Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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DATA | INFORMATION | 123424331911 | Your winning lottery ticket number | 140593 | Your date of birth | Aaabbbccd | The grades you want in your GCSEs | Data and information Data‚ information & knowledge Data Data consist of raw facts and figures - it does not have any meaning until it is processed and turned into something useful. It comes in many forms‚ the main ones being letters‚ numbers‚ images‚ symbols and sound. It is essential that data is available because it is the first
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Big Data‚ Data Mining and Business Intelligence Techniques 2 What is Data? • Data is information in a form suitable for use with a computer. • There are two types of data ▫ Structured ▫ Unstructured • The total volume of data is growing 59% every year. • The number of files grow at 88% every year. 3 What is Big Data? Exa Analytics on Big Data at Rest Up to 10‚000 Times larger Peta Data Scale Giga Data at Rest Tera Data Scale Mega Traditional Data Warehouse
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billion bytes of data in digital form be it on social media‚ blogs‚ purchase transaction record‚ purchasing pattern of middle class families‚ amount of waste generated in a city‚ no. of road accidents on a particular highways‚ data generated by meteorological department etc. This huge size of data generated is known as big data. Generally managers use data to arrive at decision. Marketers use data analytics to determine customer preferences and their purchasing pattern. Big data has tremendous potential
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Data Mining Assignment 4 Shauna N. Hines Dr. Progress Mtshali Info Syst Decision-Making December 7‚ 2012 Benefits of Data Mining Data mining is defined as “a process that uses statistical‚ mathematical‚ artificial intelligence‚ and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases‚ including data warehouses” (Turban & Volonino‚ 2011). The information identified using data mining includes patterns indicating trends‚ correlations
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Activity 1 Reasons why organisations need to collect HR Data. It is important for organisations to collect and retain HR data as this will be key for strategic and HR planning. It will also help to have all the information necessary to make informed decisions‚ for the formulation and implementation of employment policies and procedures‚ to monitor fair and consistent treatment of staff‚ to contribute to National Statistics and to comply with statutory requirements. The key organisational
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DATA COMMUNICATION (Basics of data communication‚ OSI layers.) K.K.DHUPAR SDE (NP-II) ALTTC ALTTC/NP/KKD/Data Communication 1 Data Communications History • 1838: Samuel Morse & Alfred Veil Invent Morse Code Telegraph System • 1876: Alexander Graham Bell invented Telephone • 1910:Howard Krum developed Start/Stop Synchronisation ALTTC/NP/KKD/Data Communication 2 History of Computing • 1930: Development of ASCII Transmission Code • 1945: Allied Governments develop the First Large Computer
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Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business ’s product or also in winning additional customers that may be purchasing from the competitor. Generally‚ data are any “facts‚ numbers‚ or text that can be processed by a computer.” Today
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Data warehousing is the process of collecting data in raw form for analyzing trends. The benefits to data warehousing are improved end-user access‚ increased data consistency‚ various kinds of reports can be made from the data collected‚ gather the data in a common place from separate sources and additional documentation of data. Potential lower computing costs‚ increased productivity‚ end-users can query the database without using overhead of the operational systems and creates an infrastructure
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Turnage‚ Bonebright‚ Buhman‚ Flowers (1996) showed that untrained participants can listen to shapes. That is‚ they used data sonification – musical representation of two dimensional space‚ with pitch as the vertical dimension and time as the horizontal dimension – to present participants the visual and auditory representation of waveforms. In two conditions‚ they showed the participants could match one visual presentation to one of two auditory representations‚ or match one auditory presentation
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