Abstract visitors intrigued by coming, and advantages travelers themselves


Tourists are essential money related wage for a nation,
obviously as we are in 21st century, bending tourism with technology (particularly
Internet of things IoT)) would profit the nation by making more visitors
intrigued by coming, and advantages travelers themselves by giving valuable
data to them in the meantime. In this work we will make profit of utilizing IoT
in tourism segment and will concentrates on the management and analysis of huge
information utilizing data mining strategies to separate imperative data from
the enormous information produced by the utilization of Internet of things in
smart tourism framework. By proposing upgrade algorithm for data
classification, the proposed arrangement is shaped to deliver a classifier that
can manages diverse sorts of information with acknowledged many-sided quality
as opposed to utilizing distinctive classifiers for numeric information
composes. Besides, it presents assessment technique, in which the majority of
classifier can be incorporated with high proficiency. The android based mobile
application that uses data mining concepts is proposed to access useful
information in real time. The proposed real time system will be tested and the
obtained results will be evaluated using appropriate evaluation methods.

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cloud computing, clustering, data management, data mining, Global Positioning
System, internet of things, machine learning, Smart tourism.


There are many aspects, including the
establishment of a smart Tourism Data Management System to manage data and tourism
services. Data Management globally encompass all the controls identified with
overseeing information as a profitable asset it is a deliberate procedure of catching,
conveying, working, ensuring, improving, and cleaning of data cost-effectively,
which needs the consistently going support of plans, approaches, projects and
practices. Internet of Things (IOT) defined as The Internet of Things is a
network of networks where, typically, a massive number of
objects/things/sensors/devices are connected through communications and
information infrastructure to provide value-added services. The term was first
coined in 1998 and later defined as “The Internet of Things allows people and
things to be connected anytime anyplace with anything and anyone, ideally using
any path/ network and any service” 1. The communication to each other with
minimal human intervention each object has unique identification and
self-directed data transfer capability over the network. The Aim of using IOT
in tourism system is to make the system, faster automated and more comfortable
for the user but also many activists have had access to excessive data and
Millions of data records are produced daily in this industry.

Statement of Problem and Motivation

IoT Data Management is a Big Data
problem, Big data is massive amounts of data Commonly described as the 4V’s (volume,
variety, velocity and veracity) .so to deal with the massive data generated
form IOT, data mining classification techniques can be apply intelligent
methods to extract data patterns .Data Mining is the computing process of
discovering patterns in large data sets involving methods at the
intersection of machine learning, statistics, and database systems
2. Using (Internet of things IoT)
the system can merge into well-known elements of the surrounding infrastructure
that provides multiple new services and benefits to the users, but it’s
difficult to obtain real time and valid information of the huge online tourism
data. This process creates complex and unstructured data sets ranging from data
related to daily transactions to data about customers ‘preferences. On the
other hand level of smart tourism in Iraq it is still in the beginning steps of
organizational and practical progresses. There are many aspects, including the establishment of a smart Tourism with
IOT Data Management System to be a smart real time system to
manage the information and tourism services

Aims of Research

This work is selected to demonstrate
the vital role of tourism industry for the future of humanity, specifically
their relevance towards achieving the Sustainable tourism growth goals. Smart
tourism system will be proposed to address the problem of insufficient tourism
information by guiding tourists to central website for tourism activities and
services. A classifier that can deals with the diversity, scattered tourism
data will Proposed with minimum complexity using data mining classification
techniques with accepted complexity instead of using different multi classifiers
for numeric data types. Moreover, it introduces evaluation method, in which
most of classifier can be included with high efficiency. Using (Internet of
things IoT), the system can merge into well-known elements of the surrounding
infrastructure that provides multiple new services and benefits to the tourists.
The system has to facilitate access to authenticated information and provide it
to tourists. Cartographic services will provided on the Internet and enable the
tourist to use maps for the respective areas using GPS location from mobile
Data management tourism system that controls and analyzes the information can
significantly ease managing and maintaining tourism resources then employ it to
serve the tourist or the respective authorities, The system also will provide
guidance to the tourists (Whether the tourist inside or intends to visit the
Tourist area).

This is beneficial for active
industries in tourism and is led to many chances to improve processes in this
industry in Iraq, respond better to customers’ needs, focus on tourism
requirements, improve the products and services, and predict areas that
interest tourists.


In this survey many papers were
explored to give a comprehensive overview of previous works related to the
subject of data management using IOT specifically that related to data mining
and machine learning techniques that used for big data management .Some of
these research related to the use of Internet of things technology in the field
of smart tourism or smart city applications to put forward proposed smart
tourism system based on IoT technology with appropriate enhancement of
classification algorithm and other services.

 Muhammad Rizwan Bashir, .et al. in (2016)3 presents
IoT Big Data Analytics system for the capacity and investigation of continuous
information produced from IoT sensors conveyed inside the smart building. The
appropriateness of the system is shown with the assistance of a situation
including the examination of continuous smart building information for
naturally dealing with the oxygen level, glow and smoke/dangerous gases in
various parts of the brilliant building. The underlying outcomes demonstrate
that the proposed system is fit for the reason and appears to be helpful for IoT-empowered
Big Data Analytics for smart structures. The key commitment of this paper is
the complex incorporation of Big Data Analytics and IoT for tending to the
extensive volume and speed test of continuous information in the smart building

Oscar GCABA ,.et al. (2016)4 This
work implemented IoT for the South African tourism industry to improve the
effectiveness of the business, and effect on the South African economy. This
paper answers the topic of what IoT advancements can improve the tourism
business. The potential applications which are of business advantage to IoT in
tourism traverse the zones of natural life observing and tracking, monitoring
oceans and waters, plant species, promoting tourism, friendliness and
enterprise tourism. the way things are, reports what is in the market and not
the future innovations. The system  require to additionally create and pull these IoT
toward the South African Surroundings, and to deal more with the IoT in tourism

Bishnu Prasasd Gautam,.et al. (2016)5
This research effort to upgraded e tourism and offers arrangement philosophies
by presenting IoT based applications. The work effort for the improvement of
local tourism to revive the regional enhancement. This technique endeavors to
join the Internet of Things (IoT) innovation with the improvement of the
tourism industry and keen tourism urban communities. The target of this
research is to upgrade the nature of tourism benefits by tending to the
broadened needs of guests in the topographically tested regions and contribute
to the economy of those areas by presenting new strategies

Shapoval V., Wang., C. Hara T., & Shioya, H. (2017) 6This
work Using Data Mining to Analysis Tourism Data for Inbound Visitors to Japan .The
study consequences of around four thousand perceptions demonstrate the primary
inspiration for guests’ future return isn’t driven by encounters had amid their
most current visit but instead by foreseen encounters later on. Data mining strategy
is helpful for valuable disclosures of specific examples with expansive guest
informational collections, furnishing governments and goal advertising
associations with extra devices to better plan viable goal promoting
methodologies. This examination has a few basic restrictions, prompt more
reasonable upcoming works in the field of quantitative goal advertising. The
research depends on the information of guests who came to Japan, speaking to a
little division of the considerable number of explorers who chose not to visit
Japan and to whom the researches can’t extrapolate their discoveries. Second,
the exploration information was gathered at one time of the investigation year
of 2010, after which Japan saw a colossal drop in guests because of Great East
Japan Earthquake. The absence of direct information accumulation involvement
with the dataset kept them from having certain bits of knowledge which might be
helpful in assessing the dataset

Mervat Abu-Elkheir,.et al. (2013) 7
In this paper, the specialists examined a portion of the data management  arrangements proposed for the Internet of
Things, with an attention on the required outline components that ought to be
tended to so as to give a thorough arrangement. the proposed configuration
cover the three principle elements of taking care of information; how it is
gathered, how it is put away, and how it is handled. The arrangements are just
incomplete as in the address information administration prerequisites of IoT
subsystems, for example, WSNs, and incorporate fractional subsets of the
coveted plan natives. To make up for this limitation , the segments of an
exhaustive IoT information administration system were illustrated with center
information and sources layers and bolster for unified design. The structure
concentrates the requirement for two-way, cross-layered plan approach that can
address real time and chronicled queries, investigation, and administration requirements.

Yue Xu (2015)8 This paper initially
presents IoT and machine learning Challenges in managing IoT data .IoT tries to
interface objects or things to one network, and join those date created to some
sort of knowledge. Subsequently, the insight is finished up and produced
naturally by machines. However, there are stills many limits and difficulties
to overcome. In the paper the researchers touched on a number of applications
in machine learning application in IoT. From both research and industry, these applications
are fundamental and extraordinarily esteemed. The research presents some ML
algorithms to manage the information (Bayesian Statistics, k-Nearest Neighbors
(k-NN), Neural Network, Support Vector Machines (SVM), Decision Tree(DT),pca,
k-Means,and Reinforcement Learning)

Leonardo L. A. Heitzmann (2016)9 In
this work, the construction for anticipating the status of a given gadgets in a
smart surroundings were exhibited and designed using classification algorithms.
The proposed design was assessed against numerous preprocessing dataset systems.
Seven classifiers were computed using the AUC (Area
Under the Curve) metric.
As indicated by the acquired outcomes, considering time highlights amid the
dataset preprocessing stage has prompted an expansion in prediction performance
uncovered by more prominent AUC values . Also, the way toward linking no less
than one preview expanded the execution in correlation with no connection.
Indeed, even not having the capacity to reach exact determinations from the
utilization of resampling procedures, the researcher was used  down sampling to minimize the size of  training dataset , which thus could request
less computational assets (e.g. memory utilization, handling time). The work
demonstrated that there is no single classifier that is most reasonable for
every single given devices. In light of this actuality, a classifier decision
technique was proposed with the exhibited decision strategy, they acquired an
expansion in forecast execution when contrasted and related work. A measurable
examination was performed of a few classifiers utilizing both Friedman factual
test and Nemenyi post-hoc test.

Muhammad Nasir Khan, et.al. (2014)10
In this paper a strategy for classifying data stream was proposed. Simple
Aggregation and Approximation (SAX) was utilized for reducing data dimensions clustering
were applied to find the number of classes and allot labels to the data depend
on the extracted classes. classification algorithm was  proposed 
for the data streams, customary classification calculation are of little
significant in data streams due  to the
intricate nature, unbounded memory necessities and idea floating issue in
information streams. The proposed strategy adopts a novel strategy towards the
classification of the information streams through applying unsupervised
classification strategies for example. The high volume information is tested
also, lessened with Simple (SAX) Density based clustering algorithm DB Scan is
connected on the information stream to uncover the quantity of classes
introduce and thusly mark the information. Support vector Machine (SVM) was
applied to classify the label data. the 
proposed technique was tested on the Intel Lab Data set, an
informational index of four ecological factors (Temperature, Humidity, light)
gathered through S4 Mica2Dot sensors more than 36 Days at every below average.
The calculation is assessed on distinctive test size and normal exactness of
80% is acquired

Li (2017)11 check the viability of the enhanced BP(back propagation)
calculation, in this paper data classification system were executed  for the Internet of Things. Through the
framework, a lot of classification tests were done. The outcomes demonstrate
that the enhanced BP calculation can quicken the union speed of the system in a
specific training period. The BP neural system is utilized to group the
informational collection in the Internet of Things. The achievement rate of
grouping is as high as 98% that can meet the necessity of general data
classification. By investigating the blunder surface of a solitary neuron, it
is discovered that the system isn’t caught in the nearby least. Clearly, the
enhanced BP calculation can enhance the execution of BP neural system to a
specific extent. The presentation and improvement of the Internet of things has
conveyed awesome chances and difficulties to the current Internet framework. In
spite of the fact that this paper develops the data classification   under the Internet of Things framework,
there are still a few issues that need additionally think about. The Internet
of things is excessively expansive. Particularly in managing a lot of
high-dimensional information, the current BP arrange calculations should be
demonstrated , In the functional application, it is discovered that the
conventional BP neural system has the issues of slow convergence and easy fall
into the local minimum. Clearly, these issues will limit the use of BP neural
systems in the Internet of things with a lot of data. The framework is utilized
to order the information gathered by the coal mine security observing
framework, and the network convergence performance is compared before and after
the enhanced algorithm


Due to the diversity
of data sources related to tourism, it is essential to make maximum use of this
information, by collecting , processing 
and classifying  raw input data to
improve the quality of services and its ease of management, in this work we
will explore the most appropriate classification algorithms that can be
enhanced to deal with huge and different data types in the sector of tourism to
extract and archiving knowledge that can be useful to serve tourist or  the respective authorities that using web or
mobile application.

Data Management for Modern Tourism System

With the huge developments in the new
world in terms of technology and systems, the tourism fields starting from
booking process till return home. As mention earlier in
the presented review of previous work, the current tourism systems suffers from
different issues. The most important issues are appeared in the using of data
mining methods. This can include the complexity data classifiers and data
accessing in addition to clustering. The other issue is the performance
evolution of the adopted classifiers. The proposed solution is formed to
produce a classifier that can deals with different types of data with accepted
complexity instead of using different classifiers for numeric data types.
Moreover, it introduces evaluation method, in which most of classifier can be
included with high efficiency. The proposed solution is also introduced to
tackle the problem of database management, in which the accessing time and
complexity are reduced. For solving the problem of data clustering, the
proposed idea can adopt the modification of current algorithms to come up with a
new schematic that can overcome the mentioned issues.

At the other hand, the proposed
system include web and mobile based applications to enable the tourist from
identifying the underlying area. In addition to obtain the required information
regarding the accommodation (hotel and motels), restaurants, historical places
and locations, available technologies, the weather, Geographic Position System
(GPS) and so on. This needs to allocate different types of sensors and data
sources under the Internet of Things (IoT) techniques and methodology.
Therefore, the proposed system can be represented as a guide for tourist
regardless the requirements.

The android based
mobile application that uses data mining concepts is proposed to access useful
information in real time. It will depend on tourist country origin and current
GPS location to provide notification feedback system that suggests going to specific
locations (where those locations are near tourist and tourists from same origin
country are interested in).



There are many aspects to be done for
smart tourism, including the establishment of a Tourism Data Management System
to be a central system to manage the information and tourism services using IoT  technology .IoT Data Management is a Big Data
problem so dealing with massive amounts of data, both structured and unstructured.
In this research a smart tourism system will proposed and demonstrate on proposing
a data classification technique that can deal with many types of data in the
introduced system. using stabile platform  to built  a Nosql database and use ml libraries for the
proposed system ,Designing and implementing the website  and mobile application with related pages of
the proposed system, Connecting the processed parts of the system ,Testing the
proposed system for big data and the obtained results will be Evaluated using appropriate
 evaluation method.