A Logical Model for Multiversion Data Warehouses

Ahmed, Waqas; Zimányi, Esteban; Wrembel, Robert

doi:10.1007/978-3-319-10160-6_3

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…In [33], the authors attempt to update DW Schema to reflect modifications that already took place. As mentioned in [33], [33], [34], [5], [35], [36] , the authors use temporal DW and schema versions to update the DWs Structure by keeping more than one DWs version. These works do not depict how the user's needs impact the evolution changes.…”

Section: F Apache Spark and Delta Lakementioning

confidence: 99%

Lake Data Warehouse Architecture for Big Data Solutions

Saddad¹,

El-Bastawissy²,

Mokhtar³

et al. 2020

IJACSA

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Traditional Data Warehouse is a multidimensional repository. It is nonvolatile, subject-oriented, integrated, timevariant, and non-operational data. It is gathered from multiple heterogeneous data sources. We need to adapt traditional Data Warehouse architecture to deal with the new challenges imposed by the abundance of data and the current big data characteristics, containing volume, value, variety, validity, volatility, visualization, variability, and venue. The new architecture also needs to handle existing drawbacks, including availability, scalability, and consequently query performance. This paper introduces a novel Data Warehouse architecture, named Lake Data Warehouse Architecture, to provide the traditional Data Warehouse with the capabilities to overcome the challenges. Lake Data Warehouse Architecture depends on merging the traditional Data Warehouse architecture with big data technologies, like the Hadoop framework and Apache Spark. It provides a hybrid solution in a complementary way. The main advantage of the proposed architecture is that it integrates the current features in traditional Data Warehouses and big data features acquired through integrating the traditional Data Warehouse with Hadoop and Spark ecosystems. Furthermore, it is tailored to handle a tremendous volume of data while maintaining availability, reliability, and scalability.

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Section: F Apache Spark and Delta Lakementioning

confidence: 99%

Lake Data Warehouse Architecture for Big Data Solutions

Saddad¹,

El-Bastawissy²,

Mokhtar³

et al. 2020

IJACSA

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“…Most of the past research on temporal data warehousing concentrates on changes in the dimension tables, often referred to as slowly changing dimensions (Kimball and Ross, 2013;Faisal and Sarwar, 2014), and on the evolution of data warehouse schemas (Blaschka et al, 1999;Wrembel and Bebel, 2007;Ahmed et al, 2014). Kimball and Ross (2013) proposed for the first time three basic techniques for representing changing attributes in the dimension tables, together with five variations thereof.…”

Section: Related Workmentioning

confidence: 99%

“…There has been extensive research on dealing with various aspects of temporal information in data warehouses, often referred to as temporal data warehousing; see the works of Golfarelli and Rizzii (2009b;2011) for an overview. Most of the past research on temporal data warehousing concentrates on changes in the dimension tables, often referred to as slowly changing dimensions (Jensen et al, 2010;Kimball and Ross, 2013;Faisal and Sarwar, 2014) and on the evolution of data warehouse schemas (Blaschka et al, 1999;Wrembel and Bebel, 2007;Ahmed et al, 2014). How to model and represent changes in the fact data has been less studied, with a few exceptions (e.g., Bliujute et al, 1998;Goller and Berger, 2015), but none of them investigates data warehouse scenarios with aggregation queries over time.…”

Section: Introductionmentioning

confidence: 99%

Modeling and querying facts with period timestamps in data warehouses

Mahlknecht

Dignös

Kozmina

2019

International Journal of Applied Mathematics and Computer Science

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In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on how to represent the time periods that are associated with the facts in order to support convenient and efficient aggregations over time. We propose three distinct logical models that represent time periods as sets of all time points in a period (instant model), as pairs of start and end time points of a period (period model), and as atomic units that are explicitly stored in a new period dimension (period∗ model). The period dimension is enriched with information about the days of each period, thereby combining the former two models. We use four different classes of aggregation queries to analyze query formulation, query execution, and query performance over the three models. An extensive empirical evaluation on synthetic and real-world datasets and the analysis of the query execution plans reveal that the period model is the best choice in terms of runtime and space for all four query classes.

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“…There are in general two approaches to solving evolution problems. One approach is to adapt just the existing data warehouse schema (Bentayeb et al, 2008) or ETL processes (Wojciechowski, 2018) without keeping the history of changes and another approach (Ahmed et al, 2014;Golfarelli et al, 2006;Malinowski and Zimnyi, 2008) is to maintain multiple versions of schema that are valid during some period of time. Futhermore, several studies (Thakur and Gosain, 2011;Thenmozhi and Vivekanandan, 2014;Solodovnikova et al, 2015) propose solutions to the formalization of requirements of a data warehouse and treatment of their evolution.…”

Section: Introductionmentioning

confidence: 99%

Handling Evolution in Big Data Architectures

Solodovnikova¹,

Niedrite²

2020

BJMC

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One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used to achieve the same goal, they could be leveraged also to provide analysis of Big Data. The problem of adapting data warehouse data and schemata to changes in user requirements and data sources has been studied by many researchers worldwide. However, innovative methods must be developed also to support evolution in Big Data systems. In this paper, we analyze architectures designed for Big Data processing and analysis described in the literature with the purpose to identify the most appropriate solution for the evolution problem. We concentrate on four architecture types: data lakes, virtual integration, polystores, and λ-architecture, and, in addition to them, we consider solutions that apply data warehouse/OLAP methods to Big Data processing and analysis. Finally, we describe our proposal of an architecture that allows to perform different kinds of analytical tasks on Big Data retrieved from multiple heterogeneous data sources with different latency and is capable of processing changes in data sources as well as evolving analysis requirements.

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A Logical Model for Multiversion Data Warehouses

Cited by 10 publications

References 11 publications

Lake Data Warehouse Architecture for Big Data Solutions

Lake Data Warehouse Architecture for Big Data Solutions

Modeling and querying facts with period timestamps in data warehouses

Handling Evolution in Big Data Architectures

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