DataBig Data and Future of Data-Driven Innovation A. A. C. Sandaruwan Faculty of Information Technology University of Moratuwa chanakasan@gmail.com The section 2 of this paper discuss about real world examples of big data application areas. The section 3 introduces the conceptual aspects of Big Data. The section 4 discuss about future and innovations through big data. Abstract: The promise of data-driven decision-making is now being recognized broadly‚ and there is growing enthusiasm
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What is Data Communications? Next Topic | TOC The distance over which data moves within a computer may vary from a few thousandths of an inch‚ as is the case within a single IC chip‚ to as much as several feet along the backplane of the main circuit board. Over such small distances‚ digital data may be transmitted as direct‚ two-level electrical signals over simple copper conductors. Except for the fastest computers‚ circuit designers are not very concerned about the shape of the conductor or
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and Kimball’s definition of Data Warehousing. Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store‚ individual departmental databases are developed to serve most decision support needs. Ralph Kimball‚ on the other hand‚ suggests a bottom-up approach that uses dimensional modeling‚ a data modeling approach unique to data warehousing. Rather than building
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Research Project - Data Protection Data can be collected by organisations such as the websites we use daily‚ such as Facebook and Twitter. They have our information such as our age‚ date of birth‚ home address and other personal information which we would not share with strangers‚ and it is their job to protect that data‚ so that it doesn’t get into the wrong hands‚ such as scammers. Organisations may collect information from you in a number of ways‚ over the internet‚ over the phone‚ or also
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Data Processing During the collection of data‚ our group noted the effect that temperature change had on aquatic macro invertebrates. Our data was collected from three different ponds amongst the Lake Harriet/Lake Calhoun vicinity. We took samples from the bird sanctuary pond‚ Lake Calhoun holding pond and the Lake Harriet duck area. Prior to our procedure‚ we measured the temperatures of each pond area. We used the low-temperature climate (bird sanctuary pond) to compare to the higher-temperature
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WEEK-3 Data Abstraction Destructors • Destructors are functions without any type • The name of a destructor is the character ’~’ followed by class name – For example: ~clockType(); • A class can have only one destructor – The destructor has no parameters • Destructor automatically executes when the class object goes out of scope C++ Programming: Program Design Including Data Structures‚ Sixth Edition 2 Data Abstract‚ Classes‚ and Abstract Data Types • Abstraction – i
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Networks Volvo utilized data mining in an effort to discover the unknown valuable relationships in the data collected and to assist in making early predictive information. It created a network of sensors and CPUs that were embedded throughout the cars and from which data was captured. Data was also captured from customer relationship systems (CRM)‚ dealership systems‚ product development and design systems and from the production floors in their factories. The terabytes of data collected was streamed
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Process in Houston CIVT 301 Outline Wastewater collection data in Houston What is sewage treatment? Where does wastewater come from? Factors that affect the flow of pipelines Industrial wastewater? Storm water/ Data The treatment plant operator Sources of wastewater Why treat wastes Waste water treatment facilities Treatment processes Drinking water What can be done to help? Wastewater collection data in Houston 640 square miles area 3 million citizens served 6‚250 miles
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Services E20-007 Data Science and Big Data Analytics Exam Exam Description Overview This exam focuses on the practice of data analytics‚ the role of the Data Scientist‚ the main phases of the Data Analytics Lifecycle‚ analyzing and exploring data with R‚ statistics for model building and evaluation‚ the theory and methods of advanced analytics and statistical modeling‚ the technology and tools that can be used for advanced analytics‚ operationalizing an analytics project‚ and data visualization techniques
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Summary of data gathering There are more cars and trucks going through the intersection of Spring Street and Route 27 in the morning than the afternoon. How to determine cars and trucks Cars are usually used for taking passengers to the destination; in general‚ they are smaller than trucks. As for trucks‚ there are larger spaces to carry items‚ for example‚ gasoline‚ foods‚ and other goods. In addition‚ school buses are important transportations on campus‚ but they are counted as neither cars
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