scholarly journals Text Mining for Big Data Analysis in Financial Sector: A Literature Review

2019 ◽  
Vol 11 (5) ◽  
pp. 1277 ◽  
Author(s):  
Mirjana Pejić Bach ◽  
Živko Krstić ◽  
Sanja Seljan ◽  
Lejla Turulja
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Literature Review ◽  
Financial Sector ◽  
Structured Data ◽  
Decision Making Process ◽  
Strong Impact ◽  
Big Data Technologies ◽  
Research Questions ◽  
Data Investigation

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

scholarly journals Sherlock: an open-source data platform to store, analyze and integrate Big Data for biology

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 409
Author(s):  
Balázs Bohár ◽  
David Fazekas ◽  
Matthew Madgwick ◽  
Luca Csabai ◽  
Marton Olbei ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Open Source ◽  
Large Scale ◽  
Genomic Sequence ◽  
Large Data ◽  
Structured Data ◽  
Biological Research ◽  
Data Platform ◽  
Big Data Technologies

In the era of Big Data, data collection underpins biological research more so than ever before. In many cases this can be as time-consuming as the analysis itself, requiring downloading multiple different public databases, with different data structures, and in general, spending days before answering any biological questions. To solve this problem, we introduce an open-source, cloud-based big data platform, called Sherlock (https://earlham-sherlock.github.io/). Sherlock provides a gap-filling way for biologists to store, convert, query, share and generate biology data, while ultimately streamlining bioinformatics data management. The Sherlock platform provides a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to analyse, process, query and extract the information from extremely complex and large data sets. Furthermore, Sherlock is capable of handling different structured data (interaction, localization, or genomic sequence) from several sources and converting them to a common optimized storage format, for example to the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and easily execute distributed analytical queries on extremely large data files as well as share datasets between teams. The Sherlock platform is freely available on Github, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users are able to easily and quickly create and work with the specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, data analytics, data integration and collaboration through modern big data technologies.

scholarly journals Software product in the system of management of state-owned corporation

2021 ◽  
Vol 14 (3) ◽  
pp. 309-316
Author(s):  
O. V. Danilova ◽  
D. N. Zhuravleva
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Making Process ◽  
Conceptual Basis ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Software Complex ◽  
Managerial Decisions ◽  
Software Product ◽  
Big Data Technologies

The authors present their methodological recommendations for choosing a software complex to make managerial decisions within a corporation’s activity. They study the system of management in state-owned corporations and give reasons for its improvement by means of implementing IT-decisions. Digitalization is transforming managerial decision making process in corporations in accordance with the speed of decision-making, timely maintenance of production process, ways and forms of information transmission. The authors prove that top-management’s responsibility for a company’s competitive positions in the market is growing fast due to the more intensive implementation of information technology and continuous development of innovative decisions. As state-owned enterprises have a complicated interdependent structure, the system of their management becomes a process which should conform with conceptual basis and principles of transformation of a company’s activity within digital economics. The authors prove that suggested recommendations for using big data technologies, determining and implementing IT-decisions make it possible to increase the efficiency of the management system by means of optimizing business process.

scholarly journals Data-driven smart eco-cities and sustainable integrated districts: A best-evidence synthesis approach to an extensive literature review

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Urban Ecology ◽  
Smart Cities ◽  
Evidence Synthesis ◽  
Data Driven ◽  
Extensive Literature ◽  
Best Evidence Synthesis ◽  
Big Data Technologies ◽  
Smart Urbanism

AbstractAs materializations of trends toward developing and implementing urban socio-technical and enviro-economic experiments for transition, eco-cities have recently received strong government and institutional support in many countries around the world due to their ability to function as an innovative strategic niche where to test and introduce various  reforms. There are many models of the eco-city based mainly on either following the principles of urban ecology or combining the strategies of sustainable cities and the solutions of smart cities. The most prominent among these models are sustainable integrated districts and data-driven smart eco-cities. The latter model represents the unprecedented transformative changes the eco-city is currently undergoing in light of the recent paradigm shift in science and technology brought on by big data science and analytics.  This is motivated by the growing need to tackle the problematicity surrounding eco-cities in terms of their planning, development, and governance approaches and practices. Employing a combination of both best-evidence synthesis and narrative approaches, this paper provides a comprehensive state-of-the-art and thematic literature review on sustainable integrated districts and data-driven smart eco-cities. The latter new area is a significant gap in and of itself that this paper seeks to fill together with to what extent the integration of eco-urbanism and smart urbanism is addressed in the era of big data, what driving factors are behind it, and what forms and directions it takes. This study reveals that eco-city district developments are increasingly embracing compact city strategies and becoming a common expansion route for growing cities to achieve urban ecology or urban sustainability. It also shows that the new eco-city projects are increasingly capitalizing on data-driven smart technologies to implement environmental, economic, and social reforms. This is being accomplished by combining the strengths of eco-cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve their performance with respect to sustainability as to its tripartite composition. This in turn means that big data technologies will change eco-urbanism in fundamental and irreversible ways in terms of how eco-cities will be monitored, understood, analyzed, planned, designed, and governed. However, smart urbanism poses significant risks and drawbacks that need to be addressed and overcome in order to achieve the desired outcomes of ecological sustainability in its broader sense. One of the key critical questions raised in this regard pertains to the very potentiality of the technocratic governance of data-driven smart eco-cities and the associated negative implications and hidden pitfalls. In addition, by shedding light on the increasing adoption and uptake of big data technologies in eco-urbanism, this study seeks to assist policymakers and planners in assessing the pros and cons of smart urbanism when effectuating ecologically sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on this topic.

USE OF BIG DATA TECHNOLOGIES IN THE FINANCIAL INDUSTRY

2020 ◽  
Vol 13 (4) ◽  
pp. 32-38
Author(s):  
N.P. Kozlova ◽  
Keyword(s):  
Big Data ◽  
Personal Data ◽  
Financial Sector ◽  
Business Analytics ◽  
Financial Industry ◽  
Big Data Technologies ◽  
Implementation Experience

This article discusses the use of Big Data technologies in the financial sector. It was revealed that big data technologies are extremely important for the financial industry, since with their help financial organizations can make their products and services more accessible and effective for clients. The author defines the concept of Big Data, compares traditional business analytics and big data technologies, characterizes the advantages of using big data technologies in financial organizations. The article analyzes the problems of introducing big data technologies into the practice of Russian financial organizations, caused by a lack of implementation experience and a lack of specialists, as well as additional requirements for ensuring confidentiality when collecting, processing and storing personal data about clients. Several solutions are proposed for the Russian financial industry in the field of working with big data, which will help to make them an integral part of the digital life of the financial sector.

Big data text mining in the financial sector

2019 ◽  
pp. 80-96
Author(s):  
Mirjana Pejić Bach ◽  
Živko Krstić ◽  
Sanja Seljan
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Financial Sector

scholarly journals Modeling and Management Big Data in Databases—A Systematic Literature Review

2020 ◽  
Vol 12 (2) ◽  
pp. 634 ◽  
Author(s):  
Diana Martinez-Mosquera ◽  
Rosa Navarrete ◽  
Sergio Lujan-Mora
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Digital Libraries ◽  
Data Modeling ◽  
Unstructured Data ◽  
Publication Data ◽  
Data Source ◽  
Research Questions ◽  
Entity Relationship

The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question is how the number of published papers about Big Data modeling and management has evolved over time. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 2010 and 2019, were deemed relevant. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. This SLR reveal very interesting facts. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data.

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