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.
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.
As existing computer search engines struggle to understand the meaning of natural language, semantically enriched metadata may improve interest-based search engine capabilities and user satisfaction. This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection and enrichments. It is based on a previous paper, a semantic metadata enrichment software ecosystem (SMESE). Through text analysis approaches for topic detection and metadata enrichments this paper propose an algorithm to enhance search engines capabilities and consequently help users finding content according to their interests. It presents the design, implementation and evaluation of SATD (Scalable Annotation-based Topic Detection) model and algorithm using metadata from the web, linked open data, concordance rules, and bibliographic record authorities. It includes a prototype of a semantic engine using keyword extraction, classification and concept extraction that allows generating semantic topics by text, and multimedia document analysis using the proposed SATD model and algorithm.The performance of the proposed ecosystem is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques (e.g., AlchemyAPI, DBpedia, Wikimeta, Bitext, AIDA, TextRazor). It was noted that SATD algorithm supports more attributes than other algorithms. The results show that the enhanced platform and its algorithm enable greater understanding of documents related to user interests.
Abstract. Deployment of small cells (i.e., picocells and femtocells) within macrocell coverage is seen as a cost-effective way to increase system capacity and to equip wireless WANs with the ability to keep up with the increasing demand for data capacity. Existing cell discovery mechanisms are tailored for homogeneous networks (macrocells only). User Equipment (UE) cannot efficiently save energy in the process of small cells detection in order to exploit offloading opportunities provided by such heterogeneous deployments. In this paper, we propose a Mobility Prediction aware Scanning Start Time Estimation (MPSTE) scheme to discover/detect small cells efficiently in terms of energy. Based on the current data on road segments (e.g., density of road segment, UEs' speeds and physical aspects of road segment) and current behaviour of UEs on the road segment, MPSTE allows deriving the time interval UE will spend in the small cell and making decision to perform handoff or no; if handoff is necessary, MPSTE derives the best time to begin the scanning process to discover small cells. Simulation results show the benefits of MPSTE over existing schemes in terms of energy saving by UEs.
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