Folie 1
... • GVP is divided in a range of projects and subprojects • e.g. in Phase II „Land Use“ with subprojects L1, L2 etc. • e.g. in Phase III „Analysis of Long-Term Environemental Change“ with the subprojects E1, E2 etc. • with their own processings, models, input and output data (- formats) data flows and ...
... • GVP is divided in a range of projects and subprojects • e.g. in Phase II „Land Use“ with subprojects L1, L2 etc. • e.g. in Phase III „Analysis of Long-Term Environemental Change“ with the subprojects E1, E2 etc. • with their own processings, models, input and output data (- formats) data flows and ...
The SAS System for Data Warehousing
... one-time effort of bu ilding the SAS/Access view descriptors is required. SAS/Access descriptors can be built either interactively or in batch mode. Once built, SAS/Access descriptors need no additional maintenance, unless the fonn of the target data source is altered. Next, a batch job is scheduled ...
... one-time effort of bu ilding the SAS/Access view descriptors is required. SAS/Access descriptors can be built either interactively or in batch mode. Once built, SAS/Access descriptors need no additional maintenance, unless the fonn of the target data source is altered. Next, a batch job is scheduled ...
NoSQL in Action - A New Pathway to Database
... superior because of its highly semantic features and usage. Soon as Big Data stepped into the IT industry, handling large volume of data and variety in data type and structure has lead to a tedious job. To handle such Big Data, relational databases are not suitable because of its strict data constra ...
... superior because of its highly semantic features and usage. Soon as Big Data stepped into the IT industry, handling large volume of data and variety in data type and structure has lead to a tedious job. To handle such Big Data, relational databases are not suitable because of its strict data constra ...
TABLE OF CONTENTS 2. Background and Rationale
... However, before analysis can begin, the first level (creating an accessible database) must be completed. Thus the goal of this thesis is to complete the database. Several problems had to be solved. One was how to deal with diverse data sources. Another problem was digitizing data. Another problem wa ...
... However, before analysis can begin, the first level (creating an accessible database) must be completed. Thus the goal of this thesis is to complete the database. Several problems had to be solved. One was how to deal with diverse data sources. Another problem was digitizing data. Another problem wa ...
OLAP OF THE FUTURE A Min Tjoa , Roland Wagner , Philipp
... In order to achieve interactive response time to queries, pre-materialization of aggregated data is used to construct an independent, redundant database from denormalized data [13, 14]. Pre-materialization attempts to do as much work in advance as possible. All data sources are integrated into a sin ...
... In order to achieve interactive response time to queries, pre-materialization of aggregated data is used to construct an independent, redundant database from denormalized data [13, 14]. Pre-materialization attempts to do as much work in advance as possible. All data sources are integrated into a sin ...
Is the SAS System a Database Management System?
... William D. Clifford, SAS Institute Inc., Austin, TX ABSTRACT ...
... William D. Clifford, SAS Institute Inc., Austin, TX ABSTRACT ...
managing a data warehouse
... What must we back up? First the database itself, but also any other files or links that are a key part of its operation. How do we back it up? The simplest answer is to quiesce the entire data warehouse and do a ‘cold’ backup of the database and related files. This is often not an option as they may ...
... What must we back up? First the database itself, but also any other files or links that are a key part of its operation. How do we back it up? The simplest answer is to quiesce the entire data warehouse and do a ‘cold’ backup of the database and related files. This is often not an option as they may ...
Introducing Microsoft Technologies for Data Storage, Movement and
... Blob storage. Storage for large amounts of unstructured data. Table storage. A NoSQL database, which is an alternative to traditional relational databases. Queue storage. A messaging solution for asynchronous communication between application components, whether they’re running in the cloud, on a co ...
... Blob storage. Storage for large amounts of unstructured data. Table storage. A NoSQL database, which is an alternative to traditional relational databases. Queue storage. A messaging solution for asynchronous communication between application components, whether they’re running in the cloud, on a co ...
Storing Unstructured Data in SQL Server 2008
... in businesses throughout the world; and as it becomes increasingly easy to create digital content, organizations are finding new, innovative ways to use this digital content to improve or extend their business capabilities. While the proliferation of new kinds of unstructured, digital content brings ...
... in businesses throughout the world; and as it becomes increasingly easy to create digital content, organizations are finding new, innovative ways to use this digital content to improve or extend their business capabilities. While the proliferation of new kinds of unstructured, digital content brings ...
chapter 8: online analytical processing(olap)
... → It is more easily used with existing relational DBMS and → The data can be stored efficiently using tables. → Greater scalability • Disadvantages: →Poor query performance.(Proponents of MOLAP model have called ROLAP model as SLOWLAP) • Some products in this category are Oracle OLAP mode and OLAP D ...
... → It is more easily used with existing relational DBMS and → The data can be stored efficiently using tables. → Greater scalability • Disadvantages: →Poor query performance.(Proponents of MOLAP model have called ROLAP model as SLOWLAP) • Some products in this category are Oracle OLAP mode and OLAP D ...
Flexible Data Warehouse Parameters: Toward Building an
... from research to management [11], [12]. In this regard, the organization of the captured design data such as data modeling, normalization, and their aspects that makes it easy to measure the effectiveness of treatment of the relationship between causality and treatment protocols for systemic disease ...
... from research to management [11], [12]. In this regard, the organization of the captured design data such as data modeling, normalization, and their aspects that makes it easy to measure the effectiveness of treatment of the relationship between causality and treatment protocols for systemic disease ...
Data Warehouse - Courses - University of California, Berkeley
... • Time-variant – The data in the warehouse contain a time dimension so that they may be used as a historical record of the business ...
... • Time-variant – The data in the warehouse contain a time dimension so that they may be used as a historical record of the business ...
Is the SAS System a Database Management System?
... DBMS as the data repository and non-DBMS applications as consumers of the data. In a model 5 environment, Ihe DATA step can provide the data to applications in a wide variety of fiat file formats when the original data cannot be read by the applications.. The DATA step can produce multiple different ...
... DBMS as the data repository and non-DBMS applications as consumers of the data. In a model 5 environment, Ihe DATA step can provide the data to applications in a wide variety of fiat file formats when the original data cannot be read by the applications.. The DATA step can produce multiple different ...
Optimizing Data Analysis with a Semi-structured Time Series Database Splunk Inc. Abstract
... amounts of data stored in a Hadoop distributed filesystem (HDFS). Similar to Splunk, Hive uses the MapReduce paradigm, with Hadoop as the job management engine. Hive is designed primarily as a batch processing system and thus queries are not expected to be real-time for the operator. According to th ...
... amounts of data stored in a Hadoop distributed filesystem (HDFS). Similar to Splunk, Hive uses the MapReduce paradigm, with Hadoop as the job management engine. Hive is designed primarily as a batch processing system and thus queries are not expected to be real-time for the operator. According to th ...
CH05_withFigures
... Eleven major tasks that could be performed in parallel for successful implementation of a data warehouse (Solomon, 2005) : ...
... Eleven major tasks that could be performed in parallel for successful implementation of a data warehouse (Solomon, 2005) : ...
Data Warehouse Development
... Eleven major tasks that could be performed in parallel for successful implementation of a data warehouse (Solomon, 2005) : ...
... Eleven major tasks that could be performed in parallel for successful implementation of a data warehouse (Solomon, 2005) : ...
Your Master Data Is a Graph: Are You Ready?
... In traditional data management, we prepare logical and physical data models. The logical data model describes business requirements for a data story and the physical data model specifies how data is to be persisted in a database. In a relational design, we apply a common structure to each instance o ...
... In traditional data management, we prepare logical and physical data models. The logical data model describes business requirements for a data story and the physical data model specifies how data is to be persisted in a database. In a relational design, we apply a common structure to each instance o ...
IR_OLAP - NDSU Computer Science
... particular subject issues by excluding data that are not useful in the decision support process. ...
... particular subject issues by excluding data that are not useful in the decision support process. ...
13_GLP_Data_Integrity_draft
... electronically, system design should always provide for the retention of full audit trails to show all changes to the data while retaining previous and original data. It should be possible to associate all changes to data with the persons making those changes, and changes should be time stamped and ...
... electronically, system design should always provide for the retention of full audit trails to show all changes to the data while retaining previous and original data. It should be possible to associate all changes to data with the persons making those changes, and changes should be time stamped and ...
Using SAS as a Clinical Data Repository
... involved in managing a study database. The database administrator works closely with the other data management specialists working to build the clinical database. The database administrator is responsible for making time critical decisions and must be cognizant of the status of data in various slots ...
... involved in managing a study database. The database administrator works closely with the other data management specialists working to build the clinical database. The database administrator is responsible for making time critical decisions and must be cognizant of the status of data in various slots ...
2nd Semantic Web Mining Workshop at ECML/PKDD
... In the DF problem ontology, for each instance of an object to be classified, the notion of entity identifier ("ID entity") is introduced. This entity identifier plays the role of the primary key of the instance (in analogy with the primary key of a table). For each such identifier, a rule as a compo ...
... In the DF problem ontology, for each instance of an object to be classified, the notion of entity identifier ("ID entity") is introduced. This entity identifier plays the role of the primary key of the instance (in analogy with the primary key of a table). For each such identifier, a rule as a compo ...
DSS Chapter 1
... The main focus is on efficiency of routine tasks A system is designed to address the need of information extraction by providing effectively and efficiently ad hoc analysis of organizational data The main focus is on effectiveness ...
... The main focus is on efficiency of routine tasks A system is designed to address the need of information extraction by providing effectively and efficiently ad hoc analysis of organizational data The main focus is on effectiveness ...
TDWI Checklist Report: Data Requirements for Advanced
... database. The point is to gather data and start analyzing it as soon as possible in reaction to a sudden change in the business environment. Urgency doesn’t allow time and resources for much (if any) data transformation and modeling. So, users make do with operational schema and non-cleansed data by ...
... database. The point is to gather data and start analyzing it as soon as possible in reaction to a sudden change in the business environment. Urgency doesn’t allow time and resources for much (if any) data transformation and modeling. So, users make do with operational schema and non-cleansed data by ...
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.""