, the prescriptive analytics is considered as generic advice
that helps in changing the design in a global perspective. This makes
the developer to match with the appropriate libraries based on the
required application.
3. Thirdly, the implementation is carried out by grouping the objectives
that supports prescriptive analytics as a global concept.
4. Finally, the aspects of prescriptive analytics supports the global
changes and that adopts the changes in the design based on the new
design and can be applied over it. This helps in reducing the bugs and
the changes is made in some requisite places. Similarly, the reapplication of different advice can be imbibed and the dependency
and schedulability is checked for automatic running of the program.
This helps in achieving multiple design options and modifications
over the design [39].
2. Prescriptive analytics
Prescriptive analytics is defined as a mathematical technique, which
determines computationally the set of high valued actions or decisions.
The decisions are obtained from the varied set of objectives, constraints
and requirements that improves the performance of a particular sector
[37]. The prescriptive analytics combines the use of models, rules and
data using mathematical models, which combines the hybrid data and
the rule. It helps to solve the problems associated with the many fields
that include: Big Data, operational research, decision support system and
optimization [38].
2.3. Five pillars
Prescriptive analytics is one such technique to structure the data
items of the Big Data. Prescriptive analytics identifies the alternatives
required to maximize or minimize the objective function using optimization. Areas like operations, business, finance and marking are the key
areas in which the analytics is used. In each technique, the best price and
advertising policy is used to increase the revenue that includes storing
maximum cash in ATM, managing risk using best investment plan for
retirement. The use of statistical and mathematical operations are combined with optimization technique to look forward for better decisions in
prescriptive analytics [26]. Prescriptive analytics describes, explains and
predicts the way in suggesting the courses of action need in future. This
2.1. Specification
The prescriptive analytics considers the advice to be applied as a
model or rules over a design and they are independent from the
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S. Poornima, M. Pushpalatha
International Journal of Intelligent Networks 1 (2020) 76–84
optimizes the applications/business to achieve the goal with better objectives. The decision alternative is associated with the prediction
outcome. Optimization and simulations are used for better decision
making in prescriptive analytics.
There are the five major pillars of the prescriptive analytics, which is
shown in Fig. 2.
of surveying, however, the literature required to survey the prescriptive
analytics is very few. Since, the research is moving forward their interest
in this analytics and it is been a new emergent field in mining of big data
analytics. Henceforth, this paper would be a starting point for the future
researches in exploring the prescriptive analytics techniques used previously by the researches.
1. Adaptive Algorithms: As the volume, velocity and variety of data
grows rapidly, prescriptive analytics technology ought to have the
capability to recalibrate by default to all its built-in algorithms, and
also to generate novel protocols. This complete recalibration requires
to be adaptive – dynamic and/or continuous –to help the business
procedure which is being managed in an ongoing manner.
2. Integrated Predictions & Prescriptions: The prediction as well as
prescription ought to work together for prescriptive analytics to
ensure its guaranteed promise. Integrating the two is the key for
extensive adoption as well as inheriting the values of prescriptive
analytics.
3. Hybrid Data: Hybrid data is the fusion of unstructured as well as
structured data. Hybridized data enables the business to utilize both
structured and unstructured data to make the best possible decision.
Ability to process hybrid data makes the prescriptive analytics technology transformative. In recent days, many of the businesses work
on structured data - numbers and categories.
4. Prescriptions & Side Effects: Prescriptions that recommend, time
oriented actions to enhance the future uses various methods.
5. Feedback Mechanism: Prescriptions are usually, time-sensitive action plans which include alterations over few actionable influencers
to anticipate 1 or more estimated problems (or for gaining from 1 or
more estimated opportunities) [23].
3. Review on applications of perspective analytics
The framework of the prescriptive analytics (shown in Fig. 3) varies in
accordance with several domains, which are discussed below:
3.1. Data provisioning in streaming networks
Nechifor et al. [2], demonstrated an ongoing study in CityPulse
project with regards to a fresh look into the union between autonomous
networking as well as stream based prescriptive analytics which is
employed to dynamic city issues. His work suggested the goal of getting
the probable role of prescriptive analytics within the autonomous loop
with a clear focus on steps of analyses. Problems under examination were
determined on the choosing of correct stream provisions that are supposed to be used on different city services.
3.2. Research and development
Song et al. [4], initiated a prescriptive analytics model, InSciTe
advisory, for providing advice to research scholars on their future
research directions as well as strategies. The model analyses 1000s of
diverse kinds of information sources like papers, reports, news, magazines, collective intelligence data and so on. It comprises two primary
components of descriptive analysis as well as prescriptive analysis. If a
research scholar is provided, descriptive analysis gives solution from
previous history of activities as well as research with respect to the
specified research scholar. Prescriptive analysis puts forward a set of role
model research scholars as well as the ways and means to be as the role
model researchers. The analytical outcomes as well as their description
regarding the particular research scholar are automated created as well
as stored as a report.
Weber et al. [9], focused on the primary step in the combination of
prescriptive analysis with scenario methods for providing strategic
growth following the usage of InSciTe, a data prescriptive analytics
application. InSciTe supported the development of research scholars’
individual performance through the recommendation of novel research
directions. Standard influential factors are given as a base for automatic
scenario modelling like prototype report generating function in InSciTe.
In addition to this, a use-case that authenticates the potential of standardized influential factors for unrefined scenario growth is also exposed.
Cho et al. [10], introduced a prescriptive analytics service prototype
to present research scholars with several points of view as well as
schemes for directing their future research. Prototype works on various
kinds of information sources on science and tech like papers, reports,
magazines, news and so on. It presents 3 analytic as well as advice
Prescriptive analytics addresses what, when and why the prediction
happens in big data analytics. The analytics is carried out in terms of
operational research that works in conjugation with the business and its
relevant domain rules. Hence, the impact and its outcome is seen immediate [29] than predictive and descriptive analytics.
Unfortunately, perspective analytics has very few application in real
world scenario. The shortfall in databases or the constraint in databases
includes the number of dimensions required in capturing the data items,
which is actually less. Hence, the analysis from such data provides, at
best, partial insights into a complex business problem. Most prescriptive
analytics exercises are therefore half-baked and hence, it needs to be used
with caution. Nevertheless, business analysts have devised “scenario
builders” based on statistical analysis of market response data which
provide elasticity measures (impacts) of different managerially
controlled parameters. Using them, they have devised “what if” simulators that help provide insights about what may be the reasonable options
that the business ought to implement in order to maintain or strengthen
its position in the market [27].
In the present study, applications of prescriptive analytics is explored
with comparative study that show how the analytics is efficient in Big
Data environment. Here, various literatures are reviewed for the purpose
Fig. 2. Prescriptive analytics features.
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International Journal of Intelligent Networks 1 (2020) 76–84
Fig. 3. Generic framework of proposed study.
preventive, proactive, and corrective maintenance strategies for all asset
classes in the electrical network. The described outcome summarizes an
overall health score and risk ranking in addition to the suggested optimal
maintenance strategy in consideration to the budgetary constraints. The
mean cost rate is minimised with mixed integer nonlinear optimization,
which is given as:
services. For a particular research scholar, the group of their role models
are given with SWOT analyses, activity trend tracking and so on. With
this, researchers can receive prescriptive description in several features
of 5W1H for reaching the levels of his/her models. Prototype creates an
advisory report with aims as well as schemes for enhancing the competences of research and development.
M Lee et al. [20], proposed descriptive and prescriptive analytics
technique on InSciTe Advisory system. The proposed analytics is based
on 5W1H Questions and Answers that is evaluated to measure the usefulness of advisory system. The systems’ user satisfaction is compared
with the current best system using multi-dimensional evaluation in terms
of information reliability and service quality. The system achieves a
better results than Elsevier Scival’s user satisfaction. S -k Song et al. [21],
proposed a similar system like [20] that provides strategic plan with four
phases. The usefulness of user satisfaction is 118.8%, which is much
lesser than [20] (126.5%). This proves that [20] has higher usefulness of
data using prescriptive analysis in big data environment.
cp ðK 1Þ þ cr þ cf
min
x:K
K R
P
xk
k¼1
xk þ
K
P
bk1 xk1
hk ðtÞdt
ð1 bk1 Þxk1
k¼1
Where, where ak and bk are adjustment factors,cf is the failure cost rate, xk
is the electrical age before kth preventive maintenance, h(t) is the failure
rate function, t is time and cr is the cost.
3.5. Clinical studies
L Celi et al. [22], survey various literatures on prescriptive analytics
on clinical studies. The author(s) described the existing techniques
available to overcome the challenges and shortfalls in clinical intelligence. Additionally, they also surveyed various literatures that identifies
the use of prescriptive analytics on clinical intelligence.
3.3. Health analytics
Raghupathi&Raghupathi [5] observed the upcoming health analytics
field as a major and unique application of health information technologies. The main goal of health analysis is the gaining of insights to make
informed medical care decisions. The author described the 4 phases of
health analytics, infrastructure, developmental method as well as instances in public healthcare.
L Celi et al. [22], survey various literatures on prescriptive analytics
on clinical studies. The author(s) described the existing techniques
available to overcome the challenges and shortfalls in clinical intelligence. Additionally, they also surveyed various literatures that identifies
the use of prescriptive analytics on clinical intelligence.
3.6. Information fusion
Shroff et al. [6], presented a unified Bayesian framework for prescriptive information synthesis that officially models the iterative fusion
of information from simulation, statistical as well as optimization
models, apart from the fusion of information from various data sources.
The author motivates the framework with different real-life applications
including providing warranty, the computational design of products or
the manufacturing process, and the best pricing or promotion of consumer goods. The author also compares the approach with reinforcement
learning, and other combinations of machine-learning, simulation and
optimization.
3.4. Electrical power grid
Goyal et al. [15], designed an advanced analytics to model asset
health and network reliability forecasting the aging of assets, identifying
remaining lifecycle, and computing network robustness. The analytics
used data from multiple systems such as enterprise asset management,
work management, geographic information systems, and supervisory
control as well as data acquisition system, advanced metering infrastructures, weather systems, as well as outage management system.
The algorithms systematically evaluated asset health and prioritized
3.7. Sales problem
Bischhoffshausen et al. [7], proposes an information system to plan
sales force assignment with the help of predictive as well as prescriptive
analyses. Information systems bring together predictive components that
are used to apply mining of historical sales information for predicting
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International Journal of Intelligent Networks 1 (2020) 76–84
informed decisions. The aim of the work was actually 2-fold: firstly, to
provide working definition, background as well as overview of
BA/Business intelligence (BI)/BD theory as well as practice. Second, was
to discuss if the above mentioned were merely passing fads or business
enablers.
Hong et al. [19], introduced a BD analytics system for automated
marketing scenario planning with respect to the BD platform software
like Hadoop or HBase. The analytics method for scenario planning has its
basis in prescriptive analysis, the most advanced method comprising of
creation of business scenarios as well as their optimization, amongst the 3
analytics of descriptive, predictive, as well as prescriptive. In addition,
the author designed a prototype of marketing scenarios planning system
as well as its GUI, along with the system infrastructure on the basis of
Hadoop eco-system based distributed parallel computing platform.
sales impact for any specific designation of sales team to customer account. Moreover, information systems integrate prescriptive components
that use linear programming models to frame optimum assignments
which increase revenues. The study presented prototype execution of the
method and developed an artefact which integrated predictive as well as
prescriptive analyses into user information systems. The revenue is
maximised using proper revenue.
max
XX
Vh;j
h2H j2J
X
qh;i;j xi;j
!
i2I
where, qh,i,j is the sales response function, Vh,j is the prospect revenue and
xi,j is the decision variables.
Kawas et al. [13], focuses on the interplay between data mining as
well as optimization parts, ensuring joint formulation in an efficient and
effective manner. The author performs sensitivity analyses of optimization components in order to yield greater insights into interactions between predictions as well as prescriptions. In the end, the author provides
an empirical research utilizing real world data collected from a high
technology company’s sales force. The outcomes demonstrated that
through usage of those analytics, the revenue could be increased by 15%.
The objective function is used to maximize the sales response function is
given by:
max
XXX
X
wst
βstk xstik þ βst0 zst
s2S i2I s t2T s
3.9. Knowledge base
Brodsky et al. [8], proposed an infrastructural design as well as
software model for the rapid growth of descriptive, diagnostic, predictive
as well as prescriptive analyses solution for dynamic production process.
The suggested infrastructure as well as model supports storing of
modular, extensible as well as re-utilizable KB of process performance
model. This method needs creation of automatic approaches which could
translate higher level models in reusable KB into lower level specialized
models needed by variety of base analysis tools as well as data manipulation, optimization, statistical learning, prediction as well as simulations. The author proposed an arrangement as well as key architecture for
re-utilizable KB which is comprised of atomic as well as composite process performance model as well as domain specific dashboard. Moreover,
with diagnostic task, author illustrates the usage of the suggested infrastructure as well as model on a composite performance model.
!
k2K t
st
st
where, xik
st is the decision variable, z is the binary decision variables, w
is the team win rate and the main aim is to identify the headcount of
seller k 2 K, hence the sales in future i 2 Isin segment s 2 Sis assigned to a
specific team, where t 2 Tsand this generates highest revenue.
Bischhoffshausen& Fromm [14] demonstrated predictive frameworks
that assess the effect of different sales team designations. For solving the
sales force assignments, the proposed predictive model uses mining of
operational enterprise data and serves as a sales response function.
Furthermore, the model can support the sales managers in assessing the
impact of different sales force allocation scenarios on future sales.
3.10. Synthetic data
Marathe et al. [11], describes the usage of synthetic data to perform
prescriptive as well as predictive analytics. The author discusses the
creation of synthetic information through combination of information
from several sources and then proves its role in a progressing disaster
resilience work which simulates the aftermath of a hypothetical nuclear
detonation in Washington DC.
3.8. Business process
Gr€
oger et al. [16], gives the data-mining-driven idea of
recommendation-based business process optimization on top of a holistic
process warehouse. It produces action based on recommendation in a
prescriptive manner and at the time of process implementation it avoids a
predicted metric deviation. The author also discusses data mining
methods as well as data structure for real time predictions as well as
recommendations creation and presents proof of concept with respect to
the prototypical implementation in manufacturing.
Hahn &Packowski [17] presents a complete overview on applications
of in-memory analytics in the domain of Supply Chain Management
(SCM) which could be used for aforementioned concepts. The contribution was three fold: First, the author developed a top-down model for
positioning in-memory analyses applications against extant IT systems in
SCMs. Secondly, the author conducted bottom-up categorizations of 41
in-memory analyses applications in SCMs for providing supportive
empirical proof of efficiency of the model. Thirdly, through contrast of
top-down as well as bottom-up points of views, it could obtain implications for research as well as industrial practices.
Bayrak [18] presented business analytics that had evolved as a
possible business enabler in both public as well as private domains and is
also the most rapidly growing field. Through the implementation of
business analytics initiative in their organization, decision makers will be
enabled to perform integration of separate data sources, prediction of
trends, improvement of performances, viewing of key performance indicators, identification of business opportunities, as well as take better
3.11. SCADA system
Rusitschka et al. [12], proposed an adaptive middleware idea which
could exploit current data processing resource through facilitating
distributed computations on enterprise as well as field levels. The author
applied the notion of linked data to draw maps for moving computations
to data required for analyses. If it has its basis in the IEC 61850 standard
semantic data model, linked data idea also provides location as well as
domain awareness which can be influenced for real time prescriptive
analyses in the domain. The other benefit of the suggested adaptive
middle-ware was abstracting computation resources. Analytic programmes may be created once and utilized for processing historical information stored on servers at the enterprise level and also on distributed
devices which created the data to allow quick analyses of events as they
are seemed to be unfurling.
3.12. Additive manufacturing
Yuan Jin et al. [40] used geometric accuracy control for improving
the analytics in additive manufacturing. Here, a prescriptive modeling is
used with out-of-plane deformation model, which aims for better prediction using limited test shapes. This method uses in-plane and
out-of-plane shape deformation over a cylinder. The deformation in
out-of-plane over a vertical cross section is given as:
80
S. Poornima, M. Pushpalatha
0
0
0
International Journal of Intelligent Networks 1 (2020) 76–84
0
0
0
Δzðφ ; r0 ðφ Þjθ0 Þ ¼ g1 ðφ ; r0 ðφ Þjθ0 Þ þ g2 ðφ ; r0 ðφ Þjθ0 Þ þ εφ0 jθ0
Table 1
Comparative analysis on prescriptive analysis.
Where, g1 is considered as the in-plane deformation in the cylinder.
From the above mentioned researches, it is seen that Prescription
Analysis efficiently utilized the planning of city services, health analytics,
information fusion, sales and marketing, recommendation systems and
other applications. Almost all methods focus on the analytics outcome,
however they do not attempt to understand the significance of data with
regard to effective target outcome. For instance in health analytics,
outcome suggests the optimal course of action however the prediction
accuracy has a significant role to play that relies on the data being analysed that might comprise outliers or noise. The entire analysis may be
enhanced if the right data is chosen for prediction as well as prescriptive
analytics phase that is a research gap, which points to the direction for
future research.
4. Various schemes used in prescriptive analysis
Application
Quantitative methods
Infrastructure
Data Provisioning
in Streaming
Networks
Research and
Development
Autonomic systems base
architecture [2]
IOT infrastructure
5W1H aspect [4]
Standardized Influential
Factors [9]
1. AS-IS function in SWOT
analysis
2. TO-BE function in 5W1H
aspect [10]
Researcher-Centric
Prescriptive
Analytics Framework [20]
Strategic Plan with four
steps [21]
Mentoring System
Automated report generation
in InSciTe Advisory system
Mentoring System
Health Analytics
This section provides the comparisons of prescriptive analysis that
uses various applications to prove its effectiveness. The applications vary
from vast sizes that depends on two quantities 1. Real world dataset and
2. Synthetic datasets [32]. The entire prescriptive analytics on vast data
of varied applications is shown in Table 1.
Electrical Power
Grid
Clinical Studies
Information Fusion
Sales Problem
5. Evaluation
In [2], the data provisioning is made easy by matching the best
possible quality by moving the application design to application results.
It attains a long term agility and availability towards the service providers. This method works as recommender system and not as an expert
system. It could be inferred that the system can exhibit better performance in an autonomic way but it might perform poor in implementing
the predictions over specified infrastructure.
InSciTe advisory system [4] improves the ability of research level using
prescriptive analytics. However, the method failed to implement the goal,
where the research activities follows a role mode researchers. The other
recommender system [10] works in the similar way as [4] to get detailed
strategies of 5W1H aspects. Here, the SWOT analysis is required to measure the internal and external research outcomes. This improves the
competiveness as a recommender system and helps the researches to
attain a better level in role model. The evaluation is done based on
obtaining 2 SCI journals for a researches. The attainment of best publication in a journal/conference is provided by this recommender system
within specific timeline. Finally the reports are generated in the form of
PDFs that possess analytical and advisory comments to improve the
competitiveness in research and development sector. The InSciTe Advisory recommender system [20] is chosen as an alternative to Elsevier
Scival with a group of 5–6 expert users. The scores of the InSciTe Advisory
system is high (75.4) than the Elsevier Scival (59.6). The satisfaction of the
user has reached 126.5% than the Elsevier Scival. The results of the
InSciTe Advisory system [20] is shown in Table 2. With four action models
[21], Measuring Research Performance, Finding Role Model Researchers,
Planning Research Activities and Evaluating and Applying Feedback in
InSciTe Advisory, the system is evaluated based on 5W1H questions. This
builds better action strategies than the Elsevier SciVal, however, the user
satisfaction of the method is lesser (118.8%) than [20].
InSciTe advisory system [9] provides possible implementation that
provides better impact on the influential factors. The use case is evaluated under four scenarios that includes critical, driving, buffering and
driven factors. The results of the InSciTe system explicitly selects the
critical factor for the generated reports. The system is checked for its
consistency analysis and out of 14 scenarios, few scenarios performed
within the range of 8–12 and other scenarios proved exceeding well. This
method exhibit well in terms of converting a raw scenario to a report
based system and acts as an expert system.
Business Process
Knowledge Base
Synthetic Data
SCADA System
Decisioning
Systems
Other Areas
Health analytics
architectural framework [5]
Wood pole risk prediction
[15]
Poppers’ scientific
epistemology [22]
Unified Bayesian
framework [6]
Comprehensive Approach
[7]
Optimization Model [13]
Recommendation-based
business process
optimization [16]
Comprehensive Framework
[17]
Hadoop-eco System [19]
Reusable Knowledge base
architecture [8]
Synthetic base
transportation network
[11]
Adaptive Middleware
Concept [12]
Automated decision making
by combining hardware and
software [25]
Project Management [30]
Knowledge management
[31]
2 2 factorial design [33]
Additive
Manufacturing
out-of-plane deformation
control [40]
InSciTe Advisory system,
ElsvierScival System
InSciTe Advisory system on
5W1H question, ElsvierScival
System
Health Dataset
Overall health score of electric
power grids
Clinical Research Data
Warranty cost estimation,
material aware manufacturing
and consumer pricing data
Sales Force Assignment
Sales Response Function
Real Time data
Supply Chain Management
Market Scenario Planning
Car Manufacturing Process
Communication, power,
health, other synthetic data
like disaster resilience.
Ad-hoc Data analytics
Statistica [28] Enterprise
Decisioning Platform
Not evaluated in any systems,
however, these schemes can
be used in prescriptive
analytics in future.
realistic job preview for more
new candidate adaptation in a
job
Out-of-Plane Deformation
Model, Cylindrical basis
function and Cookie-cutter
modeling framework.
The health analytics [5] using collected cancer datasets, AIDS,
Tuberculosis uses prescriptive analytics to improve its possible outcomes.
Hence, proactive decisions are made to find the medicine from the
pharmaceutical companies for an unknown disease and based on patient
medical conditions. However, certain problems associated with the analytics needs to be addressed and that includes: application can be made
transparent and user-friendly. The delay between the collection and
processing of data should be reduced. For large scale adoption, the system can be improved with algorithms, models and methods. Further, to
improve the analytics, the ontology can be integrated, since the entire
medical datasets are related to ontology.
To monitor the dynamical assess and health of grid in electric power
system, prescriptive analytics based wood pole risk prediction [15] is
used. The analytics provides data related to the failure rates of the grid at
present and future and calculates the risk factor. The entire analytics is
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International Journal of Intelligent Networks 1 (2020) 76–84
Table 2
Evaluation results of InSciTe and elsevier scival advisory systems.
InSciTe
SciVal
Accurateness
Completeness
Accuracy
Completeness
Navigability
Individualization
Timeliness
Sufficiency
9
6.8
9.6
7.2
9.4
7.6
9
7.4
9.2
7.4
9.4
6.8
9.8
8.6
10
7.8
Decision Guidance Analytics Language is used for decision making
and optimising the programming language. Decision Guidance Management Systems is further used to improve the process with centralized
Analytical KB (AKB). The Reusable KB in the proposed technique uses
atomic, composite and analytical views and dashboard [8].
Nuclear Detention is simulated using the combined predictive and
prescriptive model. This synthetic information is carried out with four
experimented design namely: No Communication Restoration (CR) with
0.1, Partial CR with 0.1, No CR with 0.9 and Partial CR with 0.9. This
multi policy implication with synthetic information modelling is effective
for prescriptive and policy making procedure. The high parallel
computing infrastructure improves the decision capability using the
rigorous model [11].
Adaptive Middleware Concept [12] using prescriptive analytics that
makes the better use of asynchronous data polling system on distributed
environment. The IEC 61850-conform middleware is executed on nodes
in adhoc environment. This method avoids unnecessary latency in data
transfer with improved efficiency and replicates at the end point. The
method does not provide any details of implementation to validate the
proposed technique.
Statistica Enterprise Metadata Repository [25] uses predictive and
prescriptive analytics uses user interface to build the predictive model.
The best ensemble is achieved using algorithms and methods that provides the possible outcomes. This methods uses rules to describe the
pre-scoring segmentation. The prediction model is validated and documented using model management and real time scoring option helps to
implement the model into production environment. The validation of the
method is not analysed qualitatively.
The other applications of prescriptive analytics include Project
Management [30], 2 2 factorial design [33] and Knowledge management [31] are not analysed to measure its effectiveness.
carried out over the grid, sub-station, production zones and its circuits. A
color coding scheme is used as a key performance indicator that helps the
analyst in making hop-spot analysis and check the performance of the
network. The predictions are made between the years of 2014–2017
using four analytical model with network risk assessment indictor. The
prediction accuracy of the high risk prediction model is found to be 95%
using the test dataset and provides better prioritization over planned
inspection cycle. The critical assessment improves well the prioritization
process based on risk and impact of the allocated budget.
The prescriptive analytics in clinical studies reports that improvement
in clinical randomised control trial for current gold standard test avoids
the shortcomings and challenges in big data analytics. The study proved
that predictive analytics with humans as a decision maker in big data
increases the risk of prediction. The data driven approach using machine
intelligence tool that uses prescriptive analytics could perform better for
the clinical trials with better validation [22].
The prescriptive information fusion [6] is evaluated for warranty cost
estimation using 1000 vehicles between the period of 2010–2012. Here,
the prediction model for counting the failure rate is a Bayesian network.
The best failure count predicted by the information fusion model performed well than other models, which includes failure rate, online sensor
diagnostics and warnings. The Bayesian network is evaluated to address
challenges in gear design problem. The parameters like carburization
temperature, time and potential, diffusion temperature, time and potential. Using the dataset, the results in re-simulation produced 14%
variance with error rate in acceptable level [6].
A comprehensive approach [7] addresses the gap between the decision supports by the sales rep and the solution provided to the customers.
The method uses both predictive and prescriptive analytics for information fusion that utilises the plan for sales force assessment. The linear
programming model is used as prescriptive model that increased the
revenue using optimal assignments to the sales force. The computation
time results in adverse effects, since there is a long waiting time for a
single user. Certain customers are served within seconds and other are
served for a long time. It is also inferred that, when the size of customer is
large, the computation time increases and vice versa. Similar technique
[13] uses interaction between the predictive and perspective analytics
increased the revenue by 15%. The data set contains 3041 different opportunities among 1562 unique clients; 1320 in the first segment, 600 in
the second, and 1121 in the third. Here, the optimal solution is attained
using fitted mean regression model. Even if the revenue is increased,
computation time of the sales force planning still a limitation.
The Recommendation-based business process optimization [16] uses
data mining driven approach to optimize the warehouse process with
improved decision making by the top management. The real time prediction improves the implementation over large scale in manufacturing
process. The evaluation of misclassification of datasets for each rule is
computed and it is found that the misclassification rates are reluctantly
lesser than 26%. Most of the metrics of the mining based system attains
its fullest satisfaction rates than the other methods with partial or nil
satisfaction rate. The supply chain management [17] uses in-memory
database technology for the top management to improve the better decision making. This method uses top-down and bottom-up approach to
evaluate efficiency of the framework. Monitoring and navigation of real
time datasets is well improved with integrated transaction and analytical
data models. Automatic marketing scenario [19] using Hadoop and
HBase. The results proved improved future profit rate, improved suggestion for business action to attain the sales target and numerical information to achieve the target with different business actions.
6. Conclusion and limitations
This paper provides an updated survey on various applications of
prescriptive analytics to handle effectively the big data in a controlled
manner. It is seen that most evaluations carried out is computationally
simpler, no proper analytics is carried out and that provides very inaccurate prediction results. Henceforth, the improper results leads to poor
prediction of results for a key business strategy. However, efforts are
carried out to improve the perspective analytics using optimization
methods and that provides optimal solution to a considerable extent. The
use of multi-objective functions can provide greater flexibility in attaining desirable solution to the available application. The survey on several
articles finds that the prescriptive analytics over certain application
proves valid in terms of its analytics better than predictive or descriptive
analytics. However, effective means of handling the data to attain better
results are not addressed well. Various prescriptive techniques are
adopted to improve the prediction analysis in different applications. Also,
much attention is needed further to improve well the prediction capability to enrich the gap between the analyst and the business managers.
It is seen that most evaluations carried out is computationally simpler,
no proper analytics is carried out and that provides very inaccurate
prediction results. Henceforth, the improper results leads to poor prediction of results for a key business strategy. However, efforts are carried
out to improve the perspective analytics using optimization methods and
that provides optimal solution to a considerable extent. The use of multiobjective functions can provide greater flexibility in attaining desirable
solution to the available application.
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International Journal of Intelligent Networks 1 (2020) 76–84
7. Future research issues
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The big data analysis and prediction of accurate results using predictive analytics is a new and emerging concept with various aspects for
improvement, which need further investigations. Few of which are
mentioned below:
1. Inspite of its excessive prediction capability, prescriptive analytics
adopted by the business organization is 3% with structured data. The
future research has to support the increasing demand of analysing the
unstructured data in the growing market. This could publicise the
prescriptive technology in big data environment.
2. Prescriptive analytics has proved its effectiveness in realising the
business value and predicts the results based on it. However, the efforts are required to furthermore address the gap between the production of analytical results and its application for a specific business.
3. The multi-objective optimization for achieving different goals is
required to improve the multi-tasking ability of the perspective analytics. The benefits over such procedure is attained by the use of nature inspired algorithms that finds a solution in a better optimal state.
4. Finally, it is seen that research on perspective recommender system is
more than expert system. Since, the expert system require the use of
rule, case and model based reasoning, and further it requires the use
of knowledge formulation and modelling. This can handle well the
analytics in a wider scope, however, research on such expert system is
less. The future research could concentrate on such data driven
approach to improve the analytical capability of the expert system in
big data environment.
5. The research on prescriptive analytics is in a miserable quantity that
requires better addressing with consistence performance. Also, stability issues has to be considered, which is still a desolate area of
research.
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S. Poornima, M. Pushpalatha
International Journal of Intelligent Networks 1 (2020) 76–84
S.Poornima: Received her M.ECSE degree from Anna University Trichy and currently doing Phd CSE in SRM University.
Working as Assistant Professor in the Department of Computer
Science and Engineering, SRM University. Her research interest
include Big Data Analytics and Data mining.
M.Pushpalatha: Received her PhD degree from SRM University. Currently working as Professor in the Department of
Computer Science and Engineering, SRM University. Her
research interests include Wireless Adhoc Networks, Distributed Systems and Wireless Sensor Networks.
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