scholarly journals A Scalable Business Intelligence Decision-Making System in the Era of Big Data

2019 ◽  
Vol 8 (12) ◽  
pp. 2034-2042
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Data Storage ◽  
Business Intelligence ◽  
Unstructured Data ◽  
Data Sources ◽  
Second Step ◽  
Decision Making System ◽  
Set Up

Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

Storage Infrastructure for Big Data and Cloud

2014 ◽  
pp. 110-128 ◽  
Author(s):  
Anupama C. Raman
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Storage ◽  
Unstructured Data ◽  
Storage Area Networks ◽  
Storage Technology ◽  
Object Based ◽  
Storage Area ◽  
Storage Technologies ◽  
Big Data Storage

Unstructured data is growing exponentially. Present day storage infrastructures like Storage Area Networks and Network Attached Storage are not very suitable for storing huge volumes of unstructured data. This has led to the development of new types of storage technologies like object-based storage. Huge amounts of both structured and unstructured data that needs to be made available in real time for analytical insights is referred to as Big Data. On account of the distinct nature of big data, the storage infrastructures for storing big data should possess some specific features. In this chapter, the authors examine the various storage technology options that are available nowadays and their suitability for storing big data. This chapter also provides a bird's eye view of cloud storage technology, which is used widely for big data storage.

The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects

2015 ◽  
pp. 94-120
Author(s):  
Jonathan Bishop
Keyword(s):  
Big Data ◽  
Linked Data ◽  
Data Sources ◽  
Social Phenomenon ◽  
Software Projects ◽  
Economic Problems ◽  
The Social ◽  
Business Analysis ◽  
Average Position

The current phenomenon of Big Data – the use of datasets that are too big for traditional business analysis tools used in industry – is driving a shift in how social and economic problems are understood and analysed. This chapter explores the role Big Data can play in analysing the effectiveness of crowd-funding projects, using the data from such a project, which aimed to fund the development of a software plug-in called ‘QPress'. Data analysed included the website metrics of impressions, clicks and average position, which were found to be significantly connected with geographical factors using an ANOVA. These were combined with other country data to perform t-tests in order to form a geo-demographic understanding of those who are displayed advertisements inviting participation in crowd-funding. The chapter concludes that there are a number of interacting variables and that for Big Data studies to be effective, their amalgamation with other data sources, including linked data, is essential to providing an overall picture of the social phenomenon being studied.

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

2022 ◽  
pp. 431-454
Author(s):  
Pinar Kirci
Keyword(s):  
Operating System ◽  
Big Data ◽  
Electronic Medical Records ◽  
Data Storage ◽  
Medical Records ◽  
Data Access ◽  
Efficient Solutions ◽  
Unstructured Data ◽  
Distributed File System ◽  
Healthcare Industry

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

Multisensory experience for enhancing hotel guest experience

2019 ◽  
Vol 31 (11) ◽  
pp. 4313-4337 ◽  
Author(s):  
Minwoo Lee ◽  
Seonjeong (Ally) Lee ◽  
Yoon Koh
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Business Intelligence ◽  
Critical Role ◽  
Big Data Analytics ◽  
Cognitive Effort ◽  
Content Type ◽  
The Relationship ◽  
Multisensory Experience

Purpose This study aims to investigate the effect of customers’ multisensory service experience on customer satisfaction with cognitive effort and affective evaluations using big data and business intelligence techniques. Design/methodology/approach Online customer reviews for all New York City hotels were collected from Tripadvisor.com and analyzed through business intelligence and big data analytics techniques including data mining, text analytics, sentiment analysis and regression analysis. Findings The current study identifies the relationship between affective evaluations (i.e. positive affect and negative affect) and customer satisfaction. Research findings also find the negative effect of reviewer’s cognitive effort on satisfaction rating. More importantly, this study demonstrates the moderating role of multisensory experience as an innovative marketing tool on the relationship between affect/cognitive evaluation and customer satisfaction in the hospitality setting. Originality/value This study is the first study to explore the critical role of sensory marketing on hotel guest experience in the context of hotel customer experience and service innovation, based on big data and business intelligence techniques.

scholarly journals Intelligent credit scoring system using knowledge management

2019 ◽  
Vol 8 (4) ◽  
pp. 391
Author(s):  
Bazzi Mehdi ◽  
Chamlal Hasna ◽  
El Kharroubi Ahmed ◽  
Ouaderhman Tayeb
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Credit Scoring ◽  
Second Step ◽  
Decision Making System ◽  
The Government ◽  
Set Up ◽  
The Arab Spring ◽  
Credit Granting ◽  
Young Entrepreneurs

Promoting entrepreneurship in Morocco among young people has been a challenge for some years of economic and social problems, especially after the events of the Arab Spring. Several programs have been set up by the government for young entrepreneurs. Thus, faced with the large number of credit applications solicited by these young entrepreneurs, banks are obliged to resort to artificial intelligence techniques. For this purpose, the aim of this article is to propose a decision-making system enabling the bank to automate its credit granting process. It is a tool that allows the bank, in the first instance, to select promising projects through a scoring approach adapted to this segment of young entrepreneurs. In a second step, the tool allows the setting of the maximum credit amount to be allocated to the selected project. Finally, based on the knowledge of the bank's experts, the tool proposes a breakdown of the amount granted by the bank into several products adapted to the needs of the entrepreneur.

scholarly journals SIDeKa: The Role of Information Technology for Knowledge Creation

2017 ◽  
Vol 2 (6) ◽  
pp. 570
Author(s):  
Cungki Kusdarjito
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Collection ◽  
Knowledge Creation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Village Level ◽  
The Village

The advancement of big data analytics is paving the way for knowledge creation based on very huge and unstructured data. Currently, information is scattered and growth tremendously, containing many information but difficult to be interpreted. Consequently, traditional approaches are no longer suitable for unstructured data but very rich in information. This situation is different from the role of previous information technology in which information is based on structured data, stored in the local storage, and in more advanced form, information can be retrieved through internet. Meanwhile, in Indonesia data are collected by many institutions with different measurement standard. The nature of the data collection is top-down, carried out by survey which is expensive yet unreliable and stored exclusively by respective institution. SIDeKa (Sistem Informasi Desa dan Kawasan/Village and Regional Information System), which are connected nationally, is proposed as a system of data collection in the village level and prepared by local people. Using SIDeKa, data reliability and readiness can be improved at the local level. The goals of the SIDeKa is not only local people have information in their hand such as poverty level, production, commodity price, the area of cultivated land, and the outbreak of diseases in their village, but also they have information from the neighboring villages or event at the national level. For government, data reliability will improve the policy effectiveness. This paper discusses the implementation and role of SIDeKa for knowledge creation in the village level, especially for the agricultural activities which has been initiated in 2015.Keywords: big data analytics; SIDeKa;  unstructured data.

scholarly journals The Use of Big Data in Regenerative Planning

2018 ◽  
Vol 10 (10) ◽  
pp. 3668 ◽  
Author(s):  
Dorota Kamrowska-Zaluska ◽  
Hanna Obracht-Prondzyńska
Keyword(s):  
Big Data ◽  
Urban Development ◽  
Efficient Solutions ◽  
Data Sources ◽  
Regenerative Approach ◽  
Development Processes ◽  
Regenerative Development ◽  
Regenerative Design ◽  
Development Paradigm

With the increasing significance of Big Data sources and their reliability for studying current urban development processes, new possibilities have appeared for analyzing the urban planning of contemporary cities. At the same time, the new urban development paradigm related to regenerative sustainability requires a new approach and hence a better understanding of the processes changing cities today, which will allow more efficient solutions to be designed and implemented. It results in the need to search for tools which will allow more advanced analyses while assessing the planning projects supporting regenerative development. Therefore, in this paper, the authors study the role of Big Data retrieved from sensor systems, social media, GPS, institutional data, or customer and transaction records. The study includes an enquiry into how Big Data relates to the ecosystem and to human activities, in supporting the development of regenerative human settlements. The aim of the study is to assess the possibilities created by Big Data-based tools in supporting regenerative design and planning and the role they can play in urban projects. In order to do this, frameworks allowing for the assessment of planning projects were analyzed according to their potential to support a regenerative approach. This has been followed by an analysis of the accessibility and reliability of the data sources. Finally, Big Data-based projects were mapped upon aspects of regenerative planning according to the introduced framework.

ERP Integrated Instruments – Opportunities and Challenges for Audit

2021 ◽  
Vol 19 (163) ◽  
pp. 574-586
Author(s):  
Casiana Maria DARIE ◽  
Keyword(s):  
Big Data ◽  
Large Volume ◽  
Business Intelligence ◽  
External Environment ◽  
Financial Accounting ◽  
Computer Assisted ◽  
Questionnaire Data ◽  
Digital Era ◽  
It Systems

The digital era affects all the fundamental areas known so far. In meeting the high levels of competition and industry pressures, the organizations used IT systems to help them achieve market advantages by saving resources, developing domestically and adapting to the challenges posed by the external environment. This paper includes in the first part a description of the role of systems such as ERP, Business Intelligence, "Analytics", "Big Data" and Computer Assisted Audit Techniques – CAAT's in the activity of auditors but also in collecting and processing a large volume of data by those in charge with the financial accounting field. In the second part, with the help of the questionnaire, data on the use of these systems by Romanian auditors were collected and analysed.

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