In this paper, we discuss the problem of performing impact prediction for changes that occur in the schema/structure of the data warehouse sources. We abstract Extract-Transform-Load (ETL) activities as queries and sequences of views. ETL activities and its sources are uniformly modeled as a graph that is annotated with policies for the management of evolution events. Given a change at an eleme...

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we delve into the logical design of ETL scenarios and provide a generic and customizable framework in order to support the DW designer in his task. First, we present a metamodel partic...

Extract-transform-load (ETL) tools are primarily designed for data warehouse loading, i.e. to perform physical data integration. When the operational data sources happen to change, the data warehouse gets stale. To ensure data timeliness, the data warehouse is refreshed on a periodical basis. The naive approach of simply reloading the data warehouse is obviously inefficient. Typically, only a s...

In Data Warehousing, Extraction-Transformation-Loading (ETL) are the key tasks that are responsible for the extraction of data from several sources, their cleansing, customization and insertion into data warehouse [10]. More specifically ETL tools are category of specialized tools with the task of dealing with data warehouse cleaning and loading problems. These task are very critical in every d...

Data Warehouses bestehen aus zwei Hauptkomponenten: einer flexiblen Anfrageschnittstelle zur Datenanalyse (OLAP) und einer relativ starren ETL-Komponente zum Laden der Daten ins Data Warehouse. In diesem Artikel soll vorgestellt werden, wie die Datenintegration bedarfsabhängig zu flexibilisieren ist, welche Vorteile sich daraus ergeben und welche Herausforderungen bei der Entwicklung eines solc...

This paper presents our first attempts to develop a new methodology for measuring housing deficit at small areas. It combines the advantages of two types of census data: (a) individual-level sample data, which are very useful for depicting many dimensions of the housing deficit, but do not present detailed geographic information; and (b) universal data with detailed spatial resolution (census t...

Both scientific data and business data have analytical needs. Analysis takes place after a scientific data warehouse is eagerly filled with all data from external data sources (repositories). This is similar to the initial loading stage of Extract, Transform, and Load (ETL) processes that drive business intelligence. ETL can also help scientific data analysis. However, the initial loading is a ...

We present our tool for assisting designers in the error-prone and timeconsuming tasks carried out at the early stages of a data warehousing project. Our tool semi-automatically produces multidimensional (MD) and ETL conceptual designs from a given set of business requirements (like SLAs) and data source descriptions. Subsequently, our tool translates both the MD and ETL conceptual designs prod...

The bottleneck of a data warehouse implementation is the ETL (extraction, transformation, and load) process, which carries out the initial population of the data warehouse and its further (usually periodical) updates. There is a number of software products supporting the OLAP analysis. However, the ETL process implementation is not repeatable in a significant way. This paper reports on a resear...

In this article, we apply the mean-expected tail loss (ETL) portfolio optimization to the consensus temporary earnings forecasting (CTEF) data from global equities. The time series model with multivariate normal tempered stable (MNTS) innovations is used to generate the out-of-sample scenarios for the portfolio optimization. We find that (1) the CTEF variable continues to be of value in portfol...