Cristhian Figueroa

A systematic literature review of Linked Data-based recommender systems

Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS. Copyright

Systematic Review of Linked Data-based Recommender Systems

Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS. Copyright

Dynamic Reconfiguration of Composite Convergent Services Supported by Multimodal Search

Composite convergent services integrate a set of functionalities from Web and Telecommunication domains. Due to the big amount of available functionalities, automation of composition process is required in many fields. However, automated composition is not feasible in practice if reconfiguration mechanisms are not considered. This paper presents a novel approach for dynamic reconfiguration of convergent services that replaces malfunctioning regions of composite convergent services considering user preferences. In order to replace the regions of services, a multimodal search is performed. Our contributions are: a model for representing composite convergent services and a region-based algorithm for reconfiguring services supported by multimodal search.

Semantic Annotation and Classification in Practice

The Webs evolution into a Semantic Web and the continuous increase in the amount of data published as linked data open up new opportunities for annotation and categorization systems to reuse these data as semantic knowledge bases. Accordingly, information extraction systems use linked data to exploit semantic knowledge bases, which can be interconnected and structured to increase the precision and recall of annotation and categorization mechanisms. TellMeFirst classifies and enriches textual documents written in English and Italian. Although various works present solutions for text annotation and classification, this article describes and studies the use case of a telecommunications operator that has adopted TellMeFirst to add value to two services available to its users: FriendTV and Society.

Business Process Model Retrieval Based on Graph Indexing Method

Nowadays, business process reuse is very important and necessary inside large organizations that continually increase their process collections. Therefore, an efficient system to manage and search for concrete and relevant processes is necessary. Here we overcome to this problem proposing business process model retrieval based on a Graph Indexing Method. It takes into account a measure similarity between two graphs and provides a ranking of business process retrieved.

Improving business process retrieval using categorization and multimodal search

Enterprises use repositories of Business Processes to standardize and adapt their operations in order to reuse them for new functional requirements. However, a disorganized growth of these repositories have hampered the search of Business Processes which is fundamental for reusing them. In this paper an approach for organizing and searching Business Processes is proposed, which is composed of two phases: First, an automatic and semantic categorization phase to classify Business Processes based on their functionality, and second a multimodal search phase in order to rank Business Processes based on structural and textual features. The proposed approach was tested in an evaluation over a closed repository collaboratively built by 20 expert evaluators. Initially, evaluators were asked to rate categories assigned by our approach to each Business Process in order to assess our results against the user perspective. Later, evaluators were asked to compare six queries against the repository in order to obtain a set of relevant Business Processes for each query. With these results, precision, recall and F-measure were calculated to evaluate the relevance and ranking concordance of the proposed approach against state-of-the-art algorithms for Business Process similarity search. Additionally, we applied the Friedman and the Wilcoxon signed rank tests over the results obtained for each query over precision and F-measure in order to evaluate the statistically significance of these results. The results obtained demonstrated the effectiveness of the proposed approach for categorizing and retrieving Business Processes.

Linked Data-Driven Smart Spaces

In this paper, we present an approach to exploit Linked Data in Smart Spaces, doing more than just using RDF to represent information. In particular, we rely on knowledge stored in DBpedia, a dataset in the Web of Data. We also provide a platform to implement such an approach and a eTourism use case, both developed in collaboration with a mobile operator. Finally, we provide also a performance evaluation of the main component of the platform.

Business process indexing based on similarity of execution cases

This paper presents EC-Indexer a new approach for Business Process indexing based on execution traces extracted from event-log files. Additionally, a tool implementing the proposed similarity mechanism was developed in order to evaluate the effectiveness by common measures as precision, recall, and f-measure. The results showed that even when the EC-Indexer approach scored low values of recall, it could reach high values of precision while reducing the execution time.

A Subgraph Isomorphism Based Approach to Enable Discovery and Composition of Smart Space Elements

Nowadays, the variety of mobile services is growing together with the number of customers and the heterogeneity of their mobile devices, thus, monitoring the users of a mobile network has become a challenge. Considering Smart Space Governing to address this issue, in which the mobile network is considered an Smart Space (due to its size and complexity) and their elements (mobile devices and network monitoring services), it is possible to create rules to monitor the output of these elements. We call those rules as Smart Space Compositions and can be created through the platform’s Visual Editor, that during the graphical creation process, provides the user with a list of similar existing compositions that can be reused at any time to improve the composition process. This paper describes the implementation by a Telecommunications Operator of this composition module supported by subgraph isomorphism techniques.

Sincronización y distribución de tareas para worflows distribuidos en sistemas móviles de información

En este articulo se presenta la arquitectura de sincronizacion de BDMobIS, un sistema para la distribucion de los procesos de negocios entre dispositivos moviles, a fin de reducir los tiempos de ejecucion y las comunicaciones entre los participantes. La sincronizacion es uno de los puntos mas criticos, puesto que es la responsable de que el flujo normal del proceso no se pierda aun despues del particionamiento. Se propone un mecanismo de sincronizacion y distribucion basado en la insercion de actividades de sincronizacion que permiten mantener el flujo de ejecucion, compartir e intercambiar la informacion global del proceso basada en Servicios de Mensajeria Corta. Ademas, en este articulo se presenta un caso de estudio para la ejecucion del proceso de matricula de la Universidad del Cauca.