DATA SECURITY IN LOCAL NETWORK USING DISTRIBUTED FIREWALLS ABSTRACT Computers and Networking have become inseparable by now. A number of confidential transactions occur every second and today computers are used mostly for transmission rather than processing of data. So Network Security is needed to prevent hacking of data and to provide authenticated data transfer. Network Security
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DEFINITION: Quantitative methods are research techniques that are used to gather quantitative data — information dealing with numbers and anything that is measurable e.g. Statistics‚ tables and graphs‚ are often used to present the results of these methods. Quantitative research methods were originally developed in the natural sciences to study natural phenomena. However examples of quantitative methods now well accepted in the social sciences and education. Differences between parametric and
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Programme Management Office Project Charter & Scope Statement Project Title: Project ID: Project Sponsor: Project Manager: Charter approval date: Project and Module Data Project Brian Norton‚ President Liam Duffy‚ IS Services Document Control Date 30-01-12 02-02-12 10-02-12 16-03-12 Version V 1.0 V 2.0 V 3.0 V 4.0 Changed by Liam Duffy Liam Duffy Liam Duffy Liam Duffy Reasons for Change Original Document Consultation with Sponsor Consultation with Project
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This article talks about the importance of using Big Data which companies are easily able to collect from their businesses‚ customers and employees. It explains the numerous advantages of using the data collected by companies effectively so that it can be used by the company in improving its efficiencies‚ sales‚ faster and quicker turnaround which in turn would lead to increase revenues and finally increased profits (which is what the stakeholders of the company are looking for).It illustrates the
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you an understanding of how data resources are managed in information systems by analyzing the managerial implications of basic concept and applications of database management. Introduce the concept of data resource management and stresses the advantages of the database management approach. It also stresses the role of database management system software and the database administration function. Finally‚ it outlines several major managerial considerations of data resource management.
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Data Mining On Medical Domain Smita Malik‚ Karishma Naik‚ Archa Ghodge‚ Shivani Gaunker Shree Rayeshwar Institute of Engineering & Information Technology Shiroda‚ Goa‚ India. Smilemalik777@gmail.com; naikkarishma39@gmail.com; archaghodge@gmail.com; shivanigaunker@gmail.com Abstract-The successful application of data mining in highly visible fields like retail‚ marketing & e-business have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries
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Analyzing Data Using Pivot Tables – An Example Remember assignment 5 where you were asked to compare Invoice amounts to Sales Order amounts? You had to create a query to join together 2 tables from an Access database. If the results of that query had been downloaded into an Excel file (a simple thing to do)‚ you could have used the Excel file and a Pivot table to help in the analysis. Before you try to follow this example‚ you should learn as much as you can about Pivot Tables from Microsoft’s
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1 Secondary data analysis: an introduction All data are the consequence of one person asking questions of someone else. (Jacob 1984: 43) This chapter introduces the field of secondary data analysis. It begins by considering what it is that we mean by secondary data analysis‚ before describing the type of data that might lend itself to secondary analysis and the ways in which the approach has developed as a research tool in social and educational research. The second part of the chapter considers
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Chapter 1 Exercises 1. What is data mining? In your answer‚ address the following: Data mining refers to the process or method that extracts or \mines" interesting knowledge or patterns from large amounts of data. (a) Is it another hype? Data mining is not another hype. Instead‚ the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus‚ data mining can be viewed as the result of
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