_____40 cm________ Width ____35 cm_________ Area ________1400cm2_______ Volume ____11200cm3________ 4) Determine the mass of the objects listed below (in grams). Pay attention to the units. Since you do not have a metric scale‚ we will provide you data to work with. A) Baseball Mass (measurement 1): ____.145__kg Mass (measurement 2): ____145.05_ g Mass (measurement 3): 145‚750.77 mg Mass (average): __________g Convert: ___________kg B) Piece of fruit Mass (measurement 1): ____310____ g Mass
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Data Processing Data Processing is the term generally used to describe what was done by large mainframe computers from the late 1940’s until the early 1980’s (and which continues to be done in most large organizations to a greater or lesser extent even today): large volumes of raw transaction data fed into programs that update a master file‚ with fixed-format reports written to paper. Number System A numeral system (or system of numeration) is a writing system for expressing numbers‚ that is
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nodes which creates a dedicated channel with full bandwidth available for communication. b. Packet Switching – Groupings of transmitted data are converted into smaller packets that are sent over a network. The transmission resources are allocated as needed and a connection exists only as long as the transmission is sent. 2. Packetization is the bundling of data being sent into smaller packets in such a way that they can be sent‚ delivered‚ and Looking at the statistics of the two stocks
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Workstations are generally used by a single person whereas servers provide services over a network to multiple computers. Workstation are typically used for CPU tasks and simulation projects‚ unlike servers which are mainly used for data storage‚ to run databases and organize data‚ DNS‚ and to host web pages. 3. List some advantages of mapping a directory located on a server to a workstation. 4. Discuss how you would add new users and groups to your server. Include a discussion of the permissions
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Chapter 2 Developing a Sustainable Supply Chain Strategy Balkan Cetinkaya Learning Goals. By reading this chapter you will: l l l l l Know the basics of competitive strategy and supply chain strategy and understand their interrelations Understand the need for a sustainable supply chain strategy Understand the ingredients of a sustainable supply chain strategy Apply a generic‚ iterative approach to develop your sustainable supply chain strategy Apply a balanced scorecard to
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1. Data mart definition A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth‚ the information in data marts pertains to a single department. In some deployments‚ each department or business unit is considered the owner of its data
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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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am going to explain data protection‚ talk about the data protection acts‚ rights and principles. I’m going to talk about a data controller and a data processor and what their responsibilities are. What personal and sensitive data are. What a data receiver is and what his rights are‚ what is involved in direct marketing and I will mention an example of abuse or corruption that occurred in Ireland. Data protection acts Data protection is legal control over and access to use of data stored in computers
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an era of big data‚ this data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives; however‚ there are a number of challenges that must be addressed to allow us to exploit the full potential of big data. This paper focuses on challenges faced by online retailers when making use of big data. With the provided examples of online retailers Amazon and eBay‚ this paper addressed the key challenges of big data analytics including data capture and
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having too much data‚ and what to do about them There is rarely an instance of business that you can encounter that does not involve the processing of data on information systems these days. Businesses and organizations use information systems in a majority of their functions‚ and as a result‚ are creating mass amounts of data. Because data is so crucial to business operations‚ it is being gathered‚ stored‚ and utilized in exponential amounts compared to the previous decade. These data stores can
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