2.4 THE NEEDS OF DATA WAREHOUSE CONCEPT IN TOURISM
Nigeria is
known of its tourism endowment, but the maximization of this potential
are minimal, that is why one of the focus of the Nigeria government is
to revamp, develop the tourism sector. Strategies are to be employed to
ensure that the aim of developing the sector is achieved. Moreover, this
will be based on critical analysis on data which will eventually
provide substantial information about and giving deep knowledge into the
existing data. The provision of data for analytical and concise
decision making by the managers in the tourism sector is what the
tourism database management system using the data warehouse approach is
all about (Darubianu et al., 2009).
In respect to William H. Inmon,
explains Data Warehouse as a subject-concerned with, unified, time
variation, non-unpredictable accumulation of information in backing of
the administration's basic leadership process (Inmon, 2005).
2.4.1 CHARACTERISTICS OF DATA WAREHOUSE
2.4.1.1 SUBJECT ORIENTED WITH: Data warehouse is created to help in analyzing data.
For
instance, to take in more about your organization's business
information, data warehouse can be created that focuses on products.
Utilizing data warehouse, some disturbing questions like what product
was best sold last year, who is likely to be highest buyer or customer?
The data warehouse to have the capability to group subject matter,
products as the case may be, marks data warehouse subject-concerned with
(Docs.oracle.com, 2016).
2.4.1.2 UNIFIED: Unification,
Reconciliation or integration is firmly identified with subject-
concerned with. Data warehouse must put information from unique sources
into a predictable
configuration. They should resolve such issues as naming clashes and irregularities among units of measure. When they accomplish this, they are said to be unified or integrated (Docs.oracle.com, 2016).
2.4.1.3 TIME VARIATION: One of the characteristics of data warehouse is that it center's
emphasis
on change that occurs after some time is what is implied by the term
time variation. Keeping in mind the end goal to find slants and
recognize shrouded examples and connections in business, analysts
require a lot of information. This is particularly as opposed to online
transaction processing (OLTP) frameworks, where execution necessities
request that verifiable information be moved to a file (Docs.oracle.com,
2016).
2.4.1.4 NONVOLATILE: It is implies that once data is
loaded to the data warehouse the information ought not to change. This
is coherent on the grounds that the reason for a data warehouse is to enable the user to analyze what has happened (Docs.oracle.com, 2016).
2.5 CLOUD COMPUTING
Cloud computing can be characterized as a model for empowering universal, helpful what's
more,
on-interest system access to a mutual pool of configurable figuring
assets that can be quickly provisioned and discharged with negligible
administration exertion from the client side and insignificant
administration supplier connection (Ahmed, 2013).
Within just a
cloud-based computing system, the materials, the information are
basically on another person’s domain, control or setup and utilized
remotely by the cloud clients. The handling out of information is done
remotely suggesting that the information and other different components
from the user need to be transmitted to the cloud system for processing,
nevertheless the outcome of data processed will be determined by
completing required processing. On this note, it is obvious that a
person could store information on cloud storage. This gives rise to some
consideration using the cloud system:
â–ª The transmission of individual sensitive information to the cloud server,
â–ª The transmission of information from the cloud server to clients' PCs and
▪ The storing of user’s peculiar information in cloud system which are remote server in which the client is not the owner.
The
above three conditions stated of cloud computing are seriously susceptible to security break. Cloud computing accompanies various
potential outcomes and difficulties at the same time. With the difficulties, security is thought to be a basic obstruction for cloud computing in its path to success. The place were data are stored is an essential factor in cloud computing security. This prompted the research into what concept will be more appropriate in developing the proposed system (Monjur and Mohammad, 2014).
2.6 BIG DATA COMPUTING
Big data computing could be described as a developing information science model of multi-dimensional
data mining for scientific finding and business analysis over huge
scale framework. The information gathered/made from various scientific
examinations and business dealings regularly requires tools to encourage
proficient information management, analysis, validation, virtualization
and dissemination while protecting the basic value of the information
(Kune et al., 2015).
Regular data warehousing systems depend on
pre-determined analytics on data extracted, and adopts cleansing and
transforming into another database known as data marts- which are
periodically updated with the similar form of rolled-up data. In any
case, Big Data frameworks work on non-established or predetermined
analytics; therefore, there is no need of data cleansing and
transformations types of procedures (Kune et al., 2015).
Big data
coordinates and excerpt the most valuable information from the rapidly
growing Substantial volumes, variety forms, and regularly changing
related set of information gathered from various, separate sources in
the least conceivable time, utilizing numerous statistical and machine
learning techniques. 5V’s such as Volume, Velocity, Variety, Veracity,
and Value are associated with Big Data. Big Data and conventional data
warehousing frameworks, in any case, have the comparable objectives to
convey business value through the analysis of information, but vary in
the way of analyzing data and the coordination of data. Practically,
data warehouses coordinate data in the repository, by gathering it from
other databases like enterprise's financial systems, customer marketing
systems, billing systems, point-of-sale systems and so on (Kune et al.,
2015).
In conventional data warehouse information is separated into
operational and historical data which are mostly structured. Does apply
ETL (Extract, Transformation and Load) system for processing. As the
information volumes are expanded, the chronological information is
filtered from data warehouse system for speedier database queries (Kune
et al., 2015).
2.7 DATA WAREHOUSE IN EGYPTIAN TOURISM
The
Egypt Tourism sector considers Data Warehousing in their operations
amidst other things. It is observed that over the globe there has being
serious competitiveness in so many economic sector of any country. As
for the tourism sector in Egypt it will be considered as of a business
viewpoint , keeping in mind the end goal to survive and succeed in
today's profoundly competitive worldwide environment, the following will
be well-thought-out:
• The need to make decision quickly and accurately, utilizing every single accessible data.
• Users are tourism business environment specialists, not PC experts.
•
The quantity of data that accumulates over a short period, which
influences response time and the sheer capacity to fathom its content.
• Competition is warming up in the ranges of business intelligence and included information value.
Hence
those responsible in making decision in the tourism business need more
logical data to catch the entire image of their tourism settings, and it
is precisely the part of data warehouse to give them this worldwide
perspective and wide capacity for analysis (Hendawi and El-Shishiny,
2016).
Hendawi and El-Shishiny (2016) claimed that starting with the
data source analysis, it was made possible in building the Egyptian
Tourism Data Warehouse. The current set of information sources are found
at the Egyptian Ministry of Tourism (MoT) in various organizations and
heterogeneous structures.
