HISOverview
... • Hydrologic Observations Database design works and is ready for independent review and testing • Automated data harvesting may produce a “one stop shop” for observational data • There will never be enough database fields to describe all we want to know about data • We need a hybrid database—files s ...
... • Hydrologic Observations Database design works and is ready for independent review and testing • Automated data harvesting may produce a “one stop shop” for observational data • There will never be enough database fields to describe all we want to know about data • We need a hybrid database—files s ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... detection needs to solve an unsupervised yet unbalanced data learning problem. Similarly, we observe that removing (or adding) an abnormal data instance will affect the principal direction of the resulting data than removing (or adding) a normal one does. Using the above “leave one out” (LOO) strate ...
... detection needs to solve an unsupervised yet unbalanced data learning problem. Similarly, we observe that removing (or adding) an abnormal data instance will affect the principal direction of the resulting data than removing (or adding) a normal one does. Using the above “leave one out” (LOO) strate ...
Alexander Nikov
... 1. Organizing Data in a Traditional File Environment 2. The Database Approach to Data Management 3. Using Databases to Improve Business Performance and ...
... 1. Organizing Data in a Traditional File Environment 2. The Database Approach to Data Management 3. Using Databases to Improve Business Performance and ...
A prototype of Quality Data Warehouse in Steel Industry
... to investigate the relationships between product properties and sold/refused products. To solve these tasks it is on one hand necessary to handle information and data factory in a wide form and on the other hand to have powerful tools to analyse and interpret them. The work describes a Quality Data ...
... to investigate the relationships between product properties and sold/refused products. To solve these tasks it is on one hand necessary to handle information and data factory in a wide form and on the other hand to have powerful tools to analyse and interpret them. The work describes a Quality Data ...
Data Management in the Cloud
... – hard to support multiple, distributed updaters to the same data set – hard to replicate huge data sets for availability, due to capacity (storage, network bandwidth, …) – storage: different transactional implementation techniques, different storage semantics, or both – query processing and optimiz ...
... – hard to support multiple, distributed updaters to the same data set – hard to replicate huge data sets for availability, due to capacity (storage, network bandwidth, …) – storage: different transactional implementation techniques, different storage semantics, or both – query processing and optimiz ...
Data Warehousing and CANDA Concepts for Biometry
... with him, Apollo put a curse on her which made it impossible for her to convince anyone of the truth of her prophecies. Thus, she predicted the fall of Troy and warned of the wooden horse. She foresaw her own death and that of Agamemnon whose slave she had became after the fall of Troy. But all was ...
... with him, Apollo put a curse on her which made it impossible for her to convince anyone of the truth of her prophecies. Thus, she predicted the fall of Troy and warned of the wooden horse. She foresaw her own death and that of Agamemnon whose slave she had became after the fall of Troy. But all was ...
Customizing Clinical Data Warehouse for Business Intelligence
... Often, clinical data warehouses can achieve massive size. To obtain business or clinical insight from a diversified data warehouse in a timely manner, what is required is a user-friendly interface, a good understanding of medical data and clinical practices and practical knowledge about common data ...
... Often, clinical data warehouses can achieve massive size. To obtain business or clinical insight from a diversified data warehouse in a timely manner, what is required is a user-friendly interface, a good understanding of medical data and clinical practices and practical knowledge about common data ...
Building a Data Warehouse with SAS Software in the UNIX Environment
... The complexity of the database design just described, translates to complexity in the SAS code required to retrieve data. As an application developer or business user wishing to retrieve data, you would have to know, for each element you wanted to extract, whether it is in a main segment or a suppor ...
... The complexity of the database design just described, translates to complexity in the SAS code required to retrieve data. As an application developer or business user wishing to retrieve data, you would have to know, for each element you wanted to extract, whether it is in a main segment or a suppor ...
C. Development of custom open system architecture
... database to get power system network model, parameters and other information and DCD Server which provides real time data from external sources and other components and allows calculated data to be published and available to other system components. Applications can connect to System model database ...
... database to get power system network model, parameters and other information and DCD Server which provides real time data from external sources and other components and allows calculated data to be published and available to other system components. Applications can connect to System model database ...
Data Sheet
... Transparent Data Encryption deploys easily and installs by default as part of the database installation. Existing tablespaces can be encrypted online with zero downtime on production systems or encrypted offline with no storage overhead during a maintenance period. Additionally, Transparent Data Enc ...
... Transparent Data Encryption deploys easily and installs by default as part of the database installation. Existing tablespaces can be encrypted online with zero downtime on production systems or encrypted offline with no storage overhead during a maintenance period. Additionally, Transparent Data Enc ...
Document
... Poor security: • Because there is little control or management of data, management will have no knowledge of who is accessing or even making changes to the organization’s data. Lack of data sharing and availability: • Information cannot flow freely across different functional areas or different part ...
... Poor security: • Because there is little control or management of data, management will have no knowledge of who is accessing or even making changes to the organization’s data. Lack of data sharing and availability: • Information cannot flow freely across different functional areas or different part ...
Original article Damming the genomic data flood
... model description metadata. These structures are more suited to providing data context and long-term storage than high-speed analysis (8), although some allow basic analytical querying (9). Recent data indexing efforts seek to improve pattern or sequence search performance. While these have shown a ...
... model description metadata. These structures are more suited to providing data context and long-term storage than high-speed analysis (8), although some allow basic analytical querying (9). Recent data indexing efforts seek to improve pattern or sequence search performance. While these have shown a ...
business intelligence and nosql databases
... owes its popularity also to the universal, simple, but very powerful SQL language. This language, originally developed in IBM, allows a wide range of people, including non-programmers like managers, analysts and other decision makers to easy interact with data collected in databases. The originally ...
... owes its popularity also to the universal, simple, but very powerful SQL language. This language, originally developed in IBM, allows a wide range of people, including non-programmers like managers, analysts and other decision makers to easy interact with data collected in databases. The originally ...
HCLSIG_BioRDF_Subgroup$SW_eNeuroscience_HCLSIG2
... against multiple RDF datasets • The following two examples illustrate how such queries can be made to retrieve and integrate data from BrainPharm and SWAN ...
... against multiple RDF datasets • The following two examples illustrate how such queries can be made to retrieve and integrate data from BrainPharm and SWAN ...
Implementation of Object Oriented Data Warehousing using a
... associated with unique identifier, a set of attributes and a set of procedures. There could be no. of data types such as atomic or any other class. Object Oriented Data warehousing, like other areas of Information Technology, is a field in the midst of change. The current systems integration approac ...
... associated with unique identifier, a set of attributes and a set of procedures. There could be no. of data types such as atomic or any other class. Object Oriented Data warehousing, like other areas of Information Technology, is a field in the midst of change. The current systems integration approac ...
JDeveloper 10g and Oracle ADF Business Components
... • View attributes don’t need to map to entity attributes at all • Then they must be read-only • E.g.: Caclulated query columns ...
... • View attributes don’t need to map to entity attributes at all • Then they must be read-only • E.g.: Caclulated query columns ...
How Data Integration Works Introduction to How Data Integration Works 8/24/2014
... In general, queries to a data warehouse take very little time to resolve. That's because the data warehouse has already done the major work of extracting, converting and combining data. The user's side of a data warehouse is called the front end, so from a front-end standpoint, data warehousing is a ...
... In general, queries to a data warehouse take very little time to resolve. That's because the data warehouse has already done the major work of extracting, converting and combining data. The user's side of a data warehouse is called the front end, so from a front-end standpoint, data warehousing is a ...
Available Online through www.ijptonline.com
... contain missing data with rather naive way. Missing data treatment should be carefully handled otherwise some portion error might be introduced into the knowledge induced. Analyze the use of nearest neighbor as an imputation method. Imputation Which denotes a procedure that replaces the missing valu ...
... contain missing data with rather naive way. Missing data treatment should be carefully handled otherwise some portion error might be introduced into the knowledge induced. Analyze the use of nearest neighbor as an imputation method. Imputation Which denotes a procedure that replaces the missing valu ...
What's All This Metadata Good For, Anyway? Using Metadata to Dynamically Generate SQL
... the code (SQL) to create a table called ______, that contains Data Items A, B, and C, using constraints C = ‘Y’ and D < 5” A, B, C, and D are all Data Items which, in the metadata, point to their respective columns within Data Sources. The calling application does not need to know which Data Sources ...
... the code (SQL) to create a table called ______, that contains Data Items A, B, and C, using constraints C = ‘Y’ and D < 5” A, B, C, and D are all Data Items which, in the metadata, point to their respective columns within Data Sources. The calling application does not need to know which Data Sources ...
EXCERPT Westpac`s Journey into Big Data: From
... While for hyper-connected enterprises, the definition of internal and external data is getting blurred, some banks prefer to start off their Big Data journey by looking at their internal-unstructured data sources (e.g., call center data logs, relationshipmanagement data, etc.) before they venture in ...
... While for hyper-connected enterprises, the definition of internal and external data is getting blurred, some banks prefer to start off their Big Data journey by looking at their internal-unstructured data sources (e.g., call center data logs, relationshipmanagement data, etc.) before they venture in ...
Design of a Multi Dimensional Database for the Archimed - magic-5
... issue since that it will affects the entire architecture of the data warehouse environment and more specifically the volume of the database and the type of query that can be answered. It is generally recognized that expressing the data at a low level of granularity will help to achieve a robust data ...
... issue since that it will affects the entire architecture of the data warehouse environment and more specifically the volume of the database and the type of query that can be answered. It is generally recognized that expressing the data at a low level of granularity will help to achieve a robust data ...
Data Modeling
... A conceptual data model identifies the highest-level relationships between the different entities. Features of conceptual data model include: ...
... A conceptual data model identifies the highest-level relationships between the different entities. Features of conceptual data model include: ...
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.""