Privacy-Preserving Mechanisms for Heterogeneous Data Types

2021 ◽  
Author(s):  
Mariana Cunha
Keyword(s):  
Heterogeneous Data ◽  
Privacy Preserving ◽  
Data Types

scholarly journals A survey of privacy-preserving mechanisms for heterogeneous data types

2021 ◽  
Vol 41 ◽  
pp. 100403
Author(s):  
Mariana Cunha ◽  
Ricardo Mendes ◽  
João P. Vilela
Keyword(s):  
Heterogeneous Data ◽  
Privacy Preserving ◽  
Data Types

Extracting insights from big social data for smarter tourism destination management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gianluca Solazzo ◽  
Ylenia Maruccia ◽  
Gianluca Lorenzo ◽  
Valentina Ndou ◽  
Pasquale Del Vecchio ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Destination Image ◽  
Data Types ◽  
Social Data ◽  
Content Type ◽  
Management And Development ◽  
Destination Attractiveness ◽  
Tourist Demand ◽  
Tourist Behaviour

Purpose This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image. Design/methodology/approach This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study. Findings Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination. Originality/value The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

Heterogeneous Text and Numerical Data Mining with Possible Applications in Business and Financial Sectors

Data Mining ◽  
2013 ◽  
pp. 816-836
Author(s):  
Farid Bourennani ◽  
Shahryar Rahnamayan
Keyword(s):  
Data Mining ◽  
Quantitative Data ◽  
World Wide ◽  
Numerical Data ◽  
Heterogeneous Data ◽  
Research Centers ◽  
Unified Approach ◽  
Data Types ◽  
Qualitative And Quantitative ◽  
Uniform Manner

Nowadays, many world-wide universities, research centers, and companies share their own data electronically. Naturally, these data are from heterogeneous types such as text, numerical data, multimedia, and others. From user side, this data should be accessed in a uniform manner, which implies a unified approach for representing and processing data. Furthermore, unified processing of the heterogeneous data types can lead to richer semantic results. In this chapter, we present a unified pre-processing approach that leads to generation of richer semantics of qualitative and quantitative data.

Bio-Inspired Algorithms in Bioinformatics I

2011 ◽  
pp. 236-240
Author(s):  
José Antonio Seoane Fernández ◽  
Mónica Miguélez Rico
Keyword(s):  
Human Genome ◽  
Heterogeneous Data ◽  
Genome Project ◽  
Biological Data ◽  
Organizational Level ◽  
Data Types ◽  
Proteomic Data ◽  
New Information ◽  
Cell Tissue ◽  
The Human Genome Project

Large worldwide projects like the Human Genome Project, which in 2003 successfully concluded the sequencing of the human genome, and the recently terminated Hapmap Project, have opened new perspectives in the study of complex multigene illnesses: they have provided us with new information to tackle the complex mechanisms and relationships between genes and environmental factors that generate complex illnesses (Lopez, 2004; Dominguez, 2006). Thanks to these new genomic and proteomic data, it becomes increasingly possible to develop new medicines and therapies, establish early diagnoses, and even discover new solutions for old problems. These tasks however inevitably require the analysis, filtration, and comparison of a large amount of data generated in a laboratory with an enormous amount of data stored in public databases, such as the NCBI and the EBI. Computer sciences equip biomedicine with an environment that simplifies our understanding of the biological processes that take place in each and every organizational level of live matter (molecular level, genetic level, cell, tissue, organ, individual, and population) and the intrinsic relationships between them. Bioinformatics can be described as the application of computational methods to biological discoveries (Baldi, 1998). It is a multidisciplinary area that includes computer sciences, biology, chemistry, mathematics, and statistics. The three main tasks of bioinformatics are the following: develop algorithms and mathematical models to test the relationships between the members of large biological datasets, analyze and interpret heterogeneous data types, and implement tools that allow the storage, retrieve, and management of large amounts of biological data.

A scheme for the sticky policy representation supporting secure cyber-threat intelligence analysis and sharing

2019 ◽  
Vol 27 (5) ◽  
pp. 687-710
Author(s):  
Oleksii Osliak ◽  
Andrea Saracino ◽  
Fabio Martinelli
Keyword(s):  
Data Representation ◽  
Privacy Preserving ◽  
Entity Recognition ◽  
Process Time ◽  
Practical Implementation ◽  
Data Types ◽  
Content Type ◽  
Threat Intelligence ◽  
Cyber Threat ◽  
Cyber Threat Intelligence

Purpose This paper aims to propose a structured threat information expression (STIX)-based data representation for privacy-preserving data analysis to report format and semantics of specific data types and to represent sticky policies in the format of embedded human-readable data sharing agreements (DSAs). More specifically, the authors exploit and extend the STIX standard to represent in a structured way analysis-ready pieces of data and the attached privacy policies. Design/methodology/approach The whole scheme is designed to be completely compatible with the STIX 2.0 standard for cyber-threat intelligence (CTI) representation. The proposed scheme will be implemented in this work by defining the complete scheme for representing an email, which is more expressive than the standard one defined for STIX, designed specifically for spam email analysis. Findings Moreover, the paper provides a new scheme for general DSA representation that has been practically applied for the process of encoding specific attributes in different CTI reports. Research limitations/implications Because of the chosen approach, the research results may have limitations. Specifically, current practice for entity recognition has the limitation that was discovered during the research. However, its effect on process time was minimized and the way for improvement was proposed. Originality/value This paper has covered the existing gap including the lack of generality in DSA representation for privacy-preserving analysis of structured CTI. Therefore, the new model for DSA representation was introduced, as well as its practical implementation.

Adapting Warehouse Management Systems to the Requirements of the Evolving Era of Industry 4.0

2017 ◽  
Author(s):  
Cyril Alias ◽  
Udo Salewski ◽  
Viviana Elizabeth Ortiz Ruiz ◽  
Frank Eduardo Alarcón Olalla ◽  
José do Egypto Neirão Reymão ◽  
...  
Keyword(s):  
Information Systems ◽  
Technological Innovation ◽  
Processing Speed ◽  
Industrial Revolution ◽  
Heterogeneous Data ◽  
Management Systems ◽  
Sources Of Information ◽  
Warehouse Management ◽  
Data Types ◽  
Digital World

With global megatrends like automation and digitization changing societies, economies, and ultimately businesses, shift is underway, disrupting current business plans and entire industries. Business actors have accordingly developed an instinctive fear of economic decline and realized the necessity of taking adequate measures to keep up with the times. Increasingly, organizations find themselves in an evolve-or-die race with their success depending on their capability of recognizing the requirements for serving a specific market and adopting those requirements accurately into their own structure. In the transportation and logistics sector, emerging technological and information challenges are reflected in fierce competition from within and outside. Especially, processes and supporting information systems are put to the test when technological innovation start to spread among an increasing number of actors and promise higher performance or lower cost. As to warehousing, technological innovation continuously finds its way into the premises of the heterogeneous warehouse operators, leading to modifications and process improvements. Such innovation can be at the side of the hardware equipment or in the form of new software solutions. Particularly, the fourth industrial revolution is globally underway. Same applies to Future Internet technologies, a European term for innovative software technologies and the research upon them. On the one hand, new hardware solutions using robotics, cyber-physical systems and sensors, and advanced materials are constantly put to widespread use. On the other one, software solutions based on intensified digitization including new and more heterogeneous sources of information, higher volumes of data, and increasing processing speed are also becoming an integral part of popular information systems for warehouses, particularly for warehouse management systems. With a rapidly and dynamically changing environment and new legal and business requirements towards processes in the warehouses and supporting information systems, new performance levels in terms of quality and cost of service are to be obtained. For this purpose, new expectations of the functionality of warehouse management systems need to be derived. While introducing wholly new solutions is one option, retrofitting and adapting existing systems to the new requirements is another one. The warehouse management systems will need to deal with more types of data from new and heterogeneous data sources. Also, it will need to connect to innovative machines and represent their respective operating principles. In both scenarios, systems need to satisfy the demand for new features in order to remain capable of processing information and acting and, thereby, to optimize logistics processes in real time. By taking a closer look at an industrial use case of a warehouse management system, opportunities of incorporating such new requirements are presented as the system adapts to new data types, increased processing speed, and new machines and equipment used in the warehouse. Eventually, the present paper proves the adaptability of existing warehouse management systems to the requirements of the new digital world, and viable methods to adopt the necessary renovation processes.

scholarly journals Biclique: An R package for Maximal Biclique Enumeration in Bipartite Graphs

2020 ◽  
Author(s):  
Yuping Lu ◽  
Charles A. Phillips ◽  
Michael A. Langston
Keyword(s):  
State Of The Art ◽  
Basic Research ◽  
R Package ◽  
Bipartite Graphs ◽  
Heterogeneous Data ◽  
General Purpose ◽  
Public Repository ◽  
Data Types ◽  
Statistical Programming ◽  
Reference Manual

Abstract Objective Bipartite graphs are widely used to model relationships between pairs of heterogeneous data types. Maximal bicliques are foundational structures in such graphs, and their enumeration is an important task in systems biology, epidemiology and many other problem domains. Thus, there is a need for an efficient, general purpose, publicly available tool to enumerate maximal bicliques in bipartite graphs. The statistical programming language R is a logical choice for such a tool, but until now no R package has existed for this purpose. Our objective is to provide such a package, so that the research community can more easily perform this computationally demanding task. Results Biclique is an R package that takes as input a bipartite graph and produces a listing of all maximal bicliques in this graph. Input and output formats are straightforward, with examples provided both in this paper and in the package documentation. Biclique employs a state-of-the-art algorithm previously developed for basic research in functional genomics. This package, along with its source code and reference manual, are freely available from the CRAN public repository at https://cran.r-project.org/web/packages/biclique/index.html .

scholarly journals Social sciences, humanities and their interoperability with the European Open Science Cloud: What is SSHOC?

2019 ◽  
Vol 72 (2) ◽  
pp. 383-391
Author(s):  
Ivana Ilijašić Veršić ◽  
Julian Ausserhofer
Keyword(s):  
Social Sciences ◽  
Open Science ◽  
Heterogeneous Data ◽  
Data Types ◽  
Social Sciences And Humanities ◽  
Digital Scholarship ◽  
Confidential Data ◽  
Infrastructural Support ◽  
And Training ◽  
Human Component

The EC H2020 cluster project SSHOC aims to provide a full-fledged Social Sciences and Humanities Open Cloud where data, tools, and training are available and accessible for users of SSH data. The focus of the project is determined by the goal to further the innovation of infrastructural support for digital scholarship, to stimulate multidisciplinary collaboration across the various subfields of SSH and beyond, and to increase the potential for societal impact. The intention is to create a European open cloud ecosystem for social sciences and humanities, consisting of an infrastructural and human component. SSHOC will encourage secure environments for sharing and using sensitive and confidential data. It will contribute to the Open Science agenda and realization of the European Open Science Cloud (EOSC), as well as contribute to innovations stemming from the coupling of heterogeneous data types and work on the Interoperability principle of FAIR.

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