Knowledge management
... Knowledge Management System Cycle Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge ...
... Knowledge Management System Cycle Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge ...
COMP3017 Advanced Databases Data Types and Data Modelling
... spreadsheet or other source – Read using OCR techniques ...
... spreadsheet or other source – Read using OCR techniques ...
Semantic Web in the real world
... search engines perform very poorly • New focus is on this class of searches ...
... search engines perform very poorly • New focus is on this class of searches ...
Data Modeling and Erwin
... A. Logical Data Modeling 1. LDM is a more formal representation of the CMD. 2. Relational / dimensional theory is applied as per design decisions. 3. Normalization / de-normalization of data is taken care. 4. Like objects may be grouped into super and sub types. 5. Many-to-many relationships are re ...
... A. Logical Data Modeling 1. LDM is a more formal representation of the CMD. 2. Relational / dimensional theory is applied as per design decisions. 3. Normalization / de-normalization of data is taken care. 4. Like objects may be grouped into super and sub types. 5. Many-to-many relationships are re ...
WebFOCUS Hyperstage - Information Builders
... Usually requires a complete rebuild Cube builds are typically slow New design results in a new cube ...
... Usually requires a complete rebuild Cube builds are typically slow New design results in a new cube ...
CS3465 Business Intelligence and Data Warehousing 1 3/1/3
... CS 1070 Introduction to Information Systems CS 2188 Introduction to Programming CS 3260 Fundamentals of RDBMS MA 1001 Finite Mathematics ...
... CS 1070 Introduction to Information Systems CS 2188 Introduction to Programming CS 3260 Fundamentals of RDBMS MA 1001 Finite Mathematics ...
COURSE NAME:
... operational databases. For efficient query processing, only some of the possible summary views may be materialized. A virtual warehouse is easy to build but requires excess capacity on operational database servers. It is popular because is enables business to access & analyze data from operation ...
... operational databases. For efficient query processing, only some of the possible summary views may be materialized. A virtual warehouse is easy to build but requires excess capacity on operational database servers. It is popular because is enables business to access & analyze data from operation ...
SistemManajemenMutuKonstruksi
... Can link data elements from various tables Very supportive of ad hoc requests but slower at processing large amounts of data than hierarchical or network models ...
... Can link data elements from various tables Very supportive of ad hoc requests but slower at processing large amounts of data than hierarchical or network models ...
AQG Data Management Issues - Colorado State University
... General Tasks and Associated Questions Determine our goals: What are we trying to achieve? Design a strategy: How are we going to achieve it? Identify problem areas: What do we have to watch out for? Prioritize tasks: What is most important for success? Adjust scope: What set of goals is most reali ...
... General Tasks and Associated Questions Determine our goals: What are we trying to achieve? Design a strategy: How are we going to achieve it? Identify problem areas: What do we have to watch out for? Prioritize tasks: What is most important for success? Adjust scope: What set of goals is most reali ...
Hitachi Data Systems Upstream Oil and Gas Overview
... sources and faster acquisition times that continue to amass terabytes and even petabytes of information per square kilometer. This mass of information is a collection of different data sets, so large, varied and complex that it becomes increasingly difficult and time-consuming to work with efficient ...
... sources and faster acquisition times that continue to amass terabytes and even petabytes of information per square kilometer. This mass of information is a collection of different data sets, so large, varied and complex that it becomes increasingly difficult and time-consuming to work with efficient ...
presentation
... Need for the Patient Data Browser • Newly adopted FDA guidelines: – Clinical study data be delivered as SAS® data files in all electronic submissions. ...
... Need for the Patient Data Browser • Newly adopted FDA guidelines: – Clinical study data be delivered as SAS® data files in all electronic submissions. ...
An overview of Data Warehousing and OLAP Technology
... • Used for building, maintain, managing and using data warehouse • Administrative meta data – Information about setting up and using warehouse ...
... • Used for building, maintain, managing and using data warehouse • Administrative meta data – Information about setting up and using warehouse ...
Introduction to Data Warehousing
... From DBMS to Decision Support • DBMSs widely used to maintain transactional data • Attempts to use of these data for analysis, exploration, identification of trends etc. has led to Decision Support Systems. • Rapid Growth since mid 70’s • DBMSs vendors have answered this trend by adding new feature ...
... From DBMS to Decision Support • DBMSs widely used to maintain transactional data • Attempts to use of these data for analysis, exploration, identification of trends etc. has led to Decision Support Systems. • Rapid Growth since mid 70’s • DBMSs vendors have answered this trend by adding new feature ...
Introduction to Databases Background and Fundamentals 1.2
... • A cell [field] is the value of the column for a particular patient At the intersection of a row and column is ONE value. This value is in it’s smallest useful form (atomic form). One more thing… We use a table, with rows and columns, as a MENTAL MODEL. It helps us envision the data. The data are n ...
... • A cell [field] is the value of the column for a particular patient At the intersection of a row and column is ONE value. This value is in it’s smallest useful form (atomic form). One more thing… We use a table, with rows and columns, as a MENTAL MODEL. It helps us envision the data. The data are n ...
Notes
... We will mainly discuss structured data That can be represented in tabular forms (called Relational data) We will spend some time on XML Still the biggest and most important business Well defined problem with really good solutions that work ...
... We will mainly discuss structured data That can be represented in tabular forms (called Relational data) We will spend some time on XML Still the biggest and most important business Well defined problem with really good solutions that work ...
Infographic: Zero Data Loss Recovery Appliance
... Oracle's Zero Data Loss Recovery Appliance is a ground-breaking data protection solution that’s completely integrated with Oracle Database, eliminating data-loss exposure for all databases ...
... Oracle's Zero Data Loss Recovery Appliance is a ground-breaking data protection solution that’s completely integrated with Oracle Database, eliminating data-loss exposure for all databases ...
MAD
... MPI, SVM acronym not introduced Slang: “feeding frenzies”, “vanilla” SQL, “MAD” Better comparison of EDW vs. MAD Section 5: Data Parallel statistics quite hard to follow in ...
... MPI, SVM acronym not introduced Slang: “feeding frenzies”, “vanilla” SQL, “MAD” Better comparison of EDW vs. MAD Section 5: Data Parallel statistics quite hard to follow in ...
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.""