Research on the organization of user needs information in the big data environment

2017 ◽  
Vol 35 (1) ◽  
pp. 36-49
Author(s):  
Feicheng Ma ◽  
Ye Chen ◽  
Yiming Zhao
Keyword(s):  
Big Data ◽  
Conceptual Model ◽  
Semantic Knowledge ◽  
User Needs ◽  
User Privacy ◽  
Content Type ◽  
Knowledge Resources ◽  
Individual Privacy ◽  
Data Environment ◽  
The Web

Purpose This paper aims to propose a conceptual model for improving the organization of user needs information in the big data environment. Design/methodology/approach A conceptual model of the organization of user needs information based on Linked Data techniques is constructed. This model has three layers: the Data Layer, the Semantic Layer and the Application Layer. Findings Requirements for organizing user needs information in the big data environment are identified as follows: improving the intelligence level, establishing standards and guidelines for the description of user needs information, enabling the interconnection of user needs information and considering individual privacy in the organization and analysis of user needs. Practical implications This Web of Needs model could be used to improve knowledge services by matching user needs information with increasing semantic knowledge resources more effectively and efficiently in the big data environment. Originality/value This study proposes a conceptual model, the Web of Needs model, to organize and interconnect user needs. Compared with existing methods, the Web of Needs model satisfies the requirements for the organization of user needs information in the big data environment with regard to four aspects: providing the basis and conditions for intelligent processing of user needs information, using RDF as a description norm, enabling the interconnection of user needs information and setting various protocols to protect user privacy.

Understanding Semantic Web: a conceptual model

2015 ◽  
Vol 64 (1/2) ◽  
pp. 82-100 ◽  
Author(s):  
Michael Calaresu ◽  
Ali Shiri
Keyword(s):  
Semantic Web ◽  
Conceptual Model ◽  
The Body ◽  
Three Dimensional Model ◽  
Conceptual Approach ◽  
Web Based ◽  
Content Type ◽  
Software Applications ◽  
Web Architecture ◽  
The Web

Purpose – The purpose of this article is to explore and conceptualize the Semantic Web as a term that has been widely mentioned in the literature of library and information science. More specifically, its aim is to shed light on the evolution of the Web and to highlight a previously proposed means of attempting to improve automated manipulation of Web-based data in the context of a rapidly expanding base of both users and digital content. Design/methodology/approach – The conceptual analysis presented in this paper adopts a three-dimensional model for the discussion of Semantic Web. The first dimension focuses on Semantic Web’s basic nature, purpose and history, as well as the current state and limitations of modern search systems and related software agents. The second dimension focuses on critical knowledge structures such as taxonomies, thesauri and ontologies which are understood as fundamental elements in the creation of a Semantic Web architecture. In the third dimension, an alternative conceptual model is proposed, one, which unlike more commonly prevalent Semantic Web models, offers a greater emphasis on describing the proposed structure from an interpretive viewpoint, rather than a technical one. This paper adopts an interpretive, historical and conceptual approach to the notion of the Semantic Web by reviewing the literature and by analyzing the developments associated with the Web over the past three decades. It proposes a simplified conceptual model for easy understanding. Findings – The paper provides a conceptual model of the Semantic Web that encompasses four key strata, namely, the body of human users, the body of software applications facilitating creation and consumption of documents, the body of documents themselves and a proposed layer that would improve automated manipulation of Web-based data by the software applications. Research limitations/implications – This paper will facilitate a better conceptual understanding of the Semantic Web, and thereby contribute, in a small way, to the larger body of discourse surrounding it. The conceptual model will provide a reference point for education and research purposes. Originality/value – This paper provides an original analysis of both conceptual and technical aspects of Semantic Web. The proposed conceptual model provides a new perspective on this subject.

scholarly journals A Domain Specific Entity Linking Approach Consuming Multistore Environment

2018 ◽  
pp. 46-52
Author(s):  
Emrah Inan ◽  
Burak Yonyul ◽  
Fatih Tekbacak
Keyword(s):  
Big Data ◽  
Data Models ◽  
Use Cases ◽  
Unstructured Data ◽  
Entity Linking ◽  
Domain Specific ◽  
Storage Technology ◽  
Integrated Environment ◽  
Data Environment ◽  
The Web

Most of the data on the web is non-structural, and it is required that the data should be transformed into a machine operable structure. Therefore, it is appropriate to convert the unstructured data into a structured form according to the requirements and to store those data in different data models by considering use cases. As requirements and their types increase, it fails using one approach to perform on all. Thus, it is not suitable to use a single storage technology to carry out all storage requirements. Managing stores with various type of schemas in a joint and an integrated manner is named as 'multistore' and 'polystore' in the database literature. In this paper, Entity Linking task is leveraged to transform texts into wellformed data and this data is managed by an integrated environment of different data models. Finally, this integrated big data environment will be queried and be examined by presenting the method.

“Bio to bits”: the Millennium Ecosystem Assessment (MA) as a metaphor for Big Data ecosystem assessment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sepehr Ghazinoory ◽  
Shohreh Nasri ◽  
Roya Dastranj ◽  
Alfred Sarkissian
Keyword(s):  
Big Data ◽  
User Needs ◽  
Millennium Ecosystem Assessment ◽  
End User ◽  
Drivers Of Change ◽  
Top Down ◽  
Bottom Up ◽  
Content Type ◽  
Ecosystem Assessment ◽  
Data Ecosystem

PurposeBig Data (BD) is not only a quintessential part of many economic activities but also has evolved into a veritable business ecosystem. However, most Big Data ecosystem (BDE) models have a technical, bottom-up focus and mostly lack the capability for a broad socioeconomic analysis. This paper identifies the Millennium Ecosystem Assessment (MA) as a useful, operational framework and uses a metaphorical analogy to adapt it for the BDE. The top-down approach adopted here allows for seeing the big picture of the BD ecosystem. Meeting “end-user needs” is the main objective of the proposed BDE framework.Design/methodology/approachThe methodology of this paper consists of two parts. First, the MA is adapted for the BDE through a metaphorical analogy. Then, to operationalize and validate the proposed framework, it is applied to an emerging BD ecosystem.FindingsIn total, four types of services are offered in the BD ecosystem: provisioning information and products; regulating; cultural and supporting services. Direct and indirect drivers of change impact ecosystem processes such as BD service provision. Based on the assessment results, interventions can be devised to remedy problems, sustain the ecosystem or accelerate growth. The proposed BDE assessment framework is applied to an emerging BDE as an example of operationalization and validation of the proposed BDE framework.Originality/valueThe strengths of the proposed BDE framework is that, in contrast to existing frameworks that are technical and bottom-up, it is constructed top-down by a metaphorical analogy from the proven MA framework. It is a generic framework with the ultimate objective of meeting the “end-user needs” and does not focus on a single sector or firm. Also, the proposed BDE framework is multi-faceted and considers broad socioeconomic issues such as regulating, cultural and supporting services and drivers of change.

A Big Data smart library recommender system for an educational institution

2018 ◽  
Vol 36 (3) ◽  
pp. 498-523 ◽  
Author(s):  
Aleksandar Simović
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Learning Process ◽  
New Technologies ◽  
Educational Institution ◽  
Economic Benefits ◽  
User Needs ◽  
Continuous Learning ◽  
Multiple Sources ◽  
Content Type

Purpose With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. Design/methodology/approach This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. Findings As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. Social implications The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. Originality/value A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

Adoption of Big Data analytics in construction: development of a conceptual model

2019 ◽  
Vol 9 (4) ◽  
pp. 564-579 ◽  
Author(s):  
Jiwat Ram ◽  
Numan Khan Afridi ◽  
Khawar Ahmed Khan
Keyword(s):  
Big Data ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Environmental Benefits ◽  
Relative Advantage ◽  
Adoption Process ◽  
Senior Managers ◽  
Content Type ◽  
New Model ◽  
Improved Design

PurposeBig Data (BD) is being increasingly used in a variety of industries including construction. Yet, little research exists that has examined the factors which drive BD adoption in construction. The purpose of this paper is to address this gap in knowledge.Design/methodology/approachData collected from literature (55 articles) were analyzed using content analysis techniques. Taking a two-pronged approach, first study presents a systematic perspective of literature on BD in construction. Then underpinned by technology–organization–environment theory and supplemented by literature, a conceptual model of five antecedent factors of BD adoption for use in construction is proposed.FindingsThe results show that BD adoption in construction is driven by a number of factors: first, technological: augmented BD–BIM integration and BD relative advantage; second, organizational: improved design and execution efficiencies, and improved project management capabilities; and third, environmental: augmented availability of BD-related technology for construction. Hypothetical relationships involving these factors are then developed and presented through a new model of BD adoption in construction.Research limitations/implicationsThe study proposes a number of adoption factors and then builds a new conceptual model advancing theories on technologies adoption in construction.Practical implicationsFindings will help managers (e.g. chief information officers, IT/IS managers, business and senior managers) to understand the factors that drive adoption of BD in construction and plan their own BD adoption. Results will help policy makers in developing policy guidelines to create sustainable environment for the adoption of BD for enhanced economic, social and environmental benefits.Originality/valueThis paper develops a new model of BD adoption in construction and proposes some new factors of adoption process.

Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain

2019 ◽  
Vol 27 (2) ◽  
pp. 606-633 ◽  
Author(s):  
Shirish Jeble ◽  
Sneha Kumari ◽  
V.G. Venkatesh ◽  
Manju Singh
Keyword(s):  
Social Capital ◽  
Big Data ◽  
Conceptual Model ◽  
Predictive Analytics ◽  
Relevant Literature ◽  
Critical Role ◽  
Social Capital Theory ◽  
Future Research ◽  
Reconstruction Process ◽  
Content Type

Purpose The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction. Design/methodology/approach After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC. Findings The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance. Research limitations/implications The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated. Practical implications Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities. Social implications Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs. Originality/value The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

Automated data-driven profiling: threats for group privacy

2019 ◽  
Vol 28 (2) ◽  
pp. 183-197 ◽  
Author(s):  
Paola Mavriki ◽  
Maria Karyda
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Big Data Analytics ◽  
User Profiling ◽  
Data Driven ◽  
Extensive Literature ◽  
Content Type ◽  
Privacy Threats ◽  
Individual Privacy ◽  
Privacy Risks

Purpose User profiling with big data raises significant issues regarding privacy. Privacy studies typically focus on individual privacy; however, in the era of big data analytics, users are also targeted as members of specific groups, thus challenging their collective privacy with unidentified implications. Overall, this paper aims to argue that in the age of big data, there is a need to consider the collective aspects of privacy as well and to develop new ways of calculating privacy risks and identify privacy threats that emerge. Design/methodology/approach Focusing on a collective level, the authors conducted an extensive literature review related to information privacy and concepts of social identity. They also examined numerous automated data-driven profiling techniques analyzing at the same time the involved privacy issues for groups. Findings This paper identifies privacy threats for collective entities that stem from data-driven profiling, and it argues that privacy-preserving mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Moreover, this paper concludes that collective privacy threats may be different from threats for individuals when they are not members of a group. Originality/value Although research evidence indicates that in the age of big data privacy as a collective issue is becoming increasingly important, the pluralist character of privacy has not yet been adequately explored. This paper contributes to filling this gap and provides new insights with regard to threats for group privacy and their impact on collective entities and society.

New agent-based proactive migration method and system for Big Data Environment (BDE)

2015 ◽  
Vol 32 (8) ◽  
pp. 2443-2466 ◽  
Author(s):  
De-gan Zhang ◽  
Xiao-dong Song ◽  
Xiang Wang ◽  
Ke Li ◽  
Wen-bin Li ◽  
...  
Keyword(s):  
Big Data ◽  
Service Discovery ◽  
Mobile User ◽  
Selection Strategy ◽  
Mobile Service ◽  
Content Type ◽  
Agent Based ◽  
Fuzzy Neural ◽  
Data Environment ◽  
Service Task

Purpose – Mobile Service of Big Data can be supported by the fused technologies of computing, communication and digital multimedia. The purpose of this paper is to propose new agent-based proactive migration method and system for Big Data Environment (BDE). Design/methodology/approach – First, the authors have designed new relative fusion method for making decision based on fuzzy-neural network. The method can make the fusion belief degree to be improved. Then the authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. Finally, the authors have designed the application system, which can support proactive seamless migration function for big data. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device. Findings – The authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device. The designed system is convenient to work and use during mobility, and which is useful or helpful for mobile user in the BDE. Originality/value – The authors have clarified and realizes how to transfer service tasks among different distances in Big Data Environment (BDE). The authors have given a formal description and classification of the mobile service task, which is independent of the realization mechanism. In the designed and developed application system, the new idea adopts fuzzy-neural control theory to make decision for task-oriented proactive seamless migration application.

Phones, privacy, and predictions

2018 ◽  
Vol 44 (2) ◽  
pp. 483-502 ◽  
Author(s):  
Isha Ghosh ◽  
Vivek Singh
Keyword(s):  
Mobile Phone ◽  
Predictive Power ◽  
Mobile App ◽  
Digital Information ◽  
Structured Interviews ◽  
User Privacy ◽  
Content Type ◽  
Personal Lives ◽  
Individual Privacy ◽  
Privacy Settings

Purpose Mobile phones have become one of the most favored devices to maintain social connections as well as logging digital information about personal lives. The privacy of the metadata being generated in this process has been a topic of intense debate over the last few years, but most of the debate has been focused on stonewalling such data. At the same time, such metadata is already being used to automatically infer a user’s preferences for commercial products, media, or political agencies. The purpose of this paper is to understand the predictive power of phone usage features on individual privacy attitudes. Design/methodology/approach The present study uses a mixed-method approach, involving analysis of mobile phone metadata, self-reported survey on privacy attitudes and semi-structured interviews. This paper analyzes the interconnections between user’s social and behavioral data as obtained via their phone with their self-reported privacy attitudes and interprets them based on the semi-structured interviews. Findings The findings from the study suggest that an analysis of mobile phone metadata reveals vital clues to a person’s privacy attitudes. This study finds that multiple phone signals have significant predictive power on an individual’s privacy attitudes. The results motivate a newer direction of automatically inferring a user’s privacy attitudes by leveraging their phone usage information. Practical implications An ability to automatically infer a user’s privacy attitudes could allow users to utilize their own phone metadata to get automatic recommendations for privacy settings appropriate for them. This study offers information scientists, government agencies and mobile app developers, an understanding of user privacy needs, helping them create apps that take these traits into account. Originality/value The primary value of this paper lies in providing a better understanding of the predictive power of phone usage features on individual privacy attitudes.

Research on architecture of security video surveillance network cascade system with big data

2016 ◽  
Vol 13 (1) ◽  
pp. 77-81 ◽  
Author(s):  
Zhihua Li ◽  
Zianfei Tang ◽  
Yihua Yang
Keyword(s):  
Big Data ◽  
Parallel Processing ◽  
Video Surveillance ◽  
Surveillance System ◽  
Processing Technology ◽  
Video Surveillance System ◽  
Surveillance Network ◽  
Content Type ◽  
Architecture Model ◽  
Data Environment

Purpose The high-efficient processing of mass data is a primary issue in building and maintaining security video surveillance system. This paper aims to focus on the architecture of security video surveillance system, which was based on Hadoop parallel processing technology in big data environment. Design/methodology/approach A hardware framework of security video surveillance network cascaded system (SVSNCS) was constructed on the basis of Internet of Things, network cascade technology and Hadoop platform. Then, the architecture model of SVSNCS was proposed using the Hadoop and big data processing platform. Findings Finally, we suggested the procedure of video processing according to the cascade network characteristics. Originality/value Our paper, which focused on the architecture of security video surveillance system in big data environment on the basis of Hadoop parallel processing technology, provided high-quality video surveillance services for security area.

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