the distributed tables
... • Grouping: Determine the largest set of data that is distribution compatible within the query. This will break queries in multiple compatible steps. ...
... • Grouping: Determine the largest set of data that is distribution compatible within the query. This will break queries in multiple compatible steps. ...
OLAP¿ë¾î
... calculation rule would normally calculate Profit for all combinations of the other dimensions in the cube (e.g., for all Products, for all Regions, for all Time Periods, etc.) using the respective Revenue and Expense data from those same dimensions. Part of the power of an OLAP system is the extensi ...
... calculation rule would normally calculate Profit for all combinations of the other dimensions in the cube (e.g., for all Products, for all Regions, for all Time Periods, etc.) using the respective Revenue and Expense data from those same dimensions. Part of the power of an OLAP system is the extensi ...
Queries management: Interfacing SAS with in-house EDC application
... In fact, even if on-line controls are cost-saving due to the limitation of errors at the time of data entry, the implementation of complex controls becomes rapidly cumbersome in EDC platforms. On the other hand, bias that could result from exhaustive on-line controls can be subject to controversy. S ...
... In fact, even if on-line controls are cost-saving due to the limitation of errors at the time of data entry, the implementation of complex controls becomes rapidly cumbersome in EDC platforms. On the other hand, bias that could result from exhaustive on-line controls can be subject to controversy. S ...
RDBMS to NoSQL: Reviewing Some Next-Generation Non
... Queries can only be processed against one domain, so if a client needs to aggregate data from different domains, this must also be done in the application layer. But it would be unwise to put ...
... Queries can only be processed against one domain, so if a client needs to aggregate data from different domains, this must also be done in the application layer. But it would be unwise to put ...
View
... • Differentiate data mining, text mining, and Web mining. Text mining involves analyzing vast amounts of textual data to determine patterns or correlations within the text. Data mining is a broader subject encompassing all types of information contained within an organization. Web mining extends da ...
... • Differentiate data mining, text mining, and Web mining. Text mining involves analyzing vast amounts of textual data to determine patterns or correlations within the text. Data mining is a broader subject encompassing all types of information contained within an organization. Web mining extends da ...
Impacts of Data Mining on Relational Database Management
... Our concern is about the RDBMS where multi relational data mining (MRDM) is the core model[20]. Data mining tasks has been categorized into two groups [21]. One is descriptive which extracts the general description or characteristics of data in the databases. And the second one is predictive that ma ...
... Our concern is about the RDBMS where multi relational data mining (MRDM) is the core model[20]. Data mining tasks has been categorized into two groups [21]. One is descriptive which extracts the general description or characteristics of data in the databases. And the second one is predictive that ma ...
of changed data
... • 760 tables • 200.000 changes per day are processed • Propagation to DB2 UDB under SUN Solaris • Processing time with tcVISION per day less than 30 minutes ...
... • 760 tables • 200.000 changes per day are processed • Propagation to DB2 UDB under SUN Solaris • Processing time with tcVISION per day less than 30 minutes ...
Designing Distributed Data Warehouses and OLAP Systems
... This motivates us to take a closer look into the systems dynamics, not just the static data structures. Furthermore, as dialogue objects over a data warehouse lead to views over a view, it may be questioned, whether it makes sense to take a holistic approach to data warehouse design or whether it mi ...
... This motivates us to take a closer look into the systems dynamics, not just the static data structures. Furthermore, as dialogue objects over a data warehouse lead to views over a view, it may be questioned, whether it makes sense to take a holistic approach to data warehouse design or whether it mi ...
Lecture 5
... information in two different systems, and hence they may not use these systems because they cannot trust the accuracy of the data. ...
... information in two different systems, and hence they may not use these systems because they cannot trust the accuracy of the data. ...
An Opportunity for the Database Community
... content providers set the prices and query limits for subscriptions. In both cases, the cloud provider takes a percent cut of the content provider’s income. There are several weaknesses with these existing market pricing models: First, per-query (or per-transaction) costs are irrelevant when chargin ...
... content providers set the prices and query limits for subscriptions. In both cases, the cloud provider takes a percent cut of the content provider’s income. There are several weaknesses with these existing market pricing models: First, per-query (or per-transaction) costs are irrelevant when chargin ...
OLAP: ON-LINE ANALYTICAL PROCESSING Defined terms On
... system, such a calculation rule would normally calculate Profit for all combinations of the other dimensions in the cube (e.g., for all Products, for all Regions, for all Time Periods, etc.) using the respective Revenue and Expense data from those same dimensions. Part of the power of an OLAP system ...
... system, such a calculation rule would normally calculate Profit for all combinations of the other dimensions in the cube (e.g., for all Products, for all Regions, for all Time Periods, etc.) using the respective Revenue and Expense data from those same dimensions. Part of the power of an OLAP system ...
Data warehousing with PostgreSQL
... • Allow to analyse, transform, model and deliver data within the database server ...
... • Allow to analyse, transform, model and deliver data within the database server ...
Chapter 7: Managerial Overview
... 2. Analytical Databases - Stores data and information extracted from selected operational and external databases. 3. Data Warehouses - Stores data from current and previous years that has been extracted from the various operational databases of an organization. 4. Distributed Databases - Stores copi ...
... 2. Analytical Databases - Stores data and information extracted from selected operational and external databases. 3. Data Warehouses - Stores data from current and previous years that has been extracted from the various operational databases of an organization. 4. Distributed Databases - Stores copi ...
Topic: The Database Environment
... A database is an organized collection of data and metadata, managed over a period of time. The data are what we’re mainly interested in, so that we may retrieve information, typically via query (where we ask a question of the data or perform a read in the CRUD operations). However, it is the metadat ...
... A database is an organized collection of data and metadata, managed over a period of time. The data are what we’re mainly interested in, so that we may retrieve information, typically via query (where we ask a question of the data or perform a read in the CRUD operations). However, it is the metadat ...
RFGex Prediction 2009 pt1
... columns such as dates, social security numbers, addresses and transaction amounts. ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantees database transactions are processed reliably and is a necessity for financial transactions and other applications where preci ...
... columns such as dates, social security numbers, addresses and transaction amounts. ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantees database transactions are processed reliably and is a necessity for financial transactions and other applications where preci ...
Lecture 2 - Unit information
... • Different functions and different data: – missing data: Decision support requires historical data which operational DBs do not typically maintain – data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources – data quality: different sources typica ...
... • Different functions and different data: – missing data: Decision support requires historical data which operational DBs do not typically maintain – data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources – data quality: different sources typica ...
Real time data loading and OLAP queries: Living - SEER-UFMG
... data, it is integrated and moved to the integrated sector. This approach does not provide query and update transparency, since it is not possible that queries access both real time and historical data simultaneously and in a continuous basis. Thomsen et al. [Thomsen et al. 2008] present a middleware ...
... data, it is integrated and moved to the integrated sector. This approach does not provide query and update transparency, since it is not possible that queries access both real time and historical data simultaneously and in a continuous basis. Thomsen et al. [Thomsen et al. 2008] present a middleware ...
Datawarehousing and Data Mining
... as in ROCK and Chameleon), or by first performing microclustering (that is, grouping objects into “microclusters”) and then operating on the microclusters with other clustering techniques, such as iterative relocation (as in BIRCH). A density-based method clusters objects based on the notion of dens ...
... as in ROCK and Chameleon), or by first performing microclustering (that is, grouping objects into “microclusters”) and then operating on the microclusters with other clustering techniques, such as iterative relocation (as in BIRCH). A density-based method clusters objects based on the notion of dens ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... sources e.g. relational database system, in the consolidated form using aggregation operation. We have created dimension and fact tables from RDBMS table schemas. Queries are executed on both RDBMS tables and DW dimension and fact tables with and without indexing using Java programs. The data retrie ...
... sources e.g. relational database system, in the consolidated form using aggregation operation. We have created dimension and fact tables from RDBMS table schemas. Queries are executed on both RDBMS tables and DW dimension and fact tables with and without indexing using Java programs. The data retrie ...
Challenges and Opportunities with Big Data
... educational activities, and this will generate an increasingly large amount of detailed data about students' performance. It is widely believed that the ...
... educational activities, and this will generate an increasingly large amount of detailed data about students' performance. It is widely believed that the ...
Processing Semi-Structured Data
... Each emigrant was described by about 50 attributes, such as AttendedSchools, Opinions, StayingPlaces, Duels, Jobs, etc. Each attribute might be optional (null valued), repeated and complex; each occurence of an attribute might be (optionally) associated with dates. To the purpose of this system I im ...
... Each emigrant was described by about 50 attributes, such as AttendedSchools, Opinions, StayingPlaces, Duels, Jobs, etc. Each attribute might be optional (null valued), repeated and complex; each occurence of an attribute might be (optionally) associated with dates. To the purpose of this system I im ...
Research on the data warehouse testing method in database design
... Cloud computing, or in simpler shorthand just "the cloud", also focuses on maximizing the effectiveness of the shared resources. Cloud resources are usually not only shred by multiple users but are also dynamically reallocated per demand. This can work for allocating resources to users. This approac ...
... Cloud computing, or in simpler shorthand just "the cloud", also focuses on maximizing the effectiveness of the shared resources. Cloud resources are usually not only shred by multiple users but are also dynamically reallocated per demand. This can work for allocating resources to users. This approac ...
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.""