DATASPREAD: Unifying Databases and Spreadsheets
... Although spreadsheets and databases have both been designed to manage data in form of tables, their treatment of this data is vastly different. Spreadsheets have been developed primarily with presentation of data in mind and hence their design focuses primarily on simplicity, intuitiveness and a ric ...
... Although spreadsheets and databases have both been designed to manage data in form of tables, their treatment of this data is vastly different. Spreadsheets have been developed primarily with presentation of data in mind and hence their design focuses primarily on simplicity, intuitiveness and a ric ...
DATASPREAD: Unifying Databases and Spreadsheets
... Although spreadsheets and databases have both been designed to manage data in form of tables, their treatment of this data is vastly different. Spreadsheets have been developed primarily with presentation of data in mind and hence their design focuses primarily on simplicity, intuitiveness and a ric ...
... Although spreadsheets and databases have both been designed to manage data in form of tables, their treatment of this data is vastly different. Spreadsheets have been developed primarily with presentation of data in mind and hence their design focuses primarily on simplicity, intuitiveness and a ric ...
Oracle: Big Data for the Enterprise
... Distributed file systems and transaction (key-value) stores are primarily used to capture data and are generally in line with the requirements discussed earlier in this paper. To interpret and distill information from the data in these solutions, a programming paradigm called MapReduce is used. MapR ...
... Distributed file systems and transaction (key-value) stores are primarily used to capture data and are generally in line with the requirements discussed earlier in this paper. To interpret and distill information from the data in these solutions, a programming paradigm called MapReduce is used. MapR ...
data warehousing - Sayco - Secured Assets Yield Corporation
... Data Warehousing is a technique of bringing collectively all of a company’s data from different computer systems, together with those connecting to customers, employees, vendors, product, inventory, and financial. The data warehouse connects different database together in order to offer a more inclu ...
... Data Warehousing is a technique of bringing collectively all of a company’s data from different computer systems, together with those connecting to customers, employees, vendors, product, inventory, and financial. The data warehouse connects different database together in order to offer a more inclu ...
Database Management Systems Objectives of Lecture 5 Data
... HOLAP: Hybrid OLAP - combines ROLAP and MOLAP technology. (Scalability of ROLAP and faster computation of MOLAP) ...
... HOLAP: Hybrid OLAP - combines ROLAP and MOLAP technology. (Scalability of ROLAP and faster computation of MOLAP) ...
Oracle: Big Data for the Enterprise
... Oracle Big Data Appliance includes a combination of open source software and specialized software developed by Oracle to address enterprise big data requirements. Oracle NoSQL Database is a distributed, highly scalable, key-value database based on Oracle Berkeley DB. It delivers a general purpose, e ...
... Oracle Big Data Appliance includes a combination of open source software and specialized software developed by Oracle to address enterprise big data requirements. Oracle NoSQL Database is a distributed, highly scalable, key-value database based on Oracle Berkeley DB. It delivers a general purpose, e ...
E15003 0817804 CO6002 Assignment 2010-11: Word
... A surrogate key is usually created to simplify the key structure. That is to say it is an artificial substitution for a natural primary key, data held within this key may change with time, known as a slowly changing dimension. These surrogate keys are neither intelligent nor business specific and mo ...
... A surrogate key is usually created to simplify the key structure. That is to say it is an artificial substitution for a natural primary key, data held within this key may change with time, known as a slowly changing dimension. These surrogate keys are neither intelligent nor business specific and mo ...
Finding and Fixing Data Quality Problems
... For both cases, many of the same tools and techniques can be used. In fact, it’s often beneficial, in divide and conquer approach, to always start with 1 ...
... For both cases, many of the same tools and techniques can be used. In fact, it’s often beneficial, in divide and conquer approach, to always start with 1 ...
Data Warehouse
... The Data Mart Because of the time, money and considerable mangerial effort required to create a data warehouse, many companies begin on a smaller scale with a data mart A data mart is a small, single-subject data warehouse subset that provides decision support to a small group of people Lower ...
... The Data Mart Because of the time, money and considerable mangerial effort required to create a data warehouse, many companies begin on a smaller scale with a data mart A data mart is a small, single-subject data warehouse subset that provides decision support to a small group of people Lower ...
Querying Semi-Structured Data
... database query languages, application-speci c interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure ...
... database query languages, application-speci c interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure ...
(CDW) Website: From Spreadsheet to Database Using
... 5) Interpretability -- Standardize naming and typing conventions; create clear, concise, easy to understand data definitions. 6) Coherence -- Develop common key structures to improve record matching across database tables; ensure key fields have common names and data types. The source of the six dat ...
... 5) Interpretability -- Standardize naming and typing conventions; create clear, concise, easy to understand data definitions. 6) Coherence -- Develop common key structures to improve record matching across database tables; ensure key fields have common names and data types. The source of the six dat ...
High Performance Data Analytics in Precision Medicine
... high performance supercomputing with advanced data analytics. The ultimate goal of HPDA in Precision Medicine is to provide medical professionals with crucial recommendations for the best course of treatment for individual patients based on all the relevant data available. FedCentric is developing s ...
... high performance supercomputing with advanced data analytics. The ultimate goal of HPDA in Precision Medicine is to provide medical professionals with crucial recommendations for the best course of treatment for individual patients based on all the relevant data available. FedCentric is developing s ...
Some Considerations in Designing SAS®-Based Data Management Systems for Large International Epidemiologic Studies
... and developing data processing procedures. This paper will focus on the practical strategies used in designing a common database as well as procedures for transfer and processing data from these large studies in a microcomputing environment. The review will be more practical than technical in focus, ...
... and developing data processing procedures. This paper will focus on the practical strategies used in designing a common database as well as procedures for transfer and processing data from these large studies in a microcomputing environment. The review will be more practical than technical in focus, ...
SUGI 27: IDW -- The Next Generation Data Warehouse
... definition, historical, static and in today’s market old news. The primary function of this data is to manage the business with after the fact information. One reason that Data Warehouse projects fail to live up to management expectations is the failure to understand the relationship of the data to ...
... definition, historical, static and in today’s market old news. The primary function of this data is to manage the business with after the fact information. One reason that Data Warehouse projects fail to live up to management expectations is the failure to understand the relationship of the data to ...
RFGex Prediction 2009 pt1
... representation of "ownership" for documents within an enterprise for legal or ediscovery purposes. ...
... representation of "ownership" for documents within an enterprise for legal or ediscovery purposes. ...
Clinical Data Management Using SAS/AF
... In the final cleaning step. data sets are compared for an exact match. A PROC COMPARE report is generated that lists records in PRIMARY and DOUBLE slots that disagree in one or more fields. When all fields in a record agree in PRIMARY and DOUBLE, the record is considered clean and stamped with a cle ...
... In the final cleaning step. data sets are compared for an exact match. A PROC COMPARE report is generated that lists records in PRIMARY and DOUBLE slots that disagree in one or more fields. When all fields in a record agree in PRIMARY and DOUBLE, the record is considered clean and stamped with a cle ...
网络化TITAN-MGIS的研究与开发
... multi-resolution, three-dimensional representation of our planet, into which we can embed vast quantities of geo-referenced data [1]. A new wave of technological innovation is allowing us to capture, store, process and display an unprecedented amount of information about our planet and a variety of ...
... multi-resolution, three-dimensional representation of our planet, into which we can embed vast quantities of geo-referenced data [1]. A new wave of technological innovation is allowing us to capture, store, process and display an unprecedented amount of information about our planet and a variety of ...
Training Plan for Stream II
... business systems collect. Typically, a data warehouse is housed on an enterprise mainframe server. Data from various online transaction processing (OLTP) applications and other sources is selectively extracted and organized on the data warehouse database for use by analytical applications and user q ...
... business systems collect. Typically, a data warehouse is housed on an enterprise mainframe server. Data from various online transaction processing (OLTP) applications and other sources is selectively extracted and organized on the data warehouse database for use by analytical applications and user q ...
System, method and software application for incorporating data from
... manner. Accordingly, the integrated applications 64 and 66 each transmit and report raW data they collect directly to the I&T interface layer 46 via the database links 70 and 72. This collected and reported data is then processed, re?ned and transmitted to the data Warehouse 48 via the database link ...
... manner. Accordingly, the integrated applications 64 and 66 each transmit and report raW data they collect directly to the I&T interface layer 46 via the database links 70 and 72. This collected and reported data is then processed, re?ned and transmitted to the data Warehouse 48 via the database link ...
File - Information Technology SNIST
... The data source view exposes the information being captured, stored, and managed by operational systems. This information may be documented at various levels of detail and accuracy, from individual data source tables to integrated data source tables. Data sources are often modeled by traditional dat ...
... The data source view exposes the information being captured, stored, and managed by operational systems. This information may be documented at various levels of detail and accuracy, from individual data source tables to integrated data source tables. Data sources are often modeled by traditional dat ...
Using Temporary Tables to Improve Performance for
... A temporary table is a table in a relational database that stores intermediate, temporary data. Complex queries commonly require storage for large amounts of intermediate data, such as information from joins. When you implement temporary tables, business intelligence tools can retrieve this data fro ...
... A temporary table is a table in a relational database that stores intermediate, temporary data. Complex queries commonly require storage for large amounts of intermediate data, such as information from joins. When you implement temporary tables, business intelligence tools can retrieve this data fro ...
9.Sybase IQ tabelite administreerimine.
... industry. Mostly, data marts are presented as an alternative to a data warehouse that takes significantly less time and money to build. However, the term data mart means different things to different people. A rigorous definition of this term is a data store that is subsidiary to a data warehouse of ...
... industry. Mostly, data marts are presented as an alternative to a data warehouse that takes significantly less time and money to build. However, the term data mart means different things to different people. A rigorous definition of this term is a data store that is subsidiary to a data warehouse of ...
extract
... the particular attributes needed for a particular query without also transferring the surrounding attributes. Therefore, for this query, the row-oriented approach will be forced to read in significantly more data, as both the needed attributes and the surrounding attributes stored in the same blocks ...
... the particular attributes needed for a particular query without also transferring the surrounding attributes. Therefore, for this query, the row-oriented approach will be forced to read in significantly more data, as both the needed attributes and the surrounding attributes stored in the same blocks ...
Beyond the Data Model: Designing the Data Warehouse
... In this view, we can now see how a data warehouse flattens, or “denormalizes” data for querying. This model would not work well at all for a transactional system, but will help those queries that are trying to tie large numbers of records together, usually aggregating certain data points along the w ...
... In this view, we can now see how a data warehouse flattens, or “denormalizes” data for querying. This model would not work well at all for a transactional system, but will help those queries that are trying to tie large numbers of records together, usually aggregating certain data points along the w ...
Slide 1
... Databases (cont’d.) • Critical component of information systems – Any type of analysis that’s done is based on data available in the database ...
... Databases (cont’d.) • Critical component of information systems – Any type of analysis that’s done is based on data available in the database ...
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.""