Malware Analytics: Review of Data Mining, Machine Learning and Big Data Perspectives

2019 ◽  
Author(s):  
Subash Poudyal ◽  
Zahid Akhtar ◽  
Dipankar Dasgupta ◽  
Kishor Datta Gupta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Mining Machine

scholarly journals Cross-Device Computation Coordination for Mobile Collocated Interactions with Wearables

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 796 ◽  
Author(s):  
Hyoseok Yoon ◽  
Choonsung Shin
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Energy Efficient ◽  
Form Factors ◽  
Analysis Data ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Coordination Problem ◽  
Multiple Devices

Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices should communicate collectively to share computation burdens and stay energy-efficient. In this paper, we present a cross-device computation coordination method for scenarios of mobile collocated interactions with wearables. We formally define a cross-device computation coordination problem and propose a method for solving this problem. Lastly, we demonstrate the feasibility of our approach through experiments and exemplar cases using 12 commercial Android devices with varying computation capabilities.

Big Data Models and the Public Sector

Web Services ◽  
2019 ◽  
pp. 105-126
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

Machine Learning for Business Analytics

2021 ◽  
pp. 232-256
Author(s):  
Kağan Okatan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Business Analytics ◽  
Business Intelligence Systems ◽  
Long Time ◽  
Rules Of The Game

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.

Big Data and Service Science

2016 ◽  
pp. 180-196
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

scholarly journals A technologist view of the next decade evolution of the librarian work environment

2018 ◽  
Vol 186 ◽  
pp. 09004
Author(s):  
André Schaaff ◽  
Marc Wenger
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Work Environment ◽  
New Technologies ◽  
State Of The Art ◽  
Working Environment ◽  
Online Resources ◽  
Mining Machine ◽  
Daily Work

The work environment has deeply evolved in recent decades with the generalisation of IT in terms of hardware, online resources and software. Librarians do not escape this movement and their working environment is becoming essentially digital (databases, online publications, Wikis, specialised software, etc.). With the Big Data era, new tools will be available, implementing artificial intelligence, text mining, machine learning, etc. Most of these technologies already exist but they will become widespread and strongly impact our ways of working. The development of social networks that are "business" oriented will also have an increasing influence. In this context, it is interesting to reflect on how the work environment of librarians will evolve. Maintaining interest in the daily work is fundamental and over-automation is not desirable. It is imperative to keep the human-driven factor. We draw on state of the art new technologies which impact their work, and initiate a discussion about how to integrate them while preserving their expertise.

Sign in / Sign up

Export Citation Format

Share Document