PPT
... Each value in the column has a bit vector: bit-op is fast The length of the bit vector: # of records in the base table The i-th bit is set if the i-th row of the base table has the value for the indexed column not suitable for high cardinality domains ...
... Each value in the column has a bit vector: bit-op is fast The length of the bit vector: # of records in the base table The i-th bit is set if the i-th row of the base table has the value for the indexed column not suitable for high cardinality domains ...
Is Data Staging Relational: A Comment
... file:///E|/FrontPage Webs/Content/EISWEB/DKMSDataStage.html ...
... file:///E|/FrontPage Webs/Content/EISWEB/DKMSDataStage.html ...
VO as a Data Grid, NeSC `03
... • detailed (but relatively small) amount of descriptive data with images and catalogues • required to track descriptive data and images along with catalogue data • for current/future generation surveys processing and ingest dictated by observing patterns • but users require well defined, stable cata ...
... • detailed (but relatively small) amount of descriptive data with images and catalogues • required to track descriptive data and images along with catalogue data • for current/future generation surveys processing and ingest dictated by observing patterns • but users require well defined, stable cata ...
What is a data model
... •It provides efficient encoding of topology ,and as a result ,more efficient;implementation of operations that require topological information ,such as network analysis ...
... •It provides efficient encoding of topology ,and as a result ,more efficient;implementation of operations that require topological information ,such as network analysis ...
Data Integration with Talend Open Studio Robert A. Nisbet, Ph.D.
... analytical modeling operations. Even newer tools employ a menu-driven graphical user interface (GUI) to orchestrate various data integration operations to create data structures in a format suitable for data mining. The latest generation of data integration tools use a graphical programming user int ...
... analytical modeling operations. Even newer tools employ a menu-driven graphical user interface (GUI) to orchestrate various data integration operations to create data structures in a format suitable for data mining. The latest generation of data integration tools use a graphical programming user int ...
Data Warehousing and Data Mining in Business Applications
... aggregating data from multiple sources into one common repository. This process occurs before the Data Mining process. In Data Warehousing, data which is stored in different databases are combined into one central phase in order to access the database. This is further available to managers to use fo ...
... aggregating data from multiple sources into one common repository. This process occurs before the Data Mining process. In Data Warehousing, data which is stored in different databases are combined into one central phase in order to access the database. This is further available to managers to use fo ...
data mining and warehousing
... as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive r ...
... as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive r ...
data
... “Data Quality: Concepts, Methodologies and Techniques”, C. Batini and M. Scannapieco, Springer-Verlag, 2006 (Chapts. 1, 2, and 4). “A Survey of Data Quality tools”, J. Barateiro, ...
... “Data Quality: Concepts, Methodologies and Techniques”, C. Batini and M. Scannapieco, Springer-Verlag, 2006 (Chapts. 1, 2, and 4). “A Survey of Data Quality tools”, J. Barateiro, ...
© 2011 by The 451 Group. All rights reserved
... Introducing Total Data – Defined by The 451 Group to describe new approaches to data management – Reflects the changing data management landscape as pragmatic choices are made about data storage and analysis techniques – Processing any data applicable to analytic query • in database, data warehouse ...
... Introducing Total Data – Defined by The 451 Group to describe new approaches to data management – Reflects the changing data management landscape as pragmatic choices are made about data storage and analysis techniques – Processing any data applicable to analytic query • in database, data warehouse ...
Document
... which is also a major component within the field. Data mining (DM) is normally used on large quantities (terabytes) of data, to find meaningful patterns. Neural nets, statistical modelling, decision trees are just some AI methods used. SQL can be used too. Parallel data processing is used with DM. I ...
... which is also a major component within the field. Data mining (DM) is normally used on large quantities (terabytes) of data, to find meaningful patterns. Neural nets, statistical modelling, decision trees are just some AI methods used. SQL can be used too. Parallel data processing is used with DM. I ...
Extraction, Transformation, Loading (ETL) and Data Cleaning
... Receive walk-through of the dimensional model and the desired data has to be identified and extracted from many different sources, including database systems and applications. Very often, it is not possible to identify the specific subset of interest; therefore, more data than is necessary has to be ...
... Receive walk-through of the dimensional model and the desired data has to be identified and extracted from many different sources, including database systems and applications. Very often, it is not possible to identify the specific subset of interest; therefore, more data than is necessary has to be ...
auth3in
... view are needed, data will have to be stored. Images may bear two types of attributes: some that are usually found in the image file header (format, compression rate, size, resolution), and some that need to be extracted by program, such as color or texture distributions. Eventually, sounds and vide ...
... view are needed, data will have to be stored. Images may bear two types of attributes: some that are usually found in the image file header (format, compression rate, size, resolution), and some that need to be extracted by program, such as color or texture distributions. Eventually, sounds and vide ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... M.Tech(CSE) Lecturer , Department of Information Systems, UNIVERSITY OF GONDAR ETHOPIA ...
... M.Tech(CSE) Lecturer , Department of Information Systems, UNIVERSITY OF GONDAR ETHOPIA ...
transparent - CERN Indico
... • Many (online?) people also want the freedom of a full-function RDBMS – The freedom to design and implement their own data model and schemas – At the same time the common project can only address common solutions (components that can be factored out: metadata model, API, common schema…) • The rest ...
... • Many (online?) people also want the freedom of a full-function RDBMS – The freedom to design and implement their own data model and schemas – At the same time the common project can only address common solutions (components that can be factored out: metadata model, API, common schema…) • The rest ...
Lecture2
... Comparing the logical data model shown above with the conceptual data model diagram, we see the main differences between the two: 1. in a logical data model, primary keys are present, whereas in a conceptual data model, no primary key is present. 2. In a logical data model, all attributes are specif ...
... Comparing the logical data model shown above with the conceptual data model diagram, we see the main differences between the two: 1. in a logical data model, primary keys are present, whereas in a conceptual data model, no primary key is present. 2. In a logical data model, all attributes are specif ...
Technology in Action
... Include e-mail, word processing, spreadsheet, database, and presentation ...
... Include e-mail, word processing, spreadsheet, database, and presentation ...
Data warehouse
... provide any reporting capabilities • The reports address the organization’s q especially p y those tactical requirements, that need the most current information – No aggregation gg g abilities,, much simpler p than the OLAP analysis ...
... provide any reporting capabilities • The reports address the organization’s q especially p y those tactical requirements, that need the most current information – No aggregation gg g abilities,, much simpler p than the OLAP analysis ...
Using SAS Data Sets to Mimic a Relational Database
... together chronologically rather than hierarchically. This was one of the factors we considered when we decided to build our database in SAS instead of our usual DBMS. The databases we routinely build are structured in a strictly hierarchical manner. Since the Reason Flags segment links together seve ...
... together chronologically rather than hierarchically. This was one of the factors we considered when we decided to build our database in SAS instead of our usual DBMS. The databases we routinely build are structured in a strictly hierarchical manner. Since the Reason Flags segment links together seve ...
Data Doctor: An Efficient Data Profiling and Quality
... together to profile all data, for all projects and all applications. A set of unified, role-based data profiling and discovery tools so that the business can be more selfsufficient and IT more productive. The main idea for developing this project is to implement a user friendly graphical interface f ...
... together to profile all data, for all projects and all applications. A set of unified, role-based data profiling and discovery tools so that the business can be more selfsufficient and IT more productive. The main idea for developing this project is to implement a user friendly graphical interface f ...
In-database Analytical Systems
... specific, single location on a disk and perform the aggregations in real time. So disk-based systems adopt a variety of strategies to provide faster response times. SSAS, for example, typically pre-aggregates the data after loading and before the users start to query it. This has the disadvantage of ...
... specific, single location on a disk and perform the aggregations in real time. So disk-based systems adopt a variety of strategies to provide faster response times. SSAS, for example, typically pre-aggregates the data after loading and before the users start to query it. This has the disadvantage of ...
Relational Database vs NoSQL
... Relational data is often referred to as structured data, because the tables have a pre-defined schema (column definitions) .While a pre-defined schema offers reliability and stability, a change to a schema on a table with pre-existing data is very difficult. Nonrelational data, on the other hand, th ...
... Relational data is often referred to as structured data, because the tables have a pre-defined schema (column definitions) .While a pre-defined schema offers reliability and stability, a change to a schema on a table with pre-existing data is very difficult. Nonrelational data, on the other hand, th ...
Member. - dbmanagement.info
... place of the main member name. Aliases are commonly used for storing descriptions and for providing alternative naming conventions. The Member name and Alias name should be unique throughout the Outline. There can be multiple Alias to a member. To implement this, we need to have ...
... place of the main member name. Aliases are commonly used for storing descriptions and for providing alternative naming conventions. The Member name and Alias name should be unique throughout the Outline. There can be multiple Alias to a member. To implement this, we need to have ...
A Data Browsing from Various Sources Driven by the
... A lot of visual browsers were developed for Object-oriented databases (OODB). Good examples for the OODB browsing include PESTO [2], QBB [3], IVORY [4]. In PESTO approach data at first are queried and then visualized. User defines a main class. The main class’ objects are visualized and related obje ...
... A lot of visual browsers were developed for Object-oriented databases (OODB). Good examples for the OODB browsing include PESTO [2], QBB [3], IVORY [4]. In PESTO approach data at first are queried and then visualized. User defines a main class. The main class’ objects are visualized and related obje ...
Now - Allot Communications
... data from your network, our Professional Services experts help you identify and obtain the right data sets quickly and efficiently, for the specific use cases that you want to address. They will guide you through big and small projects alike, starting with problem definition, through proof of concep ...
... data from your network, our Professional Services experts help you identify and obtain the right data sets quickly and efficiently, for the specific use cases that you want to address. They will guide you through big and small projects alike, starting with problem definition, through proof of concep ...
data mining and warehousing concepts
... According to William J. Frawley, Gregory Piatetsky-Shapiro and Christopher J. Matheus ‘Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This encompasses a number of ...
... According to William J. Frawley, Gregory Piatetsky-Shapiro and Christopher J. Matheus ‘Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This encompasses a number of ...
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.""