Cloud computing in data mining – a survey
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paper_title: Characterising Data Mining software

paper_content:

The ever-increasing number of fielded Data Mining applications is evidence that the technology works and produces added value in a variety of business areas. Most of the research-lab generated algorithms have found their way under various guises in a number of commercial software packages. When considering the use of Data Mining, the average business user is now faced with a plethora of DM software to choose from. In order to be informed, such a choice requires a standard basis from which to compare and contrast alternatives along relevant, business-focused dimensions, as well as the location of candidate tools within the space outlined by these dimensions. This paper aims at meeting this business requirement. It presents a standard schema for the characterisation of Data Mining software tools and the results of a recent survey of 41 popular Data Mining tools described within this schema.
---
paper_title: Characterising Data Mining software

paper_content:

The ever-increasing number of fielded Data Mining applications is evidence that the technology works and produces added value in a variety of business areas. Most of the research-lab generated algorithms have found their way under various guises in a number of commercial software packages. When considering the use of Data Mining, the average business user is now faced with a plethora of DM software to choose from. In order to be informed, such a choice requires a standard basis from which to compare and contrast alternatives along relevant, business-focused dimensions, as well as the location of candidate tools within the space outlined by these dimensions. This paper aims at meeting this business requirement. It presents a standard schema for the characterisation of Data Mining software tools and the results of a recent survey of 41 popular Data Mining tools described within this schema.
---
paper_title: Selection Criteria for Data Mining Software: A Study

paper_content:

Nowadays software are flooded in the market, there is a need for selection criteria of software package that should be available for individuals and organizations. As the number of software continues to grow and new features added to the new software the selection of the most suitable software package is becoming more and more difficult. Wrong or immature decision would result in great loss of time and money. Research is being carried out throughout the world to evaluate the software. Researchers have not reached to consensus to generalize such selection and evaluation criteria. Improper selection of software package can lead to be a costly affair and adversely affect the business.
---
paper_title: Selection Criteria for Data Mining Software: A Study

paper_content:

Nowadays software are flooded in the market, there is a need for selection criteria of software package that should be available for individuals and organizations. As the number of software continues to grow and new features added to the new software the selection of the most suitable software package is becoming more and more difficult. Wrong or immature decision would result in great loss of time and money. Research is being carried out throughout the world to evaluate the software. Researchers have not reached to consensus to generalize such selection and evaluation criteria. Improper selection of software package can lead to be a costly affair and adversely affect the business.
---
paper_title: Cloud services evaluation framework

paper_content:

This paper intends to propose an easy and quick methodology to compare and choose the cloud service that best fit the small size organization needs. Approach strategies and methodologies were studied and chosen the most relevant to meet objective. Cloud computing characteristics, definitions, organization types, classifications and important taxonomy were chosen in the literature to give basic understanding it. In this work is presented one practical application to illustrate how to use it. Using this methodology, small size organizations can optimise their resources and at the same time be confidante to select the best cloud service option.
---
paper_title: Cloud services evaluation framework

paper_content:

This paper intends to propose an easy and quick methodology to compare and choose the cloud service that best fit the small size organization needs. Approach strategies and methodologies were studied and chosen the most relevant to meet objective. Cloud computing characteristics, definitions, organization types, classifications and important taxonomy were chosen in the literature to give basic understanding it. In this work is presented one practical application to illustrate how to use it. Using this methodology, small size organizations can optimise their resources and at the same time be confidante to select the best cloud service option.
---

Title: Cloud Computing in Data Mining – A Survey

Section 1: Introduction
Description 1: Introduce the relevance and intersection of cloud computing and data mining, and state the aims of the paper.

Section 2: Cloud Computing
Description 2: Define cloud computing, describe its essential characteristics, service models, and deployment models.

Section 3: Data Mining
Description 3: Define data mining, outline the process of knowledge discovery in databases (KDD), and describe the criteria for evaluating data mining tools.

Section 4: Reasons for Using Cloud Computing Solutions in DM
Description 4: List and elaborate on the reasons for moving to cloud computing for data mining from both client and organizational perspectives.

Section 5: Categorisation and Evaluation of Cloud Computing Solutions in DM
Description 5: Describe the methods and criteria used for categorizing and evaluating data mining solutions in cloud computing.

Section 6: Methodology
Description 6: Explain the methodology used for evaluating cloud-based data mining solutions, including criteria and evaluation process.

Section 7: Solutions for Processing Traditional Data
Description 7: List and evaluate solutions aimed at processing traditional data, including reasons for exclusion of certain solutions.

Section 8: Solutions for Processing Big Data (Storage and Performance)
Description 8: List and evaluate solutions aimed at processing big data, including reasons for exclusion of certain solutions.

Section 9: Conclusions and Future Work
Description 9: Summarize findings, highlight the best solutions for different data processing needs, and suggest areas for future research and improvement.