Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.

Privacy has emerged as a key concern for business and social computing as security breaches have compromised personal data for millions. Despite this, much of existing work on privacy requirements deal with them as a special case of security requirements, thereby missing essential traits of such requirements. In this context, wrong design decisions may be made due to an insufficient understanding of privacy. The main contribution of this paper is an ontology for privacy requirements that subsumes whatever proposals exist in the literature. The ontology was obtained through a systematic literature review concerning the literature on privacy, conducted in two phases, and followed by a consolidation phase where similar concepts were combined. The proposed ontology has been developed to be used by software engineers when dealing with privacy requirements, and since it is based on a systematic literature review, it is more comprehensive in coverage than all ontologies included in our systematic review. This constitutes a preliminary validity check for the comprehensiveness of our proposal, which needs to be complemented in the future with empirical validation through controlled studies.

Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool (OAPT). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.

This work is set in the context of data-centric approaches and is motivated by the observation that business artifacts are not devised as natural means of coordination, despite the fact that they have this potential. Instead of using orchestration and choreography languages, we propose to enrich business artifacts with a normative layer that defines the coordination, basing our approach on social commitments. The straightforward advantage is an increased reusability of both processes and business artifacts, thanks to a clear decoupling between the coordination logic and the business logic. We show how social commitments can be leveraged for modularizing the design of distributed tasks and discuss the advantages of the approach from a software engineering perspective.

Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.

RSS feeds are text-content rich, semantically heterogeneous, and contain dynamic XML elements streamed in asynchronous and pull strategies. Hence, for efficient retrieval of RSS feeds, semantic-aware querying operators have been proposed in the literature (Getahun and Chbeir in Inf Sci 237(237):313–342, 2013). However, it is commonly admitted that the use of semantic information would improve, on one hand, the relevance of query result but, on the other hand, at the cost of degrading the efficiency and the performance of the system. To benefit from query execution on semantic information while keeping the efficiency of the system, we propose here a multi-query optimization approach for semantic RSS feed queries. Our approach processes queries by examining the semantic relationship between them and their corresponding windows. It generates a multi-query chain for queries using their window relations for faster execution at runtime. In addition, we propose an operator called quickDrop for semantic load shedding to gracefully decrease irrelevant data load. To validate the proposed approach, we developed a prototype and conducted a set of experiments. The obtained results show that the use of our approach significantly improves the performance of the system.