Modeling and Querying Multidimensional Bitemporal Data
... are calculated over with this new live data. When a report is presented via OLAP cubes, there is no calculation performed when reporting. All the calculated values are generally stored in the OLAP cubes as before. The only processing consists of calling the report and then displaying it. Versioning ...
... are calculated over with this new live data. When a report is presented via OLAP cubes, there is no calculation performed when reporting. All the calculated values are generally stored in the OLAP cubes as before. The only processing consists of calling the report and then displaying it. Versioning ...
ra-predictive-analytics-and-interactive-queries-on-big
... recommendations based on both structured and unstructured data. The extreme requirements of this use case—deep, predictive analytics with sub-second response times—are met using a two-layer, offline and online architecture. The offline component acts on historical data using batch-oriented processin ...
... recommendations based on both structured and unstructured data. The extreme requirements of this use case—deep, predictive analytics with sub-second response times—are met using a two-layer, offline and online architecture. The offline component acts on historical data using batch-oriented processin ...
Working in the GEON Portal
... GEON is based on a service-oriented architecture (SOA) with support for “intelligent” search, semantic data integration, visualization of 4D scientific datasets, and access to high performance computing platforms for data analysis and model execution -- via the GEON Portal. While focused on Earth Sc ...
... GEON is based on a service-oriented architecture (SOA) with support for “intelligent” search, semantic data integration, visualization of 4D scientific datasets, and access to high performance computing platforms for data analysis and model execution -- via the GEON Portal. While focused on Earth Sc ...
Data Mining as Part of Knowledge Discovery in Databases (KDD
... unknown and potentially useful knowledge from data” ...
... unknown and potentially useful knowledge from data” ...
Constructing a Data Warehouse for Pharmacokinetic Data
... The pharmaceutical industry, like other research and development (R&D) driven industries, generates large volumes of data of various types during the process of developing new medicines to ultimately improve the quality of people’s lives. Data from early chemistry and pharmacology experiments, anima ...
... The pharmaceutical industry, like other research and development (R&D) driven industries, generates large volumes of data of various types during the process of developing new medicines to ultimately improve the quality of people’s lives. Data from early chemistry and pharmacology experiments, anima ...
Practial Applications of DataMining
... determine a region’s classification.Many properties are associated with spatial objects, such as hosting a university, containing interstate highways, being near a lake or ocean, and so on. These properties can be used for relevance analysis and to find interesting classification schemes. Such class ...
... determine a region’s classification.Many properties are associated with spatial objects, such as hosting a university, containing interstate highways, being near a lake or ocean, and so on. These properties can be used for relevance analysis and to find interesting classification schemes. Such class ...
Use of SAS Reports for External Vendor Data Reconciliation
... Note: Medical and scientific judgment should be exercised in deciding whether expedited reporting is appropriate in other situations, such as important medical events that may not be immediately life-threatening, result in death, or hospitalization but may jeopardize the patient or may require inter ...
... Note: Medical and scientific judgment should be exercised in deciding whether expedited reporting is appropriate in other situations, such as important medical events that may not be immediately life-threatening, result in death, or hospitalization but may jeopardize the patient or may require inter ...
slides
... Early days: customized applications built on top of file systems Drawbacks of using file systems to store data: – Data redundancy and inconsistency – Difficulty in accessing data – Atomicity of updates – Concurrency control – Security – Data isolation — multiple files and formats – Integrity problem ...
... Early days: customized applications built on top of file systems Drawbacks of using file systems to store data: – Data redundancy and inconsistency – Difficulty in accessing data – Atomicity of updates – Concurrency control – Security – Data isolation — multiple files and formats – Integrity problem ...
CIS671-Knowledge Discovery and Data Mining
... • 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 ...
Creating a Partitioned Table
... SQL Server 2008 – What’s New Row, page, and backup compression ...
... SQL Server 2008 – What’s New Row, page, and backup compression ...
Data Cleaning: Overview and Emerging Challenges
... results. The Eracer project showed how data cleaning on dirty relations could be posed as a two-step learning problem: first learning a graphical model to represent the relation and message-passing algorithm to resolve inconsistencies [57]. Furthermore, in the sensor network literature (refer to a 2 ...
... results. The Eracer project showed how data cleaning on dirty relations could be posed as a two-step learning problem: first learning a graphical model to represent the relation and message-passing algorithm to resolve inconsistencies [57]. Furthermore, in the sensor network literature (refer to a 2 ...
Power Point - Arizona State University
... • Performance and real-time nature is more important than consistency • Indexing a large number of documents • Serving pages on high-traffic web sites • Delivering streaming media ...
... • Performance and real-time nature is more important than consistency • Indexing a large number of documents • Serving pages on high-traffic web sites • Delivering streaming media ...
Data Integrity
... of some table in the database. Occasionally, and this will depend on the rules of the data owner, a foreign-key value can be null. In this case we are explicitly saying that either there is no relationship between the objects represented in the database or that this relationship is unknown. • Domain ...
... of some table in the database. Occasionally, and this will depend on the rules of the data owner, a foreign-key value can be null. In this case we are explicitly saying that either there is no relationship between the objects represented in the database or that this relationship is unknown. • Domain ...
Dia 1 - USURT
... Actively updating graphical displays that provides business users with updates on key metrics Some dashboards provide drill through capability, allowing users to start with summary data and dive in to the details ...
... Actively updating graphical displays that provides business users with updates on key metrics Some dashboards provide drill through capability, allowing users to start with summary data and dive in to the details ...
Data Integration and ETL - Campus Connect
... integrity of data. It helps: Discover the quality, characteristics and potential problems Reduce the time and resources in finding problematic data Gain more control on the maintenance and management of data Catalog and analyze metadata • The various steps in profiling include ...
... integrity of data. It helps: Discover the quality, characteristics and potential problems Reduce the time and resources in finding problematic data Gain more control on the maintenance and management of data Catalog and analyze metadata • The various steps in profiling include ...
Data Cleaning: Overview and Emerging Challenges
... for a supervised learning technique (such as an SVM or Random Forest), and Active Learning is a class of algorithms that select the most informative labels to acquire. ...
... for a supervised learning technique (such as an SVM or Random Forest), and Active Learning is a class of algorithms that select the most informative labels to acquire. ...
big data and five v`s characteristics
... of storing, analyzing and visualizing for further processes or results. The research into large amounts of data in order to reveal hidden patterns and secret correlations named as Big Data analytics. This isuseful information for companies or organizations tohelp gain richer and deeper insights and ...
... of storing, analyzing and visualizing for further processes or results. The research into large amounts of data in order to reveal hidden patterns and secret correlations named as Big Data analytics. This isuseful information for companies or organizations tohelp gain richer and deeper insights and ...
LHC Controls Project - lhc-div-mms.web.cern.ch - /lhc-div-mms
... Similar to the control of LEP, data for LHC controls will be managed with a set of centralised databases In order to feed these LHC Controls databases, data from various production databases will be needed Oracle is the chosen RDBMS We will push Oracle to the limits, real time capabilities LHC could ...
... Similar to the control of LEP, data for LHC controls will be managed with a set of centralised databases In order to feed these LHC Controls databases, data from various production databases will be needed Oracle is the chosen RDBMS We will push Oracle to the limits, real time capabilities LHC could ...
Data Transfers Across Diverse Platforms
... The transfer media are decided based on the policies of the parties involved. Email is used for data transfers, but some corporate policies do not allow data transferred over the Internet due to security issues. If data are transferred via email, the files should be compressed (e.g. using WinZip) to ...
... The transfer media are decided based on the policies of the parties involved. Email is used for data transfers, but some corporate policies do not allow data transferred over the Internet due to security issues. If data are transferred via email, the files should be compressed (e.g. using WinZip) to ...
DvoyDatabaseIdeas
... A measure has set of discrete data granules –atomic data entities that cannot be further broken down. All data points in a measure represent the same measured parameter e.g. temperature. Hence, they share the same units and dimensionality. The data points of a measure are enclosed in a conceptual mu ...
... A measure has set of discrete data granules –atomic data entities that cannot be further broken down. All data points in a measure represent the same measured parameter e.g. temperature. Hence, they share the same units and dimensionality. The data points of a measure are enclosed in a conceptual mu ...
Big Data Methods for Social Science and Policy
... data. In some research areas (for example, using census data, company data, NGO data, household data) data may be fragmented and locating data is a challenge. In terms of social data (for example, using Facebook and Twitter data) there is disconnect between researcher and data input. Social data als ...
... data. In some research areas (for example, using census data, company data, NGO data, household data) data may be fragmented and locating data is a challenge. In terms of social data (for example, using Facebook and Twitter data) there is disconnect between researcher and data input. Social data als ...
Document
... Operational Data Store (ODS) is a hybrid data architecture to cover the requirements for both analytical and operational tasks. 27)What is the difference between star schema and snowflake schema? The star schema consists of a fact table with a single table for each dimension. The snowflake schema is ...
... Operational Data Store (ODS) is a hybrid data architecture to cover the requirements for both analytical and operational tasks. 27)What is the difference between star schema and snowflake schema? The star schema consists of a fact table with a single table for each dimension. The snowflake schema is ...
Data Modeling for Business Intelligence with Microsoft
... For decades, businesses of all shapes and sizes have been storing their historical data. Everything from cash register rolls stored in filing cabinets to web application transactions being stored in relational databases have been sitting quietly, often collecting dust, just waiting for someone to di ...
... For decades, businesses of all shapes and sizes have been storing their historical data. Everything from cash register rolls stored in filing cabinets to web application transactions being stored in relational databases have been sitting quietly, often collecting dust, just waiting for someone to di ...
The SAS System as an Information Database in a Client/Server Environment
... applied to views of operational data in order to "roll up" or summarize the transaction-level data, apply user-friendly formats, perform filtering and merging tasks, and otherwise enhance an organization's raw data assets in preparation for turning that data into meaningful information. The fmal sec ...
... applied to views of operational data in order to "roll up" or summarize the transaction-level data, apply user-friendly formats, perform filtering and merging tasks, and otherwise enhance an organization's raw data assets in preparation for turning that data into meaningful information. The fmal sec ...
Data Integration and Exchange - Informatics Homepages Server
... • Do it from scratch or use commercial tools? ◦ many are available (just google for “data integration”) ◦ but do we fully understand them? ◦ lots of them are very ad hoc, with poorly defined semantics ◦ this is why it is so important to understand what really happens in data integration ...
... • Do it from scratch or use commercial tools? ◦ many are available (just google for “data integration”) ◦ but do we fully understand them? ◦ lots of them are very ad hoc, with poorly defined semantics ◦ this is why it is so important to understand what really happens in data integration ...
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.""