A case study on software ecosystem characteristics in industrial automation software

Lettner, Daniela; Angerer, Florian; Prähofer, Herbert; Grünbacher, Paul

doi:10.1145/2600821.2600826

Cited by 31 publications

(14 citation statements)

References 34 publications

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“…The SMESE extends the SECO characteristics presented in [20] from number 10 to 12 [34]. See Table 1 SECO characteristics versus SMESE characteristics.…”

Section: Smese Architecturementioning

confidence: 99%

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A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Brisebois¹,

Abran²,

Nadembéga³

2017

IJITCS

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Abstract-Information systems need to be more flexible and to allow users to find content related to their context and interests. Metadata harvesting and metadata enrichments could represent a way to help users to find content and events according to their interests. However, metadata are underused and represents an interoperability challenge. This paper presents a new framework, called SMESE, and the implementation of its prototypes that consists of its semantic metadata model, a mapping ontology model and a user interest affinity model. This proposed framework makes these models interoperable with existing metadata models.SMESE also proposes a decision support process supporting the activation and deactivation of software features related to metadata. To consider context variability into account in modeling context-aware properties, SMESE makes use of an autonomous process that exploits context information to adapt software behavior using an enhanced metadata framework. When the user chooses preferences in terms of system behavior, the semantic weight of each feature is computed. This weight quantifies the importance of the feature for the user according to their interests. This paper also proposed a semantic metadata analysis ecosystem to support data harvesting according to a metadata model and a mapping ontology model. Data harvesting is coupled with internal and external enrichments. The initial SMESE prototype represents more than 400 millions of relationships (triplets). To conclude, this paper also presents the design and implementation of different prototypes of SMESE applied to digital ecosystems.Index Terms-Metadata, metadata enrichment, metadata model, ontology, semantic metadata enrichment, software ecosystem.

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“…The SMESE extends the SECO characteristics presented in [20] from number 10 to 12 [34]. See Table 1 SECO characteristics versus SMESE characteristics.…”

Section: Smese Architecturementioning

confidence: 99%

“…Nine characteristics have been identified [20] that focus on technical processes for system development, interconnection and evolution.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Brisebois¹,

Abran²,

Nadembéga³

2017

IJITCS

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“…Devido à natureza sócio-técnica dos ECOS, é difícil determinar de forma genérica suas características. No entanto, algumas características chave com foco em processos técnicos do desenvolvimento e evolução dos ECOS são identificadas por Lettner et al [22], com base em uma revisão de literatura. E, embora não sejam características determinantes para definir um ECOS, são parâmetros relevantes para sua análise: (C1) existência de desenvolvedores internos e externos, estendendo as fronteiras de uma organização (BOSCH, 2012); (C2) existência de uma plataforma tecnológica comum, que pode ser uma base tecnológica, um framework extensível e adaptável, um conjunto componentes ou de soluções prontas e adaptáveis; (C3) existência de um controle central, que implica em um ciclo de vida da plataforma independente das aplicações externas compatíveis com a mesma; (C4) possibilidade de contribuições externas em sua plataforma, podendo ser na forma de código aberto com possibilidade de incorporação de código externo, interfaces ou pontos de extensão; (C5) arquitetura com suporte à variabilidade, onde a plataforma suporta adaptações para as especificidades de cada cliente, permitindo que aplicações sejam construídas por desenvolvedores externos baseadas nas configurações específicas de funcionalidades, aplicações e componentes oferecidos pela plataforma; (C6) ativos compartilhados, uma característica herdada da abordagem de linha de produto de software, que é uma estratégia para reuso intra-organizacional são os ativos compartilhados, recursos reutilizáveis compartilhados para a criação de aplicações compatíveis, enquanto os ECOS são uma abordagem para reuso inter-organizacional.…”

Section: Figura 1 -Ecossistema De E-learning [8]unclassified

Uma abordagem de Ecossistemas de Software para o domínio de e-Learning

Veiga

Campos

David

et al. 2016

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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O domínio de e-learning é caracterizado pela fragmentação de soluções e múltiplas implementações similares. Nesse artigo é apresentada uma abordagem para permitir o desenvolvimento, compartilhamento e reuso de serviços educacionais por meio da perspectiva de Ecossistemas de Software. Através da extensão dos atuais sistemas de informação dos ambientes de e-learning, conhecidos como Ambientes Virtuais de Aprendizagem, busca-se criar uma plataforma de um ecossistema que permita colaboração inter-organizacional. A proposta apresentada foi avaliada através um estudo de caso, verificando os conceitos, a arquitetura e as tecnologias utilizadas.

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“…At least ten SECO characteristics have been identified [18] that focus on technical processes for development and evolution, see Table 1. Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1. SECO characteristics [18]. 1 Internal and external developers Gawer and Cusumano [19] have analyzed a wide range of industry examples of SECO and identified two predominant types of platforms:…”

Section: Introductionmentioning

confidence: 99%

A Semantic Metadata Enrichment Software Ecosystem (SMESE) Based on a Multi-Platform Metadata Model for Digital Libraries

Brisebois¹,

Abran²,

Nadembéga³

2017

JSEA

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Software industry has evolved to multi-product and multi-platform development based on a mix of proprietary and open source components. Such integration has occurred in software ecosystems through a software product line engineering (SPLE) process. However, metadata are underused in the SPLE and interoperability challenge. The proposed method is first, a semantic metadata enrichment software ecosystem (SMESE) to support multi-platform metadata driven applications, and second, based on mapping ontologies SMESE aggregates and enriches metadata to create a semantic master metadata catalogue (SMMC). The proposed SPLE process uses a component-based software development approach for integrating distributed content management enterprise applications, such as digital libraries. To perform interoperability between existing metadata models (such as Dublin Core, UNIMARC, MARC21, RDF/RDA and BIBFRAME), SMESE implements an ontology mapping model. SMESE consists of nine sub-systems: 1) Metadata initiatives & concordance rules; 2) Harvesting of web metadata & data; 3) Harvesting of authority metadata & data; 4) Rule-based semantic metadata external enrichment; 5) Rule-based semantic metadata internal enrichment; 6) Semantic metadata external & internal enrichment synchronization; 7) User interest-based gateway; 8) Semantic master catalogue. To conclude, this paper proposes a decision support process, called SPLE decision support process (SPLE-DSP) which is then used by SMESE to support dynamic reconfiguration. SPLE-DSP consists of a dynamic and optimized metadata-based reconfiguration model. SPLE-DSP takes into account runtime metadata-based variability functionalities, context-awareness and self-adaptation. It also presents the design and implementation of a working prototype of SMESE applied to a semantic digital library.

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A case study on software ecosystem characteristics in industrial automation software

Cited by 31 publications

References 34 publications

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Uma abordagem de Ecossistemas de Software para o domínio de e-Learning

A Semantic Metadata Enrichment Software Ecosystem (SMESE) Based on a Multi-Platform Metadata Model for Digital Libraries

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