PRINCIPLES OF DATA QUALITY Arthur D. Chapman1 Although most data gathering disciples treat error as an embarrassing issue to be expunged‚ the error inherent in [spatial] data deserves closer attention and public understanding …because error provides a critical component in judging fitness for use. (Chrisman 1991). Australian Biodiversity Information Services PO Box 7491‚ Toowoomba South‚ Qld‚ Australia email: papers.digit@gbif.org 1 © 2005‚ Global Biodiversity Information Facility Material
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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 INTEGRATION Data integration involves combining data residing in different sources and providing users with a unified view of these data. This process becomes significant in a variety of situations‚ which include both commercial (when two similar companies need to merge their databases and scientific (combining research results from different bioinformatics repositories‚ for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes
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Lecture Notes 1 Data Modeling ADBMS Lecture Notes 1: Prepared by Engr. Cherryl D. Cordova‚ MSIT 1 • Database: A collection of related data. • Data: Known facts that can be recorded and have an implicit meaning. – An integrated collection of more-or-less permanent data. • Mini-world: Some part of the real world about which data is stored in a database. For example‚ student grades and transcripts at a university. • Database Management System (DBMS): A software package/ system to facilitate
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different types of communication between electrical devices. Distortion‚ noise‚ and cross talk on a cabling medium are factors that prevent the accuracy of transmitted data to be intact. For these reasons different encoding methods exist. An example is when 2 wires are used to transmit music data to a speaker Digital signals don’t always have to be carried over to the receiving end by electricity‚ light can also be used for digital communication. Fibre Optics use light to transmit data through
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A glimpse of Big Data Jan. 2013 What is big data? “Big data is not a precise term; rather it’s a characterization of the never ending accumulation of all kinds of data‚ most of it unstructured. It describes data sets that are growing exponentially and that are too large‚ too raw or too unstructured for analysis using relational database techniques. Whether terabytes or petabytes‚ the precise amount is less the issue than where the data ends up and how it is used.”------Cite from EMC’s report
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Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology‚ if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining
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Data Anomalies Normalization is the process of splitting relations into well-structured relations that allow users to inset‚ delete‚ and update tuples without introducing database inconsistencies. Without normalization many problems can occur when trying to load an integrated conceptual model into the DBMS. These problems arise from relations that are generated directly from user views are called anomalies. There are three types of anomalies: update‚ deletion and insertion anomalies. An update anomaly
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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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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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