Deep Learning In Business Intelligence Paper Emerging Trend Selected : Deep Learning Paper should address the following Describe the emerging trend in a
Deep Learning In Business Intelligence Paper Emerging Trend Selected : Deep Learning
Paper should address the following
Describe the emerging trend in a way that would be understandable to a nontechnical business manager.
Articulate how the author addressed the subject matter in the text
Provide at least two examples of how the trend is being applied in organizations currently.
Predict how the trend is likely to develop over the next 5 years.
Analyze how the trend may impact business organizations in the coming years, including both positive and negative impacts.
Recommend what you think interested business organizations should do in regards to this trend.
This averages 4-5 double spaced pages . The paper is to be in 12-point, Times New Roman font; be double spaced; and include a title page, table of contents, introduction, body of the paper, summary or conclusion, and references.
Papers must follow APA format. Please review and follow the APA resources in the Syllabus.
References are very important. At least five authoritative, outside references are required for EACH of the emerging trends. Anonymous authors are not acceptable. Web sources, if used, must be authored by recognized experts in the field. At least three references must be peer-reviewed. All should be listed on the last page, titled references.
Appropriate intext citations are required. I would expect to see 4-7 intext citations for each technology trend. Remember that for each reference you use, there needs to be an intext citation and for every intext citation there needs to be a reference.
Sample Paper Attached.
IEEE Paper attached Increasing Operational Efficiency with Business Intelligence and Analytics
Business Intelligence
Business Intelligence incorporates many technologies, tools, applications for analysis and best
practices are inherited to integrate, collect, analyze, and display raw data of business
organization for creating actionable and insightful business information. BI as a process of
technology-driven and as a discipline is made up of numerous linked activities, comprising
online analytical processing, data mining, reporting, and querying. BI tools comprise of businessdriven data, to provide supporting documents and reports useful for business decision making.
With BI tools, business persons may start examining the data themselves instead to wait for
Information Technology to run analytical and compound reports. This information access
benefits operators back up commercial decisions with solid numbers, rather than gut anecdotes
and feelings. BI in business aims to support executives understand their business needs to make
improvements, plan budgets, provide managers with their team performances and upgrades to
make business decisions. Organizations also use BI tools to run their budget reports for cutting
costs, modifying existing applications by upgrading to latest versions and specify incompetent
operational procedures.
BI maintains and improves working efficiency and benefits companies to increase executive
productivity. The software of Business intelligence deals with several benefits, comprising
influential data and reporting analytics abilities. Using BIs data visualization tools such as realtime dashboards, directors may generate instinctive, clear reports that enclose relevant, unlawful
data. Business Analytics is the course of discovering reports and data in order to remove
expressive insights, which may be used to high understand and increase the performance of the
business (Hung, Huang, Lin, Chen, & Tarn, 2016). BI deals as an objective management
function. Managers are capable to program data depend on goals, which can include sales
objectives, financial objectives, or productivity measures, regularly. The features of BI subsidize
to the objective of offering an awareness of present commercial practices. The software of
Business analytics is used to analyze and explore current and historical data. It exploits statistical
analysis, quantitative analysis, and data mining to recognize past commercial trends.
According to Robles-Flores and Kulkarni (2013), the rapid rise in data volume in businesses has
meant that comprehensive data gathering is barely likely through manual means. BI solutions
may help here. They offer tools with proper technologies to contribute to the integration,
collection, editing, storage, and study of existing data. Though almost only big companies were
involved in this matter a few ages ago, it has temporarily also developed necessary for start-up
businesses, and so the marketplace for BI has been increasing for years. He focuses on the
overall potentials of consuming BI in the beginning (Kulkarni & Robles-Flores, 2013). First, it
will be observed which workers of result that are appropriate for beginning and what chances
exist for realizing BI systems in the beginning. Then it will be revealed to what amount BI has
succeeded in the beginning, in which parts the methods of BI are practiced in start-ups, and what
drive BI has in the beginning. Finally, the critical success factors for the projects of BI, in the
beginning, are considered.
With growing globalization of marketplaces, aggressive competition, growing the speed with
variations in customer needs and market conditions, all market members and businesses look
new challenges. In the long run, businesses will be capable of recognize themselves, who may
adapt to these situations, who may respond quickly and be flexible to changes though at the
similar time consuming their costs under the device. For this purpose, a precise knowledge of the
present corporate and marketplace situation is crucial. To safeguard this and to offer
management with the data needed in their decision-making and planning, cultured information
and communication schemes are practiced. Like the 1960s, numerous approaches have been
industrialized for the systems, which have to develop known under numerous diverse names like
Decision Support Systems, Management Information Systems, and Executive Information
Systems. Today, the word BI has become recognized both in research and in practice. BI defines
methods like collecting, processing, storing, analyzing, and offering company data.
Practical implications of BI
BI has a direct impact on the business strategic, operational, and tactical business results. BI
confines fact-based decision making to consume historical data instead of assumptions. The tools
of BI perform business data analysis and generate reports, dashboards, summaries, graphs, maps,
and charts to offer users with complete intelligence about the business nature. BI supports on
data visualization that improves the data quality and the decision making.
Example: An owner of the hotel practices analytical applications of BI to collect statistical
information about average tenancy and room rate. It benefits to discover aggregate income
generated in each room (Wieder & Ossimitz, 2015, pp. 1163-1171). It also gathers statistics on
marketplace share and information from consumer surveys from every hotel to choose its
competitive situation in numerous markets. Through analysing these trends every year, every
month, and everyday supports management to provide discounts on hotel room rentals.
Example: A bank provides certain level of access to branch managers to multiple applications of
BI to evaluate employee performances, operational data and compare it to the other zones. It
helps the branch manager to govern who the supreme profitable consumers are and which
consumers they must work on. The usage of BI tools frees IT staff from the challenge
of producing logical reports for business departments. It also provides employees with rich
source of data with certain access levels to look over the percentage of commissions they are
earning monthly, run market share reports and look over risk profiles.
Future of BI
The key future trends of business intelligence is forecasting and development of the digital BI
world into space where platforms and tools will develop more wide-spectrum and finally, highly
collaborative (Bach, Jakli?, & Vugec, 2018, pp. 63-86). The development of BI has been
intensive on small form-factor strategies, but the emphasis will shift to actual big touch devices.
This will permit team colleagues to function towards business decisions by the side-by-side data
exploration in actual thought time. Numerous vendors are functioning toward this enlarged
integration, with application programming interfaces permitting for business data analysis in
users current systems. The BI industry has extended exponentially in current years and is
probably to endure growing. If you want to create the business data analysis in a recognized or
newly approved BI system, your team must be data-driven. Businesses must focus on how and
why they are consuming data. With these goals in mind, business leaders may design a strategy
for BI usage specialized for their team, provide them with cloud based environment to store
structured and unstructured data and create a data-driven environment. The software of BI will
become more accessible as the business grows. This development will also drive a more
informed user base. However, along with these developments, commercial leaders are essential
to take on the duty of educating their staff.
Positive and Negative impact of BI
The businesses have focused on BI for gathering important competitive data from past data and
inspecting it in graphs and dashboards. Though static data is no longer necessary for creating
informed results. In the present competitive marketplace, businesses need a view of not only the
past and today’s consequences, but also what is probable to occur in the future so they may
anticipate and strategy for change. Rather than BI, in the year 2019, the focus will be on
commercial insights, where businesses judge the performance on data-driven analytics and
measuring business analytics as per the results, and forecasting outcomes depend on past data. It
will all be around the value that data may create for its operators, instead of dashboards and
reports.
The negative impacts of BI come when user does not have a big pool of correct data from which
to compete for conclusions (Kulkarni & Robles-Flores, 2013, pp. 15-17). When this occurs,
decision-makers will often create wrong decisions as they are creating their decisions off data
that is incomplete or inaccurate. It is essential in BI to extrapolate numerous elements and factors
to go along with the data that is gathered in order to derive to a complete picture that is required
to create business decisions.
Recommendations
As per the recommendations, BI is facing new technologies ad approaches, proposing both
disruptions and opportunities for buyers and suppliers. BI consumers are now challenging
solutions that are easier to deploy, buy, use, and integrate to support mobile computing and
social or collaborative capabilities. Business Intelligence is very vital for business organizations
for distributing useful data from the great volumes of data being composed. There are numerous
BI tools accessible, but no tool is correct for each users requirement. Organizations are to
understand the emerging trend of BI to better their operational performance (Islam, 2018) and
integrate the most efficient tools by concentrating on the budget assigned to their development
team and allow them to come up with Data accuracy and compliance and be transparent to
identify and eliminate the gaps that leads to improve customer satisfaction.
IEEE COMMUNICATIONS SURVEYS & TUTORIALS , VOL. X, NO. X, XXXXX 201X
1
Deep Learning for IoT Big Data and Streaming
Analytics: A Survey
Keywords-Deep Learning, Deep Neural Network, Internet of
Things, On-device Intelligence, IoT Big Data, Fast data analytics,
Cloud-based analytics.
I. I NTRODUCTION
The vision of the Internet of Things (IoT) is to transform
traditional objects to being smart by exploiting a wide range of
advanced technologies, from embedded devices and communication technologies to Internet protocols, data analytics, and
so forth [1]. The potential economic impact of IoT is expected
to bring many business opportunities and to accelerate the
economic growth of IoT-based services. Based on McKinseys
report on the global economic impact of IoT [2], the annual
economic impact of IoT in 2025 would be in the range of $2.7
to $6.2 trillion. Healthcare constitutes the major part, about
Manuscript received September 19, 2017; revised March 30, 2018; accepted
May 23, 2018.
Mehdi Mohammadi and Ala Al-Fuqaha are with the Department of
Computer Science, Western Michigan University, Kalamazoo, MI 49008
USA (E-mail: {mehdi.mohammadi,ala.al-fuqaha}@wmich.edu.). Sameh
Sorour (E-mail: samehsorour@uidaho.edu) and Mohsen Guizani (E-mail:
mguizani@ieee.org) are with the Department of Electrical and Computer
Engineering, University of Idaho, Moscow, ID 83844 USA.
Deep Learning
Models for IoT Big
Data Analytics
IoT Cloud
Edge Devices/
Fog Computing
IoT Devices
–
Soft Real-time
Analytics
Hard Real-time
Analytics
Deep Learning for
Streaming and
Fast Data Analytics
AbstractIn the era of the Internet of Things (IoT), an
enormous amount of sensing devices collect and/or generate
various sensory data over time for a wide range of fields
and applications. Based on the nature of the application, these
devices will result in big or fast/real-time data streams. Applying
analytics over such data streams to discover new information,
predict future insights, and make control decisions is a crucial
process that makes IoT a worthy paradigm for businesses and a
quality-of-life improving technology. In this paper, we provide a
thorough overview on using a class of advanced machine learning
techniques, namely Deep Learning (DL), to facilitate the analytics
and learning in the IoT domain. We start by articulating IoT
data characteristics and identifying two major treatments for
IoT data from a machine learning perspective, namely IoT big
data analytics and IoT streaming data analytics. We also discuss
why DL is a promising approach to achieve the desired analytics
in these types of data and applications. The potential of using
emerging DL techniques for IoT data analytics are then discussed,
and its promises and challenges are introduced. We present a
comprehensive background on different DL architectures and
algorithms. We also analyze and summarize major reported
research attempts that leveraged DL in the IoT domain. The
smart IoT devices that have incorporated DL in their intelligence
background are also discussed. DL implementation approaches
on the fog and cloud centers in support of IoT applications are
also surveyed. Finally, we shed light on some challenges and
potential directions for future research. At the end of each section,
we highlight the lessons learned based on our experiments and
review of the recent literature.
Data flow
arXiv:1712.04301v2 [cs.NI] 5 Jun 2018
Mehdi Mohammadi, Graduate Student Member, IEEE, Ala Al-Fuqaha, Senior Member, IEEE,
Sameh Sorour, Senior Member, IEEE, Mohsen Guizani, Fellow, IEEE
Fig. 1. IoT data generation at different levels and deep learning models to
address their knowledge abstraction.
41% of this market, followed by industry and energy with 33%
and 7% of the IoT market, respectively. Other domains such
as transportation, agriculture, urban infrastructure, security,
and retail have about 15% of the IoT market totally. These
expectations imply the tremendous and steep growth of the IoT
services, their generated data and consequently their related
market in the years ahead.
Indeed, machine learning (ML) will have effects on jobs
and the workforce, since parts of many jobs may be suitable
for ML applications [3]. This will lead to increase in demand
for some ML products and the derived demand for the tasks,
platforms, and experts needed to produce such products. The
economic impact of machine learning in McKinseys report [2]
is defined under knowledge work automation; the use of computers to perform tasks that rely on complex analyses, subtle
judgments, and creative problem solving. The report mentions
that advances in ML techniques, such as deep learning and
neural networks, are the main enablers of knowledge work
automation. Natural user interfaces, such as speech and gesture
recognition are other enablers that are highly benefiting from
ML technologies. The estimated potential economic impact of
knowledge work automation could reach $5.2 trillion to $6.7
trillion per year by 2025. Figure shows the break down of this
estimate in different occupations. Compared to the economic
impact of IoT, this estimation asserts the more attention
toward the extraction of value out of data and the potential
impacts of ML on the economic situation of individuals and
societies. These economic impacts have serious consequences
on individuals and countries, since people need to adapt to
new means of earning income suitable for them to maintain
IEEE COMMUNICATIONS SURVEYS & TUTORIALS , VOL. X, NO. X, XXXXX 201X
2
Economic impact of knwoledge automation for different occupations by 2025
1.4
1.2
(trillion dollar)
1
0.8
sensor devices are attached to a specific location, and thus
have a location and time-stamp for each of the data items.
High noise data: Due to tiny pieces of data in IoT
applications, many of such data may be subject to errors
and noise during acquisition and transmission.
0.6
0.4
0.2
0
Clerical
Customer Education Health care Science and
service and
engineering
sales
Low Range
IT
Managers
Finance
Legal
High Range
Fig. 2. The break down of estimated economic impact of $5.2 trillion to
$6.7 trillion per year for machine learning in 2025.
their desired living standard.
In recent years, many IoT applications arose in different
vertical domains, i.e., health, transportation, smart home, smart
city, agriculture, education, etc. The main element of most
of these applications is an intelligent learning mechanism for
prediction (i.e., regression, classification, and clustering), data
mining and pattern recognition or data analytics in general.
Among the many machine learning approaches, Deep Learning
(DL) has been actively utilized in many IoT applications in
recent years. These two technologies (i.e., DL and IoT) are
among the top three strategic technology trends for 2017 that
were announced at Gartner Symposium/ITxpo 2016 [4]. The
cause of this intensive publicity for DL refers to the fact that
traditional machine learning approaches do not address the
emerging analytic needs of IoT systems. Instead, IoT systems
need different modern data analytic approaches and artificial
intelligence (AI) methods according to the hierarchy of IoT
data generation and management as illustrated in Figure 1.
The growing interest in the Internet of Things (IoT) and
its derivative big data need stakeholders to clearly understand
their definition, building blocks, potentials and challenges. IoT
and big data have a two way relationship. On one hand, IoT
is a main producer of big data, and on the other hand, it is an
important target for big data analytics to improve the processes
and services of IoT [5]. Moreover, IoT big data analytics
have proven to bring value to the society. For example, it is
reported that, by detecting damaged pipes and fixing them, the
Department of Park Management in Miami has saved about
one million USD on their water bills [6].
IoT data are different than the general big data. To better
understand the requirements for IoT data analytics, we need to
explore the properties of IoT data and how they are different
from those of general big data. IoT data exhibits the following
characteristics [6]:
Large-Scale Streaming Data: A myriad of data capturing
devices are distributed and deployed for IoT applications,
and generate streams of data continuously. This leads to
a huge volume of continuous data.
Heterogeneity: Various IoT data acquisition devices
gather different information resulting in data heterogeneity.
Time and space correlation: In most of IoT applications,
Although obtaining hidden knowledge and information out
of big data is promising to enhance the quality of our lives, it
is not an easy and straightforward task. For such a complex
and challenging task that goes beyond the capabilities of the
traditional inference and learning approaches, new technologies, algorithms, and infrastructures are needed [7]. Luckily,
the recent progresses in both fast computing and advanced
machine learning techniques are opening the doors for big
data analytics and knowledge extraction that is suitable for
IoT applications.
Beyond the big data analytics, IoT data calls for another new
class of analytics, namely fast and streaming data analytics, to
support applications with high-speed data streams and requiring time-sensitive (i.e., real-time or near real-time) actions.
Indeed, applications such as autonomous driving, fire prediction, driver/elderly posture (and thus consciousness and/or
health condition) recognition demands for fast processing of
incoming data and quick actions to achieve their target. Several
researchers have proposed approaches and frameworks for
fast streaming data analytics that leverage the capabilities
of cloud infrastructures and services [8], [9]. However, for
the aforementioned IoT applications among others, we need
fast analytics in smaller scale platforms (i.e., at the system
edge) or even on the IoT devices themselves. For exampl…
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