Big data and advanced analytics tools

2016 ◽  
Author(s):  
Rahul Kumar Chawda ◽  
Ghanshyam Thakur
Keyword(s):  
Big Data ◽  
Advanced Analytics

Big Data and Natural Language Processing for Analysing Railway Safety

2018 ◽  
pp. 240-267
Author(s):  
Kanza Noor Syeda ◽  
Syed Noorulhassan Shirazi ◽  
Syed Asad Ali Naqvi ◽  
Howard J Parkinson ◽  
Gary Bamford
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Machine Intelligence ◽  
Data Availability ◽  
Accident Data ◽  
Data Driven Approach ◽  
Advanced Analytics ◽  
The Uk

Due to modern powerful computing and the explosion in data availability and advanced analytics, there should be opportunities to use a Big Data approach to proactively identify high risk scenarios on the railway. In this chapter, we comprehend the need for developing machine intelligence to identify heightened risk on the railway. In doing so, we have explained a potential for a new data driven approach in the railway, we then focus the rest of the chapter on Natural Language Processing (NLP) and its potential for analysing accident data. We review and analyse investigation reports of railway accidents in the UK, published by the Rail Accident Investigation Branch (RAIB), aiming to reveal the presence of entities which are informative of causes and failures such as human, technical and external. We give an overview of a framework based on NLP and machine learning to analyse the raw text from RAIB reports which would assist the risk and incident analysis experts to study causal relationship between causes and failures towards the overall safety in the rail industry.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

Big Data Analytics in Intra-Data Center Networks and Components of Data Mining

2016 ◽  
pp. 82-89
Author(s):  
Pushpa Mannava
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Center ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Center Networks ◽  
Advanced Analytics ◽  
Scalable Design ◽  
Data Collections

Data mining is considered as a vital procedure as it is used for locating brand-new, legitimate, useful as well as reasonable kinds of data. The assimilation of data mining methods in cloud computing gives a versatile and also scalable design that can be made use of for reliable mining of significant quantity of data from virtually incorporated data resources with the goal of creating beneficial information which is useful in decision making. The procedure of removing concealed, beneficial patterns, as well as useful info from big data is called big data analytics. This is done via using advanced analytics techniques on large data collections. This paper provides the information about big data analytics in intra-data center networks, components of data mining and also techniques of Data mining.

Information and reformation in KM systems: big data and strategic decision-making

2017 ◽  
Vol 21 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Ali Intezari ◽  
Simone Gressel
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Strategic Decisions ◽  
Content Type ◽  
For Profit ◽  
Not For Profit ◽  
Advanced Analytics ◽  
Practical Implications

Purpose The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems. Design/methodology/approach To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature. Findings Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics. Practical implications The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making. Originality/value This is the first typology of data-based decision-making considering advanced analytics.

scholarly journals Examining the Data Analytics Skill Gap in Mid-Level Marketing Professionals, Driven by the Continuing Exponential Growth of Big Data

2017 ◽  
Vol 5 (3) ◽  
pp. 267
Author(s):  
Richard J. Vaughan
Keyword(s):  
Big Data ◽  
Customer Service ◽  
Exponential Growth ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Skill Requirements ◽  
Advanced Analytics ◽  
Service Departments ◽  
The Impact ◽  
Term Data

<p><em>The purpose of this paper is to examine the impact of big data in the skill requirements of marketing professionals. Over 12,000 marketing job listings in the top six major marketing cities were researched to determine how many positions required big data analytics skills. 38% of big data’s biggest impact is in marketing, advanced analytics are used in customer-facing marketing, sales and customer service departments. This research found 39% of all marketing positions in the defined search criteria listed “big data” as a required skill in the combined six cities. It is interesting to note the relatively new term “big data” was cited 39% and surpassed the term “Data”, which was cited 35% across all 12,796 positions.</em><em></em></p><p><br /><em></em></p>

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