...diagram, the following: Primary Data Warehouse and Data Mart. In this connection, explain the difference between ROLAP and MOLAP. A Primary Data Warehouse is a central repository of a database of a complete organization. It holds multiple subject areas and very detailed information. A Data Mart is a subset or an aggregation of the data stored to a primary data warehouse. It often holds only one subject area – for example, a specific department, finance or sales. It may hold more summaried data, and is typically smaller than a warehouse because of its employment on a different grain. Figure 1.1 illustrates the difference between data mart and a primary data warehouse. Since the data mart typically holds one subject area, it is much smaller than a primary data warehouse. These data marts can be viewed as small, local data warehouses replicating the part of primary data warehouse which is required by a specific domain or department. Data Warehouse Data Mart Data Warehouse Data Mart Figure 1.1 A data warehouse does not necessarily use a dimensional model, since it is partly normalized RDBMS, but data marts are multidimensional cubes. This connection gives arise to two concepts, ROLAP and MOLAP. ROLAP is an implementation based on a relational database, in our case which is a primary data warehouse, and MOLAP is an implementation based on a multidimensional database which are data marts. ROLAP tools use the relational database to access the data and generate SQL queries to calculate...
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...Introduction to Data Mining 3 2. Characteristics and Objectives of Data Mining 3 3. Data type in Data Mining 3 4. Patterns of Data Mining 4 5. Applications of Data Mining 5 6. Data Mining Process Models 6 7. Classification of Techniques 7 8. Common Data Mining Mistakes 8 9. Data Mining softwares 8 10. References 8 Data Mining for Business Intelligence Introduction: Business Intelligence (BI)is defined as the set of techniques and tools that transform the raw data into meaningful and useful information for business analysis. The main goal of business analyses is to...
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...It is easy to create lots of data and then become disorganised very quickly. There are many different ways as to how data can be organised. You need to consider your other work colleagues who may need to find specific files at a later date, therefore it is important to decide on an effective way that works best for you and your team. To ensure your data is understood, you should try these different ways of organising data that are listed below: Name and organise your files. This allows you and others to have the ability to easily locate them. Organise e-mails. You should try to have different folders for specific projects and so on, by creating individual folders, this again allows easy access when it comes to finding the files and it shows you have capable organisational skills and helps to maintain the department in some ways. ...
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... Companies then can utilize the data internally in the light of customer analyzing, marketing strategies developing or even business forecasting. Let’s consider an insurance company as an example. If a father wants to buy a juvenile saving insurance plan for his child, he needs to provide his personal information to the insurance company, such as name, gender, age, contact number, income and health status etc. This facilitates the process of premium calculating and policy approving by the underwriter. On the other hand, data is collected during this buying process. As insurance company had grasped information from many customers, it is easily to carry out data analysis. Clients are classified into different segments according to their total amount of annualized premium, age, marital status, product type and so on. Analysts can interpret the current situation of the company from the summary of the data. For instance, what products sold the most last quarter? Which age group of people is our target customers? What is the buying behavior of a specific group of people? Use the above example again, if the company wants to enhance the father’s total product experience and increase customer retention, companies can perform data analysis to find out which product can be recommended to him. Besides, various marketing campaigns can be worked together in order to increase the loyalty and satisfaction of customers. Apart from the description of data,...
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...3: Business Intelligence and Data Warehouses Kevin Sloley Professor Dammlash Gebre CIS-111 Intro to Related Database Management Systems Due March, 2016 Outline the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data. The primary difference between your application database and a data warehouse is that while the former is designed (and optimized) to record, the latter has to be designed (and optimized) to respond to analysis questions that are critical for your business. Application databases are OLTP (On-Line Transaction Processing) systems where every transaction has to be recorded, and super-fast at that. Consider the scenario where a bank ATM has disbursed cash to a customer but was unable to record this event in the bank records. If this started happening frequently, the bank wouldn't stay in business for too long. So the banking system is designed to make sure that every trasaction gets recorded within the time you stand before the ATM machine. This system is write-optimized, and you shouldn't crib if your analysis query (read operation) takes a lot of time on such a system. A Data Warehouse (DW) on the other end, is a database (yes, you are right, it's a database) that is designed for facilitating querying and analysis. Often designed as OLAP (On-Line Analytical Processing) systems, these databases contain read-only data that...
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...August 29, 2015 Business Intelligence and Data Warehouses Student’s name: Professor’s name: Course title: 1. Differences between the structures of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data Data Warehouse is a database which is designed to process for query and analysis rather than for transaction processing, and it is usually contains historical data derived from transaction data, but can include data from other sources while relational database optimized for online transaction which includes insertions, updates and deletion. Basically Data Warehouse is defined as a subject-oriented, non-volatile and time –variant collection of database which support management’s decisions. Data Warehouse is very distinct from online transaction systems. Some of distinctions are given below: * One of main difference, a data warehouse you can do separate analysis workload form transaction workload which makes it very much read-oriented systems. * They deal higher amount of volume in comparisons to online transaction database. * They have a far higher amount of data reading versus writing and updating. This enables far better analytical performance and avoids impacting your transaction systems. * A data warehouse system can be optimized to consolidate data from many sources to achieve a key goal. * it prevents many disputes and enhances decision-making efficiency ...
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...Business Analytics using Secured Cloud Storage System M Swetha Chandra1, M Suma Latha2, KODAVATIKANTI S M Aditya Kumar3, S K R Swamy4 1,2,3,4 Department of CSE, TRR College of Engineering, Inole, Patancheru, Hyderabad, AP, India 1 sweet.smily99@gmail.com 2 msumalathacse@gmail.com 3 smaditya@gmail.com 4 kramas2004@yahoo.com | | | ABSTRACT Business analytics go far beyond reports, dashboards, and scorecards. Analytic impact occurs after the numbers are delivered, and analytic value is driven by the kinds of questions that are answered. Ordinary analytics tell you what has already happened. Good analytics provide insight into why things happen, and great analytics provide foresight to see what lies ahead. Today’s business climate demands extraordinary analytics. Business managers need to know more than what. The hard questions today are why, what if, and what next. According to Gartner, BI and Analytics is a $12.2 billion market with 16.4% growth in 2011. Gartner's 2012 CIO survey showed that analytics/BI is the No. 1 technology priority for CIOs. The mega vendors such as Oracle, SAS, IBM etc., are already having major portion of the revenue with their packaged applications in these areas. It is estimated by Gartner that Analytics will be touching 75% of potential users by 2020. This is proven by the growth rate of new vendors such as QlickTech and Tableau by 45% (as per Gartner report). Cloud Storage: Cloud Storage, also referred as Data Storage as a Service, is...
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... What business and social problems does data center power consumption cause? Data center power consumption economically affects businesses and environmentally affects society. Operating costs for data centers is very expensive. In the article, "Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments," the total operating costs, concerning electricity, of all data centers within the U.S. alone exceeded 7 billion dollars in 2010 (Ayala, J., Moya, J., Risco-Martín, J., Sanchez, C., Zapater, M. 2012). The article then explains that data centers consumed 61 billion kilowatt-hours in 2006;the Environmental Protection Agency provided this statistic to the US Congress in a report from 2007 (Ayala, J., Moya, J., Risco-Martín, J., Sanchez, C., Zapater, M. 2012). With this amount of energy being consumed by data centers, it is a cause for concern; consequently, data centers have an impact on the cost of business and negatively impact the environment via carbon footprint. As the carbon footprint grows, there is a need to realign the way businesses looks at managing their data centers. Several companies including Cisco, Dell, Google, HP, IBM, and Intel have announced efforts to reduce the environmental footprint of their product offerings (Chang et al., 2012). Large technology companies are starting to understand that being environmentally friendly is good for the wallet and good public relations. Understanding how to manage and build better data centers...
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...The Importance of Data in Business The Importance of Data in Business Information is a subset of data, including data that possess context, relevance, and purpose (Sabherwal, & Becerra-Fernandez, 2011, p.5). Information more than likely involves the manipulation of raw data to obtain a more well-rounded indication of patterns or trends in the data itself. Humans are social animals that depend on interaction with others for daily needs, this interaction is possible through a mutual data network between them (Uddin, 2010). As with every advance in communication technology, the creation and interconnection of robust data networks are having a profound effect. Early data networks were limited to exchanging character based information between connected computer systems. Today’s current data networks have evolved right in front of our eye’s in to something spectacular, now we can carry voice, video streams, text, music and photos over many different devices (Uddin, 2010). In 1990 the use of the internet to gather data spread faster than anyone ever imagined, it once was used for a way for researchers at university’s to exchange information, but when people and businesses figured out how to take advantage of this web based communication the internet grew to limits we never thought imaginable. This sparked the creation of a new age with many new businesses and careers. Data exchange over the internet can and has also saved lives. In the year of 2008 a mother saved her sons...
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...The Importance of Data in Business The Importance of Data in Business Information is a subset of data, including data that possess context, relevance, and purpose (Sabherwal, & Becerra-Fernandez, 2011, p.5). Information more than likely involves the manipulation of raw data to obtain a more well-rounded indication of patterns or trends in the data itself. Humans are social animals that depend on interaction with others for daily needs, this interaction is possible through a mutual data network between them (Uddin, 2010). As with every advance in communication technology, the creation and interconnection of robust data networks are having a profound effect. Early data networks were limited to exchanging character based information between connected computer systems. Today’s current data networks have evolved right in front of our eye’s in to something spectacular, now we can carry voice, video streams, text, music and photos over many different devices (Uddin, 2010). In 1990 the use of the internet to gather data spread faster than anyone ever imagined, it once was used for a way for researchers at university’s to exchange information, but when people and businesses figured out how to take advantage of this web based communication the internet grew to limits we never thought imaginable. This sparked the creation of a new age with many new businesses and careers. Data exchange over the internet can and has also saved lives. In the year of 2008 a mother saved her sons...
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...MIS 6309, Prof. A. Lahiri Team Homework #2 ------------------------------------------------- 1. The answers are as follows: a) E-R diagram: M Location Positions Industry Has Located in Belongs to M 1 M Employer 1 1 M Location Positions Industry Has Located in Belongs to M 1 M Employer 1 1 b) Physical Layout (primary keys underlined, foreign keys double-underlined): Position PositionID int PositionTitle varchar(50) EmployerID varchar(5) Wage Money HoursPerWeek int Experience bit Openings int Employer EmployerID varchar(5) EmployerName varchar(50) StreetAddress varchar(50) City varchar(50) StateProv varchar(2) PostalCode varchar(7) Country varchar(50) ContactFirstName varchar(50) ContactLastName varchar(50) Position varchar(50) Phone varchar(10) Website bit NAICSCode varchar(6) Comments varchar(255), must allow NULL NAICS NAICSCode varchar(6) NAICSDesc varchar(50) Location Abv varchar(2) Location varchar(50) c) Create table statements: CREATE TABLE Location( Abv varchar(2) PRIMARY KEY NOT NULL, Location varchar(50) NOT NULL ); GO CREATE TABLE NAICS( NAICSCode varchar(6) PRIMARY KEY NOT NULL, NAICSDesc varchar(50) NOT NULL ); GO CREATE TABLE Employer( EmployerID varchar(5) PRIMARY KEY NOT NULL, EmployerName varchar(50) NULL, StreetAddress varchar(50) NULL...
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...Give an interval based on your data so that you are 95% confident that the true value of the unknown proportion lies inside it. How would you explain 95% confidence to a layman? Suppose a professor of IIMA thinks that true proportion is 0.3. Are you ready to accept the professor’s perception based on your data at 99% confidence level? Solution – 1 Sample Size n = 100 (male smokers) p = 0.2 Sd (P) = √(pq / n) = .04 95% confidence interval of p = 0.2 ± 2 x 0.04 = 0.08 to 0.32 Explanation to a layman – 95% confidence means that if the sampling experiment i.e. selection of random samples of 100 male smokers in the present problem, is repeated large no of times, 95% of the times the interval will include the true value of p (0.2) or the sample proportion of smokers in present example and 5% of the times the interval may not include the true value of p (0.2) or the sample proportion of smokers in present example. 99 % confidence interval of p = 0.2 ± 2.58 x .04 = .04 to 0.35 Based on our data, as the true proportion of 0.3 thought by the IIMA professor lies in the 99% confidence interval, we can accept IIMA Professor’s perception. 2. A week before presidential election in USA, suppose a news agency wants to predict who is going to win (Democrat or Republican)? In order to predict, they collected a random sample of size 10000 individuals nationally and found 5100 of them intend to vote for Democrat. Based on the data could you claim with 95% confidence...
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...Building An Analytical Roadmap : A Real Life Example Dr Ahmed Khamassi Chief Data Scientist & Principal Consultant 1 © 2014 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL The Issue Environment: Outcomes Big data analytics is probably going to be remembered as a technological, if not, an industrial revolution Paralysis by analysis New technologies are rolling off the assembly line daily They keep revisiting the same issues over and over again New terminologies and approaches The delve into technological questions before answering the what and why questions. What matters seems to changes quite frequently I hear stories from my competitors, am I behind? Do I need this stuff? How do I know which are the new opportunities these technologies allow me to win? Skills are short Which skills do we need anyway? How do we organise them? How do we ensure we are compliant? 2 Many customers do not know where to start? Many organise several ‘vendor’ contests without a clear end insight They lack coherent approach that leads to faster results They involve either too many or too few stakeholders Where do I start and how do I plan for big data analytics? © 2014 WIPRO LTD | WWW.WIPRO.COM | CONFIDENTIAL Establishing An Analytical Capability Business Layer Principles: Analytics is a business outcome enabler What needs to be optimised, prioritisation, alignment with overall strategy, process changes etc. It bridges commercial management and IT expertise Analytical Layer There are four...
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...Outline Database 2 Data Protection for Business Continuity Introduction Motivation Recovery Objective Data Protection Techniques Classes of Data Mapping of Company Size, Classes of Data, and Techniques Denny (denny@cs.ui.ac.id) International Bachelor Program Faculty of Computer Science 2004/2005 Version 1.0 - Internal Use Only DB2/DP/DN/V1.0/2 Introduction Why do we need data protection? SEPTEMBER 11, 2001 = 100 MEGABYTES OF DATA MORE THAN US$ 1 MILLION DATA PROTECTION DB2/DP/DN/V1.0/3 DB2/DP/DN/V1.0/4 1 Why do we need data protection? Causes of unplanned outages (Disaster Recovery Journal, 2001) Why Do We Need High Data Availability? CAN COST 1 HOUR OF DOWNTIME US$ 6.5 MILLION DB2/DP/DN/V1.0/5 DB2/DP/DN/V1.0/6 Why Do We Need High Data Availability? Data Protection and Business Continuity So, in this topic, we will see: techniques to protect data and ensure business continuity when disaster occurs. GLOBALISATION DB2/DP/DN/V1.0/7 DB2/DP/DN/V1.0/8 2 Recovery Objective LAST BACKUP DISASTER OCCURRED SYSTEM BACK TO OPERATION Data Protection Techniques Overview 1. TIME DATA LOSS RECOVERY POINT OBJECTIVE (RPO) RECOVERY TIME OBJECTIVE (RTO) 2. 3. 4. 5. 6. Vaulting Physical: backup to tape Electronic: backup over the Internet Server fortification RAID: same copies, or split into several disks Dual power supplies Network cluster NAS: independent disks connected directly to network SAN: a network...
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...Business Statistics Group Project Restaurants Serving Times Red Lobster | 7 minutes | 16 minutes | 18 minutes | 21 minutes | 15 minutes | 14 minutes | Olive Garden | 12 minutes | 16 minutes | 14 minutes | 18 minutes | 15 minutes | 12 minutes | Logan’s Steakhouse | 9 minutes | 11 minutes | 10 minutes | 8 minutes | 13 minutes | 13 minutes | Ruth Chris Steakhouse | 16 minutes | 14 minutes | 20 minutes | 19 minutes | 10 minutes | 15 minutes | Foosackly’s | 2 minutes | 3 minutes | 1 min 45 sec | 2 minutes | 2 mins 30 secs | 2 minutes | Five Guys | 6 minutes | 4 minutes | 11 minutes | 9 minutes | 6 minutes | 4 minutes | Our group decided to research food service response times for our group project. We timed customers at 6 different restaurants: From the time they ordered their food till it was delivered. We also took note of the appearance of the restaurant, employee attitude and over-all cleanliness. Red Lobster and Olive Garden were chosen because of their similar styles of service, casual dining restaurants. Logan’s and Ruth Chris were chosen because they’re both steak-houses. Foosackly’s and Five Guys were chosen because of similar service styles, grab and go. Devon recorded the food service response times on Saturday March 24, 2012. His data was collected from 4:00 pm to 6:00 pm, at the Red Lobster on Airport Blvd in Mobile, Alabama. Devon noted that upon entering Red Lobster that a pleasant smell of butter biscuits was wafting in the lobby and he was greeted...
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