On the integration of autonomous data marts

Cabibbo, L.; Torlone, R.

doi:10.1109/ssdm.2004.1311214

Cited by 12 publications

(14 citation statements)

References 10 publications

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“…In [8] we have shown that the satisfaction of this property prevents inconsistencies between aggregations computed over d and aggregations computed over E(d).…”

Section: An Algebra For Dimensionsmentioning

confidence: 96%

“…Intuitively, two dimensions of different data marts are compatible if their common information is consistent. In a preliminary study [8], we have shown that dimension compatibility gives the ability to correlate, in a correct way, multiple data marts by means of drillacross queries [17], which are basically joins, over common dimensions, of different data marts.…”

Section: Goal and Contributionsmentioning

confidence: 99%

“…DA expressions involving only projections and aggregations are always lossless [8]. On the other hand, if a DA expression involves selections, the lossless property can fail to hold: it depends on the particular sets of elements chosen to perform the selections.…”

Section: An Algebra For Dimensionsmentioning

confidence: 99%

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Two approaches to the integration of heterogeneous data warehouses

Torlone

2007

Distrib Parallel Databases

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In this paper we address the problem of integrating independent and possibly heterogeneous data warehouses, a problem that has received little attention so far, but that arises very often in practice.We start by tackling the basic issue of matching heterogeneous dimensions and provide a number of general properties that a dimension matching should fulfill. We then propose two different approaches to the problem of integration that try to enforce matchings satisfying these properties. The first approach refers to a scenario of loosely coupled integration, in which we just need to identify the common information between data sources and perform join operations over the original sources. The goal of the second approach is the derivation of a materialized view built by merging the sources, and refers to a scenario of tightly coupled integration in which queries are performed against the view.We also illustrate architecture and functionality of a practical system that we have developed to demonstrate the effectiveness of our integration strategies.

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“…In [8] we have shown that the satisfaction of this property prevents inconsistencies between aggregations computed over d and aggregations computed over E(d).…”

Section: An Algebra For Dimensionsmentioning

confidence: 96%

Section: Goal and Contributionsmentioning

confidence: 99%

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Two approaches to the integration of heterogeneous data warehouses

Torlone

2007

Distrib Parallel Databases

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“…Having dimension tables that are shareable is crucial because it will provide the capability of each data mart integrated with and connected to each other [10]. In particular, the integration of data marts will rely on a common variation of the dimensions of the existing used together at each data marts as a reference in the OLAP reporting will be done.…”

Section: Dimension Sharing Integrationmentioning

confidence: 99%

“…Bottom-Up Data Warehouse coupled with Bus Scheme for the integration and transformation of data into the data mart will be a practical solution when the company did not know the structure of future operational systems and operational systems in each section constructed independently (autonomous) [10].…”

Section: Introductionmentioning

confidence: 99%

Integration Scheme of Data Marts using Dimension Sharing Method

Sukarsa¹,

Buana²,

Quidar³

et al. 2016

IJSEIA

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Business is an entity that produces big data nowadays. A business scope, for example, a company will divide its activities part into a new body which will be focused on another specific field. The implementation warehouse data in company with that criteria will be favorable for the party that makes a decision since they get brief data to ease decision making which is strategic for the enterprise. Demand for the vital information creates a problem when the company has no draft of how the company condition in the future specifically in implementation the Management of Information System that will be built. The solution given is by creating a Bottom-Up Bus Data Warehouse Architecture by applying dimension sharing concept which will integrate all data marts. In realizing that solution needs an application that has the main feature that integrating data mart and does (Online Analytical Processing) as presentation information media for the end user.

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