Schema Evolution publication categorizer

There is a new generation of databases specifically addressing Big Data Variety: Multi-model databases store and process structurally heterogeneous data, managing several data models in one integrated backend. Yet one of the many challenges these systems face is evolution management. In our demonstration, we present our prototype implementation of a tool called MM-evolver. MM-evolver carries out user-triggered schema modification operations over a multi-model database, and propagates them across all models.

During the development of NoSQL-backed software, the database schema evolves naturally alongside the application code. Especially in agile development, new application releases are deployed frequently. Eventually, decisions have to be made regarding the migration of versioned legacy data which is persisted in the cloud-hosted production database.

A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications.

The continuous evolution of life science ontologies requires the adaptation of their associated mappings. We propose two approaches for tackling this problem in a largely automatic way: (1) a composition-based adaptation relying on the principle of mapping composition and (2) a diff-based adaptation algorithm individually handling change operations to update the mapping. Both techniques reuse unaffected correspondences, and adapt only the affected mapping part.

The Linked Open Data community publishes an increasing number of data sources on the so-called Data Web and interlinks them to support data integration applications. We investigate how the composition of existing links and mappings can
help discovering new links and mappings between LOD sources. Often there will be many alternatives for composition so that the problem arises which paths can provide the best linking results with the least computation effort. We therefore investigate

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings.

Background
Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results

There is an increasing need to interconnect biomedical ontologies. We investigate a simple but promising approach to generate mappings between ontologies by reusing and composing existing mappings across intermediate ontologies. Such an approach is especially promising for highly interconnected ontologies such as in the life science domain. There may be many ontologies that can be used for composition so that the problem arises to find the most suitable ones providing the best results.

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings.

Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology versions. Such a Diff supports the synchronization of changes in collaborative curation, the adaptation of dependent data such as annotations, and ontology version management.

There is an increasing need to interrelate different life science ontologies in order to facilitate data integration or semantic data analysis. Ontology matching aims at a largely automatic generation of mappings between ontologies mostly by calculating the linguistic and structural similarity of their concepts. In this paper we investigate an indirect computation of ontology mappings that composes and thus reuses previously determined ontology mappings that involve intermediate ontologies. The composition approach promises a fast computation of new mappings with reduced manual effort.

Matching life science ontologies to determine ontology mappings has recently become an active field of research. The large size of existing ontologies and the application of complex match strategies for obtaining high quality mappings makes ontology matching a resource- and time-intensive process. To improve performance we investigate different approaches for parallel matching on multiple compute nodes. In particular, we consider inter-matcher and intra-matcher parallelism as well as the parallel execution of element- and structure-level matching.

Ontologies are heavily used in life sciences and evolve continuously to incorporate new or changed insights. Often ontology changes affect only specific parts (regions) of ontologies making it valuable for ontology users and applications to know the heavily changed regions on the one hand and stable regions on the other hand. However, the size and complexity of life science ontologies renders manual approaches to localize changing or stable regions impossible. We therefore propose an approach to automatically discover evolving or stable ontology regions.

The problem of archiving and querying the history of a database is made more complex by the fact that, along with the database content, the database schema also evolves with time.

It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology.