Methods Towards Create A Business Intelligence Webinternet Website In Sharepoint 2013

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Methods Towards Create A Business Intelligence Webinternet Website In Sharepoint 2013

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Received: 3 October 2020 / Revised: 31 October 2020 / Accepted: 6 November 2020 / Published: 12 November 2020

Collaborative data integration in a data-driven business intelligence (BI) system brings an opportunity to foster the decision-making process to improve tourism competitiveness. This article presents

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Follows a classic BI architecture and provides functionality for data transformation, data processing, data analysis and data visualization. At the core of data processing,

Twitter offers mechanisms to identify tourists, assign tweets from TripAdvisor and Airbnb to attractions and accommodation sites, analyze the sentiment of comments issued by tourists, and all of this using Openstreetmap’s geolocation objects. With all these ingredients,

Enables data analysis and visualization to answer questions such as where tourists hang out the most, average length of stay, or the visitor profile of a particular destination.

Social and collaborative data have become an important source of information and knowledge in several domains, including political elections, sentiment recognition, disaster management, smart cities, and disease outbreaks [1, 2, 3]. Much of this importance is due to significant change on the web. Internet users have moved from being consumers to information creators, a phenomenon called Web 2.0, which allows online users to participate in social communities (co)-creating and distributing Web content [4, 5, 6]. More and more web users are participating in such content sharing and online social activities.

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For example, users are increasingly paying attention to opinions posted on the web before making a decision about an online purchase. Users confirm that they feel more confident when checking reviews on the website before visiting a hotel, restaurant or tourist attraction. In particular, content created by tourists is considered highly reliable, credible, relevant, up-to-date and attractive [7, 8, 9].

In this paper, we focus on how to use collaboratively created data in social networks to analyze the tourism sector, an industry that greatly affects the economic performance and quality of life of countries. Tourist destinations continuously strive to improve their competitive position in the international or national tourism market and attract the largest number of tourists according to their capabilities [10, 11, 12, 13, 14]. Taking 2019 as a reference, this economic sector grew by 3.5% more than the global economy, which grew by 2.5%, generating 330 million jobs (1 in 10) and representing 10.3% of global GDP. An important aspect of improving competitiveness is understanding the sector through data analysis.

As a consequence of the increase in available data, more and more information about tourists and attractions is being stored. Thanks to advances in data processing performance and machine learning maturity, we can process all of this available data to improve travel competitiveness.

Spatial data also represents a valuable source of information as places, places, roads, attractions, etc. can be located geographically. This information is very useful to know where tourists visit, how much time tourists spend at attractions.

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In this work, we use collaborative and spatial data sources that provide valuable knowledge for the analysis of the tourism sector. Specifically, we used four data sources

. Using business intelligence as the information and technical support provided by these data sources, a platform was created that was responsible for the entire process of extracting and integrating data from these sources. This includes extracting and integrating data in a consistent format, organizing and structuring data to be used in analytical tasks, and visualizing analytical results. called the constructed platform

Business Intelligence (BI) emerges as the concept of extracting and analyzing business data for better decision making, and BI is a good example that forms the basis of the current explosion and leveraging of data proliferation. According to Gartner analysts, BI is “an umbrella term that includes applications, infrastructure and tools, and best practices that can access and analyze information to improve and optimize decisions and performance” [15]. BI incorporates a wide range of technologies such as data warehousing, online analytical processing (OLAP), data mining, benchmarking, text mining, and predictive analytics [16]. A key success factor in BI is, among other aspects, the ability to manage internal and external sources of structured and unstructured data. BI architecture is rapidly expanding as a solution for tourism management and development [17].

Integrating collaborative data within a BI platform represents an attractive approach to tourism information analysis to discover activities tourists perform at a destination, opinions about a particular destination, tourist attractions or peak tourist seasons by nationality. Among many other questions.

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Existing tourism BI platforms in the literature attempt to integrate data sources for better tourism understanding. BI platforms in tourism are commonly used to:

Enables to interactively define a destination to be analyzed, load data from different sources such as spatial or topographical data, run routines to associate ideas to places, identify users who are travelers as well as visualize the data on a single platform.

Dynamic tables and graphics were created to be able to handle the results of all the operations performed on the platform. In this way, travel trends can be analyzed to focus on marketing campaigns, shortening the response time to incidents. In short, another way to reach and understand tourists.

The paper is structured as follows; Section 4 presents an overview of the platform functionality. Then, in Section 5 we present how the various components are organized. Next, Section 6 highlights some key aspects of data processing, such as tweet segmentation. Finally, Section 7 illustrates how all the data included in the platform can be exploited.

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The use of business intelligence solutions and collaborative data sources has increased over the decade, both in isolation and together. In both academic and scientific literature, several benefits of using BI have been identified, including optimizing operational activities, improving relationships with customers and suppliers, reducing data redundancy, and facilitating new types of questions by a segment of end users. , creating higher profitability, better decision support and competitive advantage [21, 22, 23].

One of the areas where BI is most used is the health sector, which includes data warehouses, OLAP systems, and dashboards for monitoring health policies [ 24 , 25 , 26 ]. spatial databases that seek to take advantage of patient information to facilitate a more effective approach to epidemiological treatment [ 27 , 28 , 29 ]; and using data mining techniques to create a health profile of patients and communities to facilitate treatment [ 26 , 29 , 30 ].

In addition, for some authors, business intelligence is one of the components of decision support systems (DSS) [40, 41] and there are several examples in the literature of DSS that try to integrate different sources to facilitate the decision-making process. For example, the Tourism Management Information System (TMIS) (

) [42] is a DSS financially supported by the Austrian National Tourist Office and the European Tourism Commission and developed according to the specific needs of tourism managers.

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Provides a unified view of multiple data sources that can be visualized and analyzed through a graphical interface.

It includes official data from Eurostat and the Federal Statistical Office, as well as local and national tourism data provided by relevant tourism organizations, which provide trends in occupancy rates, visitor numbers, hot destinations, etc.

It allows visualization and analysis of statistical indicators from different data sources and different domains (tourism, economics, environment) [18].

Semantic technologies and idea mining techniques are utilized to process the collected data and extract actionable knowledge from the repositories. In addition, it displays statistics

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As linked data (LD), it allows tourism professionals to link to other index sources and explore linked data archives.

Difficulties have been experienced in performing data integration as many open data are offered

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