Toward a Knowledge-Based Model to Fight Against Cybercrime Within Big Data Environments: A Set of Key Questions to Introduce the Topic

2020 ◽  
pp. 75-101
Author(s):  
Mustapha El Hamzaoui ◽  
Faycal Bensalah
Keyword(s):  
Big Data ◽  
Knowledge Based ◽  
Key Questions

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

scholarly journals Design of Link Evaluation Method to Improve Reliability based on Linked Open Big Data and Natural Language Processing

2018 ◽  
Vol 7 (3.33) ◽  
pp. 168
Author(s):  
Yonglak SHON ◽  
Jaeyoung PARK ◽  
Jangmook KANG ◽  
Sangwon LEE
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Language Processing ◽  
Confidence Level ◽  
Linked Data ◽  
Evaluation Method ◽  
Data Sets ◽  
Knowledge Based ◽  
Global Data ◽  
Improve Reliability

The LOD data sets consist of RDF Triples based on the Ontology, a specification of existing facts, and by linking them to previously disclosed knowledge based on linked data principles. These structured LOD clouds form a large global data network, which provides a more accurate foundation for users to deliver the desired information. However, it is difficult to identify that, if the presence of the same object is identified differently across several LOD data sets, they are inherently identical. This is because objects with different URIs in the LOD datasets must be different and they must be closely examined for similarities in order to judge them as identical. The aim of this study is that the prosed model, RILE, evaluates similarity by comparing object values of existing specified predicates. After performing experiments with our model, we could check the improvement of the confidence level of the connection by extracting the link value.  

Web Intelligence

Big Data ◽  
2016 ◽  
pp. 711-733 ◽  
Author(s):  
Jafreezal Jaafar ◽  
Kamaluddeen Usman Danyaro ◽  
M. S. Liew
Keyword(s):  
Big Data ◽  
Knowledge Base ◽  
Data Model ◽  
Performance Comparison ◽  
Web Ontology Language ◽  
Web Intelligence ◽  
Knowledge Based ◽  
Ontology Language ◽  
And Performance ◽  
Fuzzy Knowledge

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.

scholarly journals Optimizing the Electronic Health Records Through Big Data Analytics: A Knowledge-Based View

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 136223-136231 ◽  
Author(s):  
Caifeng Zhang ◽  
Rui Ma ◽  
Shiwei Sun ◽  
Yujie Li ◽  
Yichuan Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Electronic Health Records ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Records ◽  
Knowledge Based ◽  
Electronic Health

Fuzzy knowledge based performance analysis on big data

2020 ◽  
Vol 389 ◽  
pp. 218-228
Author(s):  
Neha Bharill ◽  
Aruna Tiwari ◽  
Aayushi Malviya ◽  
Om Prakash Patel ◽  
Akahansh Gupta ◽  
...  
Keyword(s):  
Big Data ◽  
Performance Analysis ◽  
Knowledge Based ◽  
Fuzzy Knowledge

scholarly journals Online Knowledge-Based Model for Big Data Topic Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Taimoor Khan ◽  
Mehr Durrani ◽  
Shehzad Khalid ◽  
Furqan Aziz
Keyword(s):  
Big Data ◽  
Knowledge Base ◽  
Scale Up ◽  
Streaming Data ◽  
Data Dependency ◽  
High Data ◽  
Knowledge Based ◽  
Processing Cost ◽  
Domain Models ◽  
User Intervention

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

A knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing

2015 ◽  
Vol 79 ◽  
pp. 3-17 ◽  
Author(s):  
Christian Esposito ◽  
Massimo Ficco ◽  
Francesco Palmieri ◽  
Aniello Castiglione
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Stream Processing ◽  
Big Data Analytics ◽  
Knowledge Based

scholarly journals THEORIES AND ANTECEDENTS OF KNOWLEDGE SHARING BEHAVIOUR IN VIRTUAL COMMUNITY: A SYSTEMATIC REVIEW

2020 ◽  
Vol 11 (10) ◽  
pp. 32-51
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Web 2.0 ◽  
Knowledge Sharing ◽  
Industry 4.0 ◽  
Virtual Community ◽  
The Internet ◽  
Knowledge Based ◽  
Comprehensive Study

Virtual Community (VC) is regarded as the best platform for professionals in various fields to share their expertise and knowledge. Since the escalation of web 2.0 and the internet within the last decade and the booming interest in big data and expansion of industry 4.0, VC is deemed as an ideal proxy for practitioners to share and earned instant knowledge that can be implemented within business activities and day to day application. Despite this emerging interest, there has been no comprehensive study on the overall antecedents of KS in VC. Applying for a systematic review, a total of 68 relevant articles that discusses knowledge sharing (KS) via VC are evaluated. Several central themes of theories applied in this field within the literature are discussed on its importance and relevance. Important antecedents are also reviewed on its practicality and implementation in understanding the role of KS in VC. The implication of this review would benefit stakeholders in maintaining the sustainability of VC as the platform for a knowledge-based society.

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