Introducing Linked Data - UWaterloo Library
... Heath, Tom and Christian Bizer (2011) Linked Data: Evolving the Web into a Global Data Space. 1st ed. Morgan & Claypool, 2011. (Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1) http://linkeddatabook.com/editions/1.0/ (open access) ...
... Heath, Tom and Christian Bizer (2011) Linked Data: Evolving the Web into a Global Data Space. 1st ed. Morgan & Claypool, 2011. (Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1) http://linkeddatabook.com/editions/1.0/ (open access) ...
Data Modeling and Erwin
... A. Data Modeling overview 1. What is a Data Model? • Data modeling is the process of describing information structures and capturing business rules in order to specify information system requirements. • A conceptual representation of data structures (tables) required for a database • A graphical re ...
... A. Data Modeling overview 1. What is a Data Model? • Data modeling is the process of describing information structures and capturing business rules in order to specify information system requirements. • A conceptual representation of data structures (tables) required for a database • A graphical re ...
Data and Knowledge Management
... • Present data in different perspectives • Involve complex calculations between data elements • Able to respond quickly to user requests ...
... • Present data in different perspectives • Involve complex calculations between data elements • Able to respond quickly to user requests ...
Database
... Centralised Data Management May be appropriate to have a single company wide data standard. – However there can also be problems with this. ...
... Centralised Data Management May be appropriate to have a single company wide data standard. – However there can also be problems with this. ...
Η παρουσίαση στα Αγγλικά.
... 3. When someone looks up a URI, provide useful information. 4. Include links to other URIs so that they can discover more things. ...
... 3. When someone looks up a URI, provide useful information. 4. Include links to other URIs so that they can discover more things. ...
Data Warehousing, Multi-Dimensional Data Models and OLAP
... The data in a data warehouse is multidimensional in nature. Though this data could be modeled in traditional ways such as ER modeling or relational modeling, it is more intuitive to think of it in terms of dimensions and facts. Facts represent the entity being measured and are a function of the dime ...
... The data in a data warehouse is multidimensional in nature. Though this data could be modeled in traditional ways such as ER modeling or relational modeling, it is more intuitive to think of it in terms of dimensions and facts. Facts represent the entity being measured and are a function of the dime ...
Creating Stovepipes: Standards and Data Collection Issues
... now available: GPS Digital videolog Multi-purpose vehicles Had-held and voice activated computers Others … ...
... now available: GPS Digital videolog Multi-purpose vehicles Had-held and voice activated computers Others … ...
R3B p7 - CenSSIS
... image and sensor data. The geographical separation between and the diverse disciplines of CenSSIS members make collaboration a particular challenge. In addition, scientific disciplines such as biology and the earth sciences have recently been generating data at enormous rates, making it difficult fo ...
... image and sensor data. The geographical separation between and the diverse disciplines of CenSSIS members make collaboration a particular challenge. In addition, scientific disciplines such as biology and the earth sciences have recently been generating data at enormous rates, making it difficult fo ...
Chapter 2
... – Refers to immunity of conceptual schema to changes in the internal schema. – Internal schema changes (e.g. using different file organizations, storage structures/devices). – Should not require change to conceptual or external schemas. ...
... – Refers to immunity of conceptual schema to changes in the internal schema. – Internal schema changes (e.g. using different file organizations, storage structures/devices). – Should not require change to conceptual or external schemas. ...
MULTI-LAYERED FRAMEWORK FOR DISTRIBUTED DATA MINING
... In the DCI and the DMA communication protocols a client will create a connection, send a request, receive a response and close the connection. A client will send only one request in a single threaded connection. The response for a request is a line with a message indicating the outcome of the reques ...
... In the DCI and the DMA communication protocols a client will create a connection, send a request, receive a response and close the connection. A client will send only one request in a single threaded connection. The response for a request is a line with a message indicating the outcome of the reques ...
distributed_db_arch_replication
... Transparency hides details at lower levels (often implementation ones) from user Four main types: ...
... Transparency hides details at lower levels (often implementation ones) from user Four main types: ...
The rational interrelationships within databases base on tables
... Queries: Sorts, filters, applies criteria to data, delete, modifies and manipulates set of datasets(see table 3 below) Forms: Interfaces with users(name, add, etc) Reports: Collections of lists of information from the table or query in a standardized page layout to be emailed, exported or prin ...
... Queries: Sorts, filters, applies criteria to data, delete, modifies and manipulates set of datasets(see table 3 below) Forms: Interfaces with users(name, add, etc) Reports: Collections of lists of information from the table or query in a standardized page layout to be emailed, exported or prin ...
emc data computing appliance
... Exploding data volumes, new data types, and ever-growing competitive challenges have led to radical changes in analytical technologies and a new approach to exploiting data. Decades-old legacy architectures for data management have reached scale limitations that make them unfit for processing big da ...
... Exploding data volumes, new data types, and ever-growing competitive challenges have led to radical changes in analytical technologies and a new approach to exploiting data. Decades-old legacy architectures for data management have reached scale limitations that make them unfit for processing big da ...
Main Responsibilities
... opportunities for simplification and automation, reducing the need for manual intervention as much as possible. 3. To ensure compliance with all relevant laws, e.g. correct capture of Gift Aid Declarations, true & proper capture of Data Protection preferences. 4. To work with key suppliers to ensure ...
... opportunities for simplification and automation, reducing the need for manual intervention as much as possible. 3. To ensure compliance with all relevant laws, e.g. correct capture of Gift Aid Declarations, true & proper capture of Data Protection preferences. 4. To work with key suppliers to ensure ...
Visualization and descriptive analytics of wellness data through Big
... media and visualization from the server and display to a user. It gets the signal fusion from heterogeneous sensors [2]. Kandogan ET all, created a feature ranking and annotation method. They do annotation interaction to help support understanding of the structure of data [3]. The new emerging direc ...
... media and visualization from the server and display to a user. It gets the signal fusion from heterogeneous sensors [2]. Kandogan ET all, created a feature ranking and annotation method. They do annotation interaction to help support understanding of the structure of data [3]. The new emerging direc ...
Description of personal data file for MoveOn application system
... unauthorised access, accidental or unlawful destruction, manipulation, disclosure, transfer or other unlawful processing. In each unit, employees shall have access only to those data on the applicants that are required to carry out their work. Data on exchange student selection and study rights shal ...
... unauthorised access, accidental or unlawful destruction, manipulation, disclosure, transfer or other unlawful processing. In each unit, employees shall have access only to those data on the applicants that are required to carry out their work. Data on exchange student selection and study rights shal ...
Handout 1 - Computer Information Systems
... Operational (aka transactional) system – a system that is used to run a business in real time, based on current data; also called a system of record Informational (analytical) system – a system designed to support decision making based on historical point-in-time and prediction data for complex quer ...
... Operational (aka transactional) system – a system that is used to run a business in real time, based on current data; also called a system of record Informational (analytical) system – a system designed to support decision making based on historical point-in-time and prediction data for complex quer ...
Topics
... When should one use an MD-database (multi-dimensional database) and not a relational one? What is a star schema? Why does one design this way? When should you use a STAR and when a SNOW-FLAKE schema? What is the difference between Oracle Express and Oracle Discoverer? How can Oracle Materialized Vie ...
... When should one use an MD-database (multi-dimensional database) and not a relational one? What is a star schema? Why does one design this way? When should you use a STAR and when a SNOW-FLAKE schema? What is the difference between Oracle Express and Oracle Discoverer? How can Oracle Materialized Vie ...
2. Data (horizontal) - NDSU Computer Science
... At the highest level, is the decision as to whether a data set should be structured as horizontal or vertical data (or some combination). Another important task to be addressed in data systems work today is RESIDUALIZATION OF DATA MUCH WELL-STRUCTURED DATA IS DISCARDED PREMATURELY Databases are abou ...
... At the highest level, is the decision as to whether a data set should be structured as horizontal or vertical data (or some combination). Another important task to be addressed in data systems work today is RESIDUALIZATION OF DATA MUCH WELL-STRUCTURED DATA IS DISCARDED PREMATURELY Databases are abou ...
Data Flow and SDLC
... improve software quality and responsiveness to changing customer requirements. ...
... improve software quality and responsiveness to changing customer requirements. ...
Predictive Analytics of Cluster Using Associative Techniques Tool
... extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of c ...
... extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of c ...
Final Project
... Linked data can be used to resolve authority control issues; rather than searching for an authority record and copying it into original cataloging, catalogers can link directly to that authority record’s URI. This has far-reaching benefits for both eliminating errors and for greatly increasing effic ...
... Linked data can be used to resolve authority control issues; rather than searching for an authority record and copying it into original cataloging, catalogers can link directly to that authority record’s URI. This has far-reaching benefits for both eliminating errors and for greatly increasing effic ...
Organizational Intelligence
... Data managers now need to support organizational intelligence technologies ...
... Data managers now need to support organizational intelligence technologies ...
Big data
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.Analysis of data sets can find new correlations, to ""spot business trends, prevent diseases, combat crime and so on."" Scientists, business executives, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research.Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data were created; The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.Work with big data is necessarily uncommon; most analysis is of ""PC size"" data, on a desktop PC or notebook that can handle the available data set.Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires ""massively parallel software running on tens, hundreds, or even thousands of servers"". What is considered ""big data"" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered ""big"" one year becomes ordinary later. ""For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.""