(OLAP).
... – bulk loading of data from internal or external data sources – incremental loading of data from operational systems; – aggregation of input level data along hierarchies; ...
... – bulk loading of data from internal or external data sources – incremental loading of data from operational systems; – aggregation of input level data along hierarchies; ...
ch34 - UT Computer Science
... – bulk loading of data from internal or external data sources – incremental loading of data from operational systems; – aggregation of input level data along hierarchies; ...
... – bulk loading of data from internal or external data sources – incremental loading of data from operational systems; – aggregation of input level data along hierarchies; ...
An Analytic Data Warehouse for Ad-hoc Queries
... In this paper, we discuss Brighthouse [19, 44], which combines the advantages of being column-oriented and storing highly compressed data with an alternative approach to indexing. We follow an idea of automatic creation and usage of higher-level data about data (see e.g. [3, 5, 6, 8]), here referred ...
... In this paper, we discuss Brighthouse [19, 44], which combines the advantages of being column-oriented and storing highly compressed data with an alternative approach to indexing. We follow an idea of automatic creation and usage of higher-level data about data (see e.g. [3, 5, 6, 8]), here referred ...
Principles of Knowledge Discovery in Data Summary of Last
... • How can we implement a data warehouse? • Are there issues related to data cube technology? ...
... • How can we implement a data warehouse? • Are there issues related to data cube technology? ...
rethink data modeling
... or the logical whole of what is being described. There is a disconnect and the physical models just end up as convoluted, poor depictions of the world they are meant to portray. ...
... or the logical whole of what is being described. There is a disconnect and the physical models just end up as convoluted, poor depictions of the world they are meant to portray. ...
EGI-Engage-D3.5-V0.7
... installation, and whether it has interfaces similar to those already in use and if can be controlled using similar languages. There are a number of resources available to the APEL project within STFC that can help enable the testing and evaluation of the different big data technologies. The first is ...
... installation, and whether it has interfaces similar to those already in use and if can be controlled using similar languages. There are a number of resources available to the APEL project within STFC that can help enable the testing and evaluation of the different big data technologies. The first is ...
Dell Storage Center OS 7.0 Data Reduction with
... Even though data reduction is a topic that has been around for a long time and is a familiar subject, it has become an increasingly important consideration in more recent years due to the explosive growth of data being generated in our digital-everything age. With the ever-increasing amount of data ...
... Even though data reduction is a topic that has been around for a long time and is a familiar subject, it has become an increasingly important consideration in more recent years due to the explosive growth of data being generated in our digital-everything age. With the ever-increasing amount of data ...
Opencsdb: Research on The Application of
... diverse; the open access mechanism to scientific databases needs to be inclusive enough to accommodate these description formats. (2) Adaptability to diverse service environments. The CSDB project involves more than 50 database construction departments of the Chinese Academy of Sciences, each depar ...
... diverse; the open access mechanism to scientific databases needs to be inclusive enough to accommodate these description formats. (2) Adaptability to diverse service environments. The CSDB project involves more than 50 database construction departments of the Chinese Academy of Sciences, each depar ...
Evolution of Database Systems
... I Copy and integrate data from different sources, I Optimize data organization and tune system for OLAP, I Periodically refresh data in the data warehouse. ...
... I Copy and integrate data from different sources, I Optimize data organization and tune system for OLAP, I Periodically refresh data in the data warehouse. ...
Evolution of Database Systems
... I Copy and integrate data from different sources, I Optimize data organization and tune system for OLAP, I Periodically refresh data in the data warehouse. ...
... I Copy and integrate data from different sources, I Optimize data organization and tune system for OLAP, I Periodically refresh data in the data warehouse. ...
Extending data warehouses by semiconsistent views
... might not be there in a fully consistent world, a second consistency strategy is used to apply aggregation operators to quantitatively express the degree of un-consistency. •scalar strategy: A third perspective in defining consistency quantitatively appears when combining entries from different data ...
... might not be there in a fully consistent world, a second consistency strategy is used to apply aggregation operators to quantitatively express the degree of un-consistency. •scalar strategy: A third perspective in defining consistency quantitatively appears when combining entries from different data ...
Data Warehouse
... z Extract data from existing operational and legacy data z Issues: Sources of data for the warehouse Data quality at the sources Merging different data sources Data Transformation How to propagate updates (on the sources) to the warehouse y Terabytes of data to be loaded ...
... z Extract data from existing operational and legacy data z Issues: Sources of data for the warehouse Data quality at the sources Merging different data sources Data Transformation How to propagate updates (on the sources) to the warehouse y Terabytes of data to be loaded ...
data warehousing and data mining
... reporting needs tradeoff with volume of data to be stored and detailed usage of data ...
... reporting needs tradeoff with volume of data to be stored and detailed usage of data ...
HP_TemIt_Guidelines
... TPC is the only benchmark organization that requires priceperformance scores across all of its benchmarks ...
... TPC is the only benchmark organization that requires priceperformance scores across all of its benchmarks ...
Benchmarking data warehouses Jérôme Darmont*, Fadila Bentayeb
... designing the OCB object-oriented database benchmark (Darmont and Schneider, 2000). OCB is generic and able to model all the other existing object-oriented database benchmarks, but it is controlled by too many parameters, few of which are used in practice. Hence, we propose to divide the parameter s ...
... designing the OCB object-oriented database benchmark (Darmont and Schneider, 2000). OCB is generic and able to model all the other existing object-oriented database benchmarks, but it is controlled by too many parameters, few of which are used in practice. Hence, we propose to divide the parameter s ...
Data Warehouse Platforms
... support the data warehouse, organizations are faced with an exponentially higher number of variations and distinct departures from the traditional online transactional processing (OLTP) database management systems than ever before. Over time, data warehouse data volumes will continue to soar as hist ...
... support the data warehouse, organizations are faced with an exponentially higher number of variations and distinct departures from the traditional online transactional processing (OLTP) database management systems than ever before. Over time, data warehouse data volumes will continue to soar as hist ...
The Impact of XML on Databases and Data Sharing
... tasks that data sharing must accomplish, regardless of architecture and formalism. We then use this list of data sharing functions as a framework for answering the questions: Where can XML help? What portion remains unsolved? Generic Tasks for Data Sharing Users want seamless access to all relevant ...
... tasks that data sharing must accomplish, regardless of architecture and formalism. We then use this list of data sharing functions as a framework for answering the questions: Where can XML help? What portion remains unsolved? Generic Tasks for Data Sharing Users want seamless access to all relevant ...
Big Data Exploration By Stratos Idreos CWI, Amsterdam, The
... main bottleneck that stops us from consuming big data today; instead of considering a big data set in one go with a slow process, exploration-based systems can incrementally and adaptively guide users towards the path that their queries and the result lead. This helps us avoid major inherent costs, ...
... main bottleneck that stops us from consuming big data today; instead of considering a big data set in one go with a slow process, exploration-based systems can incrementally and adaptively guide users towards the path that their queries and the result lead. This helps us avoid major inherent costs, ...
Data Warehousing and Data Mining
... • It is important to understand the distinction between the functions in data scrubbing and in data transformation. • Goal of data scrubbing is to correct errors in data values in the source data, whereas the goal of data transformation is to convert the data format from the source to the target sys ...
... • It is important to understand the distinction between the functions in data scrubbing and in data transformation. • Goal of data scrubbing is to correct errors in data values in the source data, whereas the goal of data transformation is to convert the data format from the source to the target sys ...
Evolution of Database Technology
... Worldwide Vendor Revenue Estimates from RDBMS Software, Based on Total Software Revenue, 2006 (Millions of Dollars) ...
... Worldwide Vendor Revenue Estimates from RDBMS Software, Based on Total Software Revenue, 2006 (Millions of Dollars) ...
ETL Evolution for Real-Time Data Warehousing
... introduced unique data quality challenges. Organizations demand for quick access to new insights has led to predictive analytics for forecasting emerging demands, risks and opportunity. Advanced analytics apply statistical and predictive algorithms to forecasting, correlation, and trend analysis. In ...
... introduced unique data quality challenges. Organizations demand for quick access to new insights has led to predictive analytics for forecasting emerging demands, risks and opportunity. Advanced analytics apply statistical and predictive algorithms to forecasting, correlation, and trend analysis. In ...
PP453OLAP - WordPress.com
... ROLAP supports RDBMS products through the use of a metadata layer, thus avoiding the requirement to create a static multi-dimensional data structure. This facilitates the creation of multiple multi-dimensional views of the two-dimensional relation. To improve performance, some ROLAP products have en ...
... ROLAP supports RDBMS products through the use of a metadata layer, thus avoiding the requirement to create a static multi-dimensional data structure. This facilitates the creation of multiple multi-dimensional views of the two-dimensional relation. To improve performance, some ROLAP products have en ...
OLAP - Computer Science
... ROLAP supports RDBMS products through the use of a metadata layer, thus avoiding the requirement to create a static multi-dimensional data structure. This facilitates the creation of multiple multi-dimensional views of the two-dimensional relation. To improve performance, some ROLAP products have en ...
... ROLAP supports RDBMS products through the use of a metadata layer, thus avoiding the requirement to create a static multi-dimensional data structure. This facilitates the creation of multiple multi-dimensional views of the two-dimensional relation. To improve performance, some ROLAP products have en ...
Key Data Replication Criteria for Enabling Operational
... Also, IDR can send data to message queue technology. The message bus can then pass that data to any type of application interested in this data. In a way, by combining IDR with message queues, data is pushed from the source system to the target system. For example, operational dashboards can really ...
... Also, IDR can send data to message queue technology. The message bus can then pass that data to any type of application interested in this data. In a way, by combining IDR with message queues, data is pushed from the source system to the target system. For example, operational dashboards can really ...
- e-Prints.FRI
... exponentially, and an enormous amount of data is being collected, processed and stored every day. That’s why companies - big and small - need robust, rapid and reliable systems. These are so-called transaction systems, mostly known as ERP (Enterprise Resource Planning) systems. Even though these sys ...
... exponentially, and an enormous amount of data is being collected, processed and stored every day. That’s why companies - big and small - need robust, rapid and reliable systems. These are so-called transaction systems, mostly known as ERP (Enterprise Resource Planning) systems. Even though these sys ...
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.""