Data Integration .

• Data Integration

https://store.theartofservice.com/the-data-integration-toolkit.html

Data fusion Data integration

1 In applications outside of the geospatial domain, differences in the usage of the terms

Data integration and Data fusion apply. In areas such as business intelligence, for

example, data integration is used to describe the combining of data, whereas data fusion is

integration followed by reduction or replacement. Data integration might be

viewed as set combination wherein the larger set is retained, whereas fusion is a set

reduction technique with improved confidence.


Data integration

1 In management circles, people frequently refer to data integration

as "Enterprise Information Integration" (EII).


Data integration History

1 As of 2009 the trend in data integration has favored loosening the coupling between data and providing

a unified query-interface to access real time data over a mediated

schema (see figure 2), which allows information to be retrieved directly

from original databases


Data integration History

1 This approach represents ontology-based data

integration


Data integration Theory of data integration

1 The theory of data integration forms a subset of database theory and

formalizes the underlying concepts of the problem in first-order logic.

Applying the theories gives indications as to the feasibility and

difficulty of data integration. While its may appear abstract, they have

sufficient generality to accommodate all manner of integration systems.


Data integration Definitions

1 When users pose queries over the data integration system, they pose queries over and the mapping then

asserts connections between the elements in the global schema and

the source schemas.



1 The burden of complexity falls on implementing mediator code

instructing the data integration system exactly how to retrieve

elements from the source databases



1 In a GAV approach to the example data integration system above, the system designer would first develop

mediators for each of the city information sources and then design

the global schema around these mediators



1 In an LAV approach to the example data integration system above, the system designer designs the global schema first and then simply inputs the schemas of the respective city

information sources


Data integration Query processing

1 The theory of query processing in data integration systems is commonly expressed using

conjunctive queries and Datalog, a purely declarative logic programming

language



1 In terms of data integration, "query containment" represents an

important property of conjunctive queries



1 In LAV systems, queries undergo a more radical process of rewriting because no mediator exists

to align the user's query with a simple expansion strategy. The integration system

must execute a search over the space of possible queries in order to find the best

rewrite. The resulting rewrite may not be an equivalent query but maximally contained, and the resulting tuples may be incomplete. As of

2009 the MiniCon algorithm is the leading query rewriting algorithm for LAV data integration

systems.


Data integration Data Integration in the Life Sciences

1 National Science Foundation initiatives such as Datanet are

intended to make data integration easier for scientists by providing cyberinfrastructure and setting

standards


Data integration Further reading

1 Ronald Schuldt (November 15, 2011). UDEF – Six Steps to Cost Effective

Data Integration. CreateSpace. ISBN 978-1-4664-6762-0.


Customer data integration

1 In data processing, 'customer data integration' ('CDI') combines the technology, processes

and services needed to set up and maintain an accurate, timely, complete and comprehensive representation of a customer across multiple channels, business-lines, and enterprises — typically from multiple sources of associated

data in multiple application systems and databases. It applies data integration|data-integration techniques in this specific area.


Customer data integration - Techniques for managing complexity

1 # management – data integration, governance, stewardship, operations and distribution all combine to make-

or-break data-value


Customer data integration - History of customer data integration

1 In the late 1990s Acxiom and GartnerGroup coined the term

customer data integration (CDI). The process of CDI, as Acxiom and Gartner described it, includes:


Customer data integration - History of customer data integration

1 , service providers deliver CDI as a hosted solution in batch volumes, on

demand using a software as a service (SaaS) model, or on-site as licensed software in companies and organizations with the resources to

drive their own data integration processing


Pentaho Data Integration

1 It offers a suite of open source Business Intelligence (BI) products called Pentaho Business Analytics providing data integration, OLAP|

OLAP services, reporting, Dashboards (management information systems)|

dashboarding, data mining and Extract, transform, load|ETL

capabilities. Pentaho is headquartered in Orlando, FL, USA.


Pentaho Data Integration - Social Media Communication

1 * 'Matt Casters', founder and developer of Pentaho Data Integration (PDI/Kettle)Matt

Casters, [ http://www.ibridge.be/?page_id=2 matt casters on data integration] Retrieved July 27, 2012 and author of the book Pentaho Kettle SolutionsMatt Casters, Bouman, Dongen, Wiley [

http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470635177.html Pentaho Kettle Solutions:

Building Open Source ETL Solutions with Pentaho Data Integration] September 2010


Data integration

1 'Data integration' involves combining data residing in different sources and providing users with a

unified view of these data.


Data integration

1 In management circles, people frequently refer to data integration

as Enterprise Information Integration (EII).


Data integration - History

1 the trend in data integration has favored loosening the coupling

between data and providing a unified query-interface to access real time

data over a data mediation|mediated schema (see figure 2), which allows information to be retrieved directly

from original databases


Data integration - History

1 This approach represents ontology based data integration|ontology-based data

integration


Data integration - Example

1 These adapters simply transform the local query results (those returned by

the respective websites or databases) into an easily processed

form for the data integration solution (see figure 2)


Data integration - Theory of data integration

1 The theory of data integration forms a subset of database theory and

formalizes the underlying concepts of the problem in first-order logic.

Applying the theories gives indications as to the feasibility and difficulty of data integration. While its definitions may appear abstract, they have sufficient generality to

accommodate all manner of integration systems.


Data integration - Definitions

1 When users pose queries over the data integration system, they pose

queries over G and the mapping then asserts connections between the

elements in the global schema and the source schemas.



1 The burden of complexity falls on implementing mediator code

instructing the data integration system exactly how to retrieve

elements from the source databases



1 In a GAV approach to the example data integration system above, the system designer would first develop

mediators for each of the city information sources and then design

the global schema around these mediators



1 In an LAV approach to the example data integration system above, the system designer designs the global schema first and then simply inputs the schemas of the respective city

information sources


Data integration - Query processing

1 The theory of query processing in data integration systems is commonly expressed using conjunctive Database query

language|queries and Datalog, a purely declarative logic programming

language



1 In terms of data integration, query containment represents an important

property of conjunctive queries



1 In LAV systems, queries undergo a more radical process of rewriting because no mediator exists

to align the user's query with a simple expansion strategy. The integration system

must execute a search over the space of possible queries in order to find the best

rewrite. The resulting rewrite may not be an equivalent query but maximally contained, and the resulting tuples may be incomplete. the

MiniCon algorithm is the leading query rewriting algorithm for LAV data integration systems.


Ontology based data integration

1 'Ontology based Data Integration' involves the use of ontology (computer

science)|ontology(s) to effectively combine data or information from multiple

heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology based data

integration is closely tied to the consistency and expressivity of the ontology used in the

integration process.https://store.theartofservice.com/the-data-integration-toolkit.html

Ontology based data integration - Background

1 Data from multiple sources are characterized by multiple types of

heterogeneity. The following hierarchy is often

used:[http://daks.ucdavis.edu/~ludaesch/Paper/AHM02/tutorial5.html

AHM02 Tutorial 5: Data Integration and Mediation; Contributors: B.

Ludaescher, I. Altintas, A. Gupta, M. Martone, R. Marciano, X. Qian]


Ontology based data integration - Background

1 In domains like bioinformatics and biomedicine, the rapid development,

adoption and public availability of ontologies

[http://www.bioontology.org/repositories.html#obo] has made it possible for the data integration community

to leverage them for semantic integration of data and information.


Ontology based data integration - Approaches using ontologies for data Integration

1 There are three main architectures that are implemented in ontology-

based data integration applications, namely,


Core data integration

1 'Core data integration' is the use of data integration technology for a significant, centrally planned and

managed IT initiative within a company. Examples of core data

integration initiatives could include:



1 Core data integrations are often designed to be enterprise-wide

integration solutions. They may be designed to provide a data

abstraction layer, which in turn will be used by individual core data

integration implementations, such as ETL servers or applications

integrated through EAI.



1 Because it is difficult to promptly roll out a centrally managed data integration solution

that anticipates and meets all data integration requirements across an organization, IT

engineers and even business users create edge data integration, using technology that may be incompatible with that used at the

core. In contrast to a core data integration, an edge data integration is not centrally planned

and is generally completed with a smaller budget and a tighter deadline.


Edge data integration

1 Many edge integrations, and actually the vast majority of all data

integration, involves hand-coded scripts


Edge data integration

1 It has been claimed that edge data integration do not typically require

large budgets and centrally managed technologies, which is in contrast to

a core data integration.


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Data Integration .

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