DAHLIA: A visual analyzer of database schema evolution

Meurice, Loup; Cleve, Anthony

doi:10.1109/csmr-wcre.2014.6747219

Cited by 18 publications

(4 citation statements)

References 2 publications

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“…The types of changes we accommodate are addition (ie, extension), deletion (ie, obsoleting), and renaming (ie, refining) of components in domain ontologies and service ontologies [ 40 , 41 ], such as concepts, property restrictions, individuals, and axioms. Changes in the source data, such as tables and their attributes, data types, and indices, can also be reported [ 42 ]. Likewise, changes to saved queries and the corresponding service dependencies can be detected.…”

Section: Methodsmentioning

confidence: 99%

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Manir¹,

Brenas²,

Baker³

et al. 2018

JMIR Public Health Surveill

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BackgroundAccording to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions.ObjectiveWe propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities.MethodsWe used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services.ResultsWe developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime.ConclusionsWe introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art.

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Section: Methodsmentioning

confidence: 99%

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Manir¹,

Brenas²,

Baker³

et al. 2018

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…Database refactoring, or some may refer to as schema evolution, has been largely studied over the past years [4], [8], [27]. Schema evolution is basically altering the schema of the database to adapt it to a certain required change [4].…”

Section: Database Refactoringmentioning

confidence: 99%

“…Schema evolution is basically altering the schema of the database to adapt it to a certain required change [4]. Attempts have been made to analyze the schema evolution from earlier versions [8], and tools have been developed to automate the evolution process [27]. However, schema evolution literature has focused on limited database refactoring tactics, such as adding or renaming a column, changing the data type, etc.…”

Section: Database Refactoringmentioning

confidence: 99%

Prioritizing technical debt in database normalization using portfolio theory and data quality metrics

Albarak

Bahsoon

2018

Proceedings of the 2018 International Conference on Technical Debt

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Database normalization is the one of main principles for designing relational databases. The benefits of normalization can be observed through improving data quality and performance, among the other qualities. We explore a new context of technical debt manifestation, which is linked to ill-normalized databases. This debt can have long-term impact causing systematic degradation of database qualities. Such degradation can be liken to accumulated interest on a debt. We claim that debts are likely to materialize for tables below the fourth normal form. Practically, achieving fourth normal form for all the tables in the database is a costly and idealistic exercise. Therefore, we propose a pragmatic approach to prioritize tables that should be normalized to the fourth normal form based on the metaphoric debt and interest of the ill-normalized tables, observed on data quality and performance. For data quality, tables are prioritized using the risk of data inconsistency metric. Unlike data quality, a suitable metric to estimate the impact of weakly or un-normalized tables on performance is not available. We estimate performance degradation and its costs using Input\Output (I\O) cost of the operations performed on the tables and we propose a model to estimate this cost for each table. We make use of Modern Portfolio Theory to prioritize tables that should be normalized based on the estimated I\O cost and the likely risk of cost accumulation in the future. To evaluate our methods, we use a case study from Microsoft, AdventureWorks. The results show that our methods can be effective in reducing normalization debt and improving the quality of the database.

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“…In contrast, our approach focuses on the monitoring and visualization of databases and applications, which communicate with them, to facilitate software landscape and database comprehension. DAHLIA [MC14] is an interactive visualization tool, which facilitates to analyze the database usage to support software and database schema evolution. The tool collects snapshots of database schemas from a software repository based on static analysis and utilizes a 3D visualization for exploring the monitored evolution.…”

Section: Database Visualizationmentioning

confidence: 99%

Collaborative Reengineering and Modularization of Software Systems

Zirkelbach¹

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Software systems evolve over their lifetime. Changing requirements make it inevitable to modify and extend the underlying source code. Understanding software systems embodies a crucial task, which needs to be addressed in an appropriate way to face inevitable challenges while performing software changes. In this thesis, we introduce three complementary approaches to support the evolution and particularly understanding of software systems in different aspects. Our main contributions are (i) an approach named CORAL for enabling collaborative reengineering and modularization of software systems, (ii) a gesture-based, collaborative, and multi-user-featuring Virtual Reality approach named ExplorViz VR for the software city metaphor, and (iii) a database behavior live-visualization approach named RACCOON for database comprehension of software systems. An extensive case study shows that our CORAL approach is capable of supporting reengineering and modularization processes. Furthermore, several evaluations demonstrate the high usability, and efficiency and effectiveness for solving comprehension tasks when using our multi-user VR approach ExplorViz VR.

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DAHLIA: A visual analyzer of database schema evolution

Cited by 18 publications

References 2 publications

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Prioritizing technical debt in database normalization using portfolio theory and data quality metrics

Collaborative Reengineering and Modularization of Software Systems

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