Web Usage Mining Issues in Big Data

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

Future of Business Intelligence in Cloud Computing

Cloud business intelligence can solve numerous management issues that are faced by many businesses. If it is used in a correct manner, it can substitute seamless utilization of crucial information in the growth of business. In the self-hosted environment, business intelligence will face resource crisis situation on the never-ending expansion of warehouses and OLAP's demands on the primary network. Today, cloud computing has instigated optimism for the prospects of future business intelligence. But thing to focus here is, how will business intelligence be implemented on cloud platform, and further, how will the traffic be managed and what will the demand profile look like? Moreover, in today's world, data generated on a daily basis from many different sources are numerous and valuable information for making effective decisions. This chapter focuses and tries to attempt these questions related to taking business intelligence to the cloud.

Task Scheduling in Cloud Computing Using Spotted Hyena Optimizer

Cloud computing provides internet users with quick and efficient tools to access and share the data. One of the most important research problems that need to be addressed is the effective performance of cloud-based task scheduling. Different cloud-based task scheduling algorithms based on metaheuristic optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO) scheduling algorithms are demonstrated and analyzed. In this chapter, cloud computing based on the spotted hyena optimizer (SHO) is proposed with a novel task scheduling technique. SHO algorithm is population-based and inspired by nature's spotted hyenas to achieve global optimization over a given search space. The findings show that the suggested solution performs better than other competitor algorithms.

Master Data-Supply Chain Management, the Key Lever for Collaborative and Compliant Partnerships in Big Data Era

With the advent of big data in supply chain information systems (SCIS), data compliance and consistency are becoming vital. Today, SC stakeholders need to pay more attention to data governance, which requires changing traditional management methods. These can be achieved by mastering a single repository through what is usually named master data management (MDM). However, accomplishing this objective is particularly challenging in the complex logistics networks of supply chains (SC). The volatile nature of the logistics flows that increase exponentially because of the facilitation of exchanges' interoperability in the information systems. In this chapter, the authors propose an MDM-based framework for the supply chain information systems as an enabler for strong collaboration and compliance. For proof of concept, a case study of a French hypermarket is examined through benchmarking scenarios. The outcomes of the case validate our approach as a hands-on solution when applied correctly. Finally, the chapter discusses the key findings and the limitations of our framework.

Migrating Complex Applications From On-Premises to Cloud Interface

Migrating applications on the cloud storage from the systems physically available on the premises is a difficult task. There are a lot of research articles providing solutions for the current problem of migration of applications by software industry. The chapter is shedding light on how to migrate the application efficiently using mathematical approach. The dependency of migration is directly proportional to the size of the data and the speed of the network. There are a number of storage options available on cloud for easy accessibility, cache-ability, and consistency. This chapter focuses on difficult migration of an application.

A Decadal Walkthrough on Energy Modelling for Cloud Datacenters

Cloud computing is the growing field in the industry, and every scale industry needs it now. The high scale usage of cloud has resulted in huge power consumption, and this power consumption has led to increase of carbon footprint affecting our mother nature. Thus, we need to optimize the power usage in the cloud servers. Various models are used to tackle this situation, of which one is a model based on link load. It minimized the bit energy consumption of network usage which includes energy efficiency routing and load balancing. Over this, multi-constraint rerouting is also adapted. Other power models which have been adapted are virtualization framework using multi-tenancy-oriented data center. It works by accommodating heterogeneous networks among virtual machines in virtual private cloud. Another strategy that is adopted is cloud partitioning concept using game theory. Other methods that are adopted are load spreading algorithm by shortest path bridging, load balancing by speed scaling, load balancing using graph constraint, and insert ranking method.

Impacts and Challenges of Cost-Effective Approaches Using Hybrid Cloud Infrastructure Model in Business Analytics

The cost-effective methodology and its implementation are the primary approaches towards cost computing to bring effectiveness with the proper requirements and provide the proper solution. This chapter focuses on the discussion about the cost-effective method using cloud infrastructure model for building and management of on-premise with the off-premise cloud service provider in business analytics. This chapter also elaborates the methodology undertaken and design considerations for implementation of cloud infrastructure with non-virtualized and on-premise infrastructure environment. The experiment using YGCIS (YG-cloud infrastructure solution) methodology is built for business analytics platform where infrastructure and its resources play a vital role. The cost-effective approach for total cost ownership (TCO) is implemented using YGCCS (YG-cost computing solution) framework. Thus, the solution obtained after implementing the above frameworks increases ROI % and reduces the TCO, impacting the business analytics needs.

A Novel Resource Allocation and Scheduling Based on Priority Using Metaheuristic for Cloud Computing Environment

In cloud computing systems, current works do not challenge the database failure rates and recovery techniques. In this chapter, priority-based resource allocation and scheduling technique is proposed by using the metaheuristic optimization approach spotted hyena optimizer (SHO). Initially, the emperor penguins predict the workload of user server and resource requirements. The expected completion time of each server is estimated with this predicted workload. Then the resources activities are classified based on the criteria of the deadline and the asset. Further, the employed servers are classified based on the workload and the estimated completed time. The proposed approach is compared with existing resource utilization techniques in terms of percentage of resource allocation, missed deadlines, and average server workload.

Cloud Governance

Artificial intelligence within the area of computer vision is creating a replacement genre in detection industry. Here, AI is using the power of computer vision in creating advanced educational software LMS that detects student emotions during online classes, interviews, and judges their understanding and concentration level. It also generates automated content in step with their needs. This LMS cannot only judge audio, video, and image of a student; it also judges the voice tone. Through this judgement, the AI model understands how much a student is learning, effectivity, intellect, and drawbacks. In this chapter, the power of deep learning models VGG Net and Alex Net in LMS computer vision are used. This LMS architecture will be able to work like a virtual teacher that will be taking a parental guide to students.

Integrating Business Intelligence With Cloud Computing

Cloud Computing has emerged as an expression that has described various other computing concepts that involve computers that are interconnected virtually. It is so prominent that it has modified the architecture by incorporating new design principles. Also, the present economic crisis, which is being experienced by most of the world, has oriented us towards cloud computing and its efficient services. Here, business intelligence plays a pivotal role in extraction of valuable information and identifying hidden patterns of data. Also, any organization in striving stage can also act smartly with the use of various business intelligence solutions. Various benefits are also offered by the BI solutions such as working together as a team and identifying various resolutions. The contribution of this chapter is to show how the cloud computing environment has been merged with business intelligence to fulfil the future need of uplifting of economy.