Approach for testing the extract-transform-load process in data warehouse systems, An

2018 Spring.Includes bibliographical references.Enterprises use data warehouses to accumulate data from multiple sources for data analysis and research. Since organizational decisions are often made based on the data stored in a data warehouse, all its components must be rigorously tested. In this thesis, we first present a comprehensive survey of data warehouse testing approaches, and then develop and evaluate an automated testing approach for validating the Extract-Transform-Load (ETL) process, which is a common activity in data warehousing. In the survey we present a classification framework that categorizes the testing and evaluation activities applied to the different components of data warehouses. These approaches include both dynamic analysis as well as static evaluation and manual inspections. The classification framework uses information related to what is tested in terms of the data warehouse component that is validated, and how it is tested in terms of various types of testing and evaluation approaches. We discuss the specific challenges and open problems for each component and propose research directions. The ETL process involves extracting data from source databases, transforming it into a form suitable for research and analysis, and loading it into a data warehouse. ETL processes can use complex one-to-one, many-to-one, and many-to-many transformations involving sources and targets that use different schemas, databases, and technologies. Since faulty implementations in any of the ETL steps can result in incorrect information in the target data warehouse, ETL processes must be thoroughly validated. In this thesis, we propose automated balancing tests that check for discrepancies between the data in the source databases and that in the target warehouse. Balancing tests ensure that the data obtained from the source databases is not lost or incorrectly modified by the ETL process. First, we categorize and define a set of properties to be checked in balancing tests. We identify various types of discrepancies that may exist between the source and the target data, and formalize three categories of properties, namely, completeness, consistency, and syntactic validity that must be checked during testing. Next, we automatically identify source-to-target mappings from ETL transformation rules provided in the specifications. We identify one-to-one, many-to-one, and many-to-many mappings for tables, records, and attributes involved in the ETL transformations. We automatically generate test assertions to verify the properties for balancing tests. We use the source-to-target mappings to automatically generate assertions corresponding to each property. The assertions compare the data in the target data warehouse with the corresponding data in the sources to verify the properties. We evaluate our approach on a health data warehouse that uses data sources with different data models running on different platforms. We demonstrate that our approach can find previously undetected real faults in the ETL implementation. We also provide an automatic mutation testing approach to evaluate the fault finding ability of our balancing tests. Using mutation analysis, we demonstrated that our auto-generated assertions can detect faults in the data inside the target data warehouse when faulty ETL scripts execute on mock source data

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining

Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies

Business Intelligence plays an important role in decision making. Based on data warehouses and Online Analytical Processing, a business intelligence tool can be used to analyze complex data. Still, summarizability issues in data warehouses cause ineffective analyses that may become critical problems to businesses. To settle this issue, many researchers have studied and proposed various solutions, both in relational and XML data warehouses. However, they find difficulty in evaluating the performance of their proposals since the available benchmarks lack complex hierarchies. In order to contribute to summarizability analysis, this paper proposes an extension to the XML warehouse benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate XML data warehouses with scalable complex hierarchies as well as summarizability processing. We experimentally demonstrated that complex hierarchies can definitely be included into a benchmark dataset, and that our benchmark is able to compare two alternative approaches dealing with summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP 2012), Maui : United States (2012

Improving lifecycle query in integrated toolchains using linked data and MQTT-based data warehousing

The development of increasingly complex IoT systems requires large engineering environments. These environments generally consist of tools from different vendors and are not necessarily integrated well with each other. In order to automate various analyses, queries across resources from multiple tools have to be executed in parallel to the engineering activities. In this paper, we identify the necessary requirements on such a query capability and evaluate different architectures according to these requirements. We propose an improved lifecycle query architecture, which builds upon the existing Tracked Resource Set (TRS) protocol, and complements it with the MQTT messaging protocol in order to allow the data in the warehouse to be kept updated in real-time. As part of the case study focusing on the development of an IoT automated warehouse, this architecture was implemented for a toolchain integrated using RESTful microservices and linked data.Comment: 12 pages, worksho