Relational Database Migration: A Perspective

Maatuk, Abdelsalam M.; Ali, Akhtar; Rossiter, Nick

doi:10.1007/978-3-540-85654-2_58

Cited by 17 publications

(11 citation statements)

References 17 publications

(31 reference statements)

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“…The transformation of a relational database into a graph database includes schema translation and data conversion [24]. The schema translation can turn the source schema into the target schema by applying a set of mapping rules.…”

Section: Graph Extractionmentioning

confidence: 99%

DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

Soussi

Cuvelier

Aufaure

et al. 2012

Lecture Notes in Social Networks

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Abstract. In the enterprise context, People need to visualize different types of interactions between heterogeneous objects (e.g. product and site, customers and product, people interaction (social network)...). The existing approaches focus on social networks extraction using web document. However a considerable amount of information is stored in relational databases. Therefore, relational databases can be seen as rich sources for extracting a social network. The extracted network has in general a huge size which makes it difficult to analyze and visualize. An aggregation step is needed in order to have more understandable graphs. In this chapter, we propose a heterogeneous object graph extraction approach from a relational database and we present its application to extract social network. This step is followed by an aggregation step in order to improve the visualisation and the analyse of the extracted social network. Then, we aggregate the resulting network using the k-SNAP algorithm which produces a summarized graph.

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Section: Graph Extractionmentioning

confidence: 99%

DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

Soussi

Cuvelier

Aufaure

et al. 2012

Lecture Notes in Social Networks

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show abstract

“…The relational database transformation into a graph database includes schema translation and data conversion (Maatuk et al 2008). The schema translation can turn the source schema into the target schema by applying a set of mapping rules.…”

Section: Converting Relational Database Into Hypernode Databasementioning

confidence: 99%

Graph Database for Collaborative Communities

Soussi

Aufaure²,

Baazaoui³

2011

Community-Built Databases

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Data manipulated in an enterprise context are structured data as well as unstructured data such as emails, documents, social networks, etc. Graphs are a natural way of representing and modeling such data in a unified manner (Structured, semi-structured and unstructured ones). The main advantage of such a structure relies in the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of handling data appearing. In this chapter, we give a survey of the main graph database models and the associated graph query languages. We then present an application using a graph database to extract social networks.

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“…Fig.4showsthe To demonstratethe effectiveness and validity ofthe CDM, a prototype has been developedusing Java 1.5,realizingthe algorithmpresented in this paper.Wesetuptwo experiments to evaluate our approach by examining the differencesbetweensource RDB and target databasesgenerated bythe prototype.Theexperimentswererun on a PC with Pentium IV 3.2 GHz CPU and 1024 MB RAM operatingunder Windows XP Professional.We measureddatabase equivalences, includingsemantics preservation,loss of dataandredundancy, and integrity constraints. Full detailsaboutthe experimentscan befound in [14,15]. In the firstexperiment, wetest schema information preservation by comparing the target schemas resultingfromour prototype and those generated from other manual-based mapping techniques.A schema is correct if all concepts of underlying model are used correctly with respect to syntax andsemantics [10].…”

Section: B Algorithm For Generation Of Cdmmentioning

confidence: 99%

“…However, most of the work has been focused onschema translation ratherthandata conversionwith an aim to generate one target data model based on its conceptual schema or other representations as an intermediate stage for enrichment. In addition, the existing work does not provide a complete solution for more than one target database,for either schema or data conversion.A classification on database migration techniques can be found in our work [13].…”

Section: B Algorithm For Generation Of Cdmmentioning

confidence: 99%

Semantic Enrichment: The First Phase of Relational Database Migration

Maatuk

Ali

Rossiter

2009

Innovations and Advances in Computer Sciences and Engineering

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Abstract-Semanticenrichment is a process of analyzing and examining a database to capture its structure and definitions at a higher level of meaning. This is done by enhancing a representation of an existing database's structure in order to make hidden semantics explicit. In contrast to other approaches, we present an approach that takes an existing relational database as input, obtains a copy of its meta data and enriches it with as much semantics as possible, and constructs an enhanced Relational Schema Representation (RSR). Based on RSR, a Canonical Data Model (CDM) is generated,which captures essential characteristics of targetdatabases(i.e., object-based and XML) suitablefor migration.We have developed an algorithm for generatingCDM from an enhanced relational representation of an input relational database.Aprototype has been implementedandexperimentalresultsare reported.

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Relational Database Migration: A Perspective

Cited by 17 publications

References 17 publications

DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

Graph Database for Collaborative Communities

Semantic Enrichment: The First Phase of Relational Database Migration

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