Monitoring morphisms to support sustainable interoperability of enterprise systems

Dissertation to obtain the Master degree in Electrical Engineering and Computer ScienceNowadays, organizations are required to be part of a global collaborative world. Sometimes this is the only way they can access new and wider markets, reaching new opportunities, skills and sharing assets, e.g. tools, lessons learnt. However, due to the different sources of enterprise models and semantics, organizations are experiencing difficulties in exchanging vital information via electronic and in a seamlessly way. To solve this issue, most of them try to attain interoperability by establishing peer-to-peer mappings with different business partners, or in optimized networks using neutral data standards to regulate communications. Moreover, the systems are more and more dynamic, changing frequently to answer new customer’s requirements, causing new interoperability problems and a reduction of efficiency. This dissertation proposes a multi-agent system to monitor existing enterprise systems, by being capable of detecting morphism changes. With this, network harmonization breakings are timely detected, and possible solutions are suggested to regain the interoperable status, thus enhancing robustness for reaching sustainability of business networks

Automated Knowledge Base Quality Assessment and Validation based on Evolution Analysis

In recent years, numerous efforts have been put towards sharing Knowledge Bases (KB) in the Linked Open Data (LOD) cloud. These KBs are being used for various tasks, including performing data analytics or building question answering systems. Such KBs evolve continuously: their data (instances) and schemas can be updated, extended, revised and refactored. However, unlike in more controlled types of knowledge bases, the evolution of KBs exposed in the LOD cloud is usually unrestrained, what may cause data to suffer from a variety of quality issues, both at a semantic level and at a pragmatic level. This situation affects negatively data stakeholders – consumers, curators, etc. –. Data quality is commonly related to the perception of the fitness for use, for a certain application or use case. Therefore, ensuring the quality of the data of a knowledge base that evolves is vital. Since data is derived from autonomous, evolving, and increasingly large data providers, it is impractical to do manual data curation, and at the same time, it is very challenging to do a continuous automatic assessment of data quality. Ensuring the quality of a KB is a non-trivial task since they are based on a combination of structured information supported by models, ontologies, and vocabularies, as well as queryable endpoints, links, and mappings. Thus, in this thesis, we explored two main areas in assessing KB quality: (i) quality assessment using KB evolution analysis, and (ii) validation using machine learning models. The evolution of a KB can be analyzed using fine-grained “change” detection at low-level or using “dynamics” of a dataset at high-level. In this thesis, we present a novel knowledge base quality assessment approach using evolution analysis. The proposed approach uses data profiling on consecutive knowledge base releases to compute quality measures that allow detecting quality issues. However, the first step in building the quality assessment approach was to identify the quality characteristics. Using high-level change detection as measurement functions, in this thesis we present four quality characteristics: Persistency, Historical Persistency, Consistency and Completeness. Persistency and historical persistency measures concern the degree of changes and lifespan of any entity type. Consistency and completeness measures identify properties with incomplete information and contradictory facts. The approach has been assessed both quantitatively and qualitatively on a series of releases from two knowledge bases, eleven releases of DBpedia and eight releases of 3cixty Nice. However, high-level changes, being coarse-grained, cannot capture all possible quality issues. In this context, we present a validation strategy whose rationale is twofold. First, using manual validation from qualitative analysis to identify causes of quality issues. Then, use RDF data profiling information to generate integrity constraints. The validation approach relies on the idea of inducing RDF shape by exploiting SHALL constraint components. In particular, this approach will learn, what are the integrity constraints that can be applied to a large KB by instructing a process of statistical analysis, which is followed by a learning model. We illustrate the performance of our validation approach by using five learning models over three sub-tasks, namely minimum cardinality, maximum cardinality, and range constraint. The techniques of quality assessment and validation developed during this work are automatic and can be applied to different knowledge bases independently of the domain. Furthermore, the measures are based on simple statistical operations that make the solution both flexible and scalable

Towards BIM/GIS interoperability: A theoretical framework and practical generation of spaces to support infrastructure Asset Management

The past ten years have seen the widespread adoption of Building Information Modelling (BIM) among both the Architectural, Engineering and Construction (AEC) and the Asset Management/ Facilities Management (AM/FM) communities. This has been driven by the use of digital information to support collaborative working and a vision for more efficient reuse of data. Within this context, spatial information is either held in a Geographic Information Systems (GIS) or as Computer-Aided Design (CAD) models in a Common Data Environment (CDE). However, these being heterogeneous systems, there are inevitable interoperability issues that result in poor integration. For this thesis, the interoperability challenges were investigated within a case study to ask: Can a better understanding of the conceptual and technical challenges to the integration of BIM and GIS provide improved support for the management of asset information in the context of a major infrastructure project? Within their respective fields, the terms BIM and GIS have acquired a range of accepted meanings, that do not align well with each other. A seven-level socio-technical framework is developed to harmonise concepts in spatial information systems. This framework is used to explore the interoperability gaps that must be resolved to enable design and construction information to be joined up with operational asset information. The Crossrail GIS and BIM systems were used to investigate some of the interoperability challenges that arise during the design, construction and operation of an infrastructure asset. One particular challenge concerns a missing link between AM-based information and CAD-based geometry which hinders engineering assets from being located within the geometric model and preventing geospatial analysis. A process is developed to link these CAD-based elements with AM-based assets using defined 3D spaces to locate assets. However, other interoperability challenges must first be overcome; firstly, the extraction, transformation and loading of geometry from CAD to GIS; secondly, the creation of an explicit representation of each 3D space from the implicit enclosing geometry. This thesis develops an implementation of the watershed transform algorithm to use real-world Crossrail geometry to generate voxelated interior spaces that can then be converted into a B-Rep mesh for use in 3D GIS. The issues faced at the technical level in this case study provide insight into the differences that must also be addressed at the conceptual level. With this in mind, this thesis develops a Spatial Information System Framework to classify the nature of differences between BIM, GIS and other spatial information systems

Semantic technologies for supporting KDD processes

209 p.Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to solve these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process. This research work aims at supporting data analysts through the different KDD phases towards the achievement of energy efficiency and thermal comfort in tertiary buildings. To do so, the EEPSA (Energy Efficiency Prediction Semantic Assistant) is proposed, which aids data analysts discovering the most relevant variables for the matter at hand, and informs them about relationships among relevant data. This assistant leverages Semantic Technologies such as ontologies, ontology-driven rules and ontology-driven data access. More specifically, the EEPSA ontology is the cornerstone of the assistant. This ontology is developed on top of three ODPs (Ontology Design Patterns) and it is designed so that its customization to address similar problems in different types of buildings can be approached methodically

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

A Quality Assessment Approach for Evolving Knowledge Bases

Knowledge bases are nowadays essential components for any task that requires automation with some degrees of intelligence.Assessing the quality of a Knowledge Base (KB) is a complex task as it often means measuring the quality of structured information, ontologies and vocabularies, and queryable endpoints. Popular knowledge bases such as DBpedia, YAGO2, and Wikidata have chosen the RDF data model to represent their data due to its capabilities for semantically rich knowledge representation. Despite its advantages, there are challenges in using RDF data model, for example, data quality assessment and validation. In thispaper, we present a novel knowledge base quality assessment approach that relies on evolution analysis. The proposed approachuses data profiling on consecutive knowledge base releases to compute quality measures that allow detecting quality issues. Our quality characteristics are based on the KB evolution analysis and we used high-level change detection for measurement functions. In particular, we propose four quality characteristics: Persistency, Historical Persistency, Consistency, and Completeness.Persistency and historical persistency measures concern the degree of changes and lifespan of any entity type. Consistency andcompleteness measures identify properties with incomplete information and contradictory facts. The approach has been assessed both quantitatively and qualitatively on a series of releases from two knowledge bases, eleven releases of DBpedia and eight releases of 3cixty. The capability of Persistency and Consistency characteristics to detect quality issues varies significantly between the two case studies. Persistency measure gives observational results for evolving KBs. It is highly effective in case of KBwith periodic updates such as 3cixty KB. The Completeness characteristic is extremely effective and was able to achieve 95%precision in error detection for both use cases. The measures are based on simple statistical operations that make the solution both flexible and scalabl

Mapping and the Citizen Sensor

Maps are a fundamental resource in a diverse array of applications ranging from everyday activities, such as route planning through the legal demarcation of space to scientific studies, such as those seeking to understand biodiversity and inform the design of nature reserves for species conservation. For a map to have value, it should provide an accurate and timely representation of the phenomenon depicted and this can be a challenge in a dynamic world. Fortunately, mapping activities have benefitted greatly from recent advances in geoinformation technologies. Satellite remote sensing, for example, now offers unparalleled data acquisition and authoritative mapping agencies have developed systems for the routine production of maps in accordance with strict standards. Until recently, much mapping activity was in the exclusive realm of authoritative agencies but technological development has also allowed the rise of the amateur mapping community. The proliferation of inexpensive and highly mobile and location aware devices together with Web 2.0 technology have fostered the emergence of the citizen as a source of data. Mapping presently benefits from vast amounts of spatial data as well as people able to provide observations of geographic phenomena, which can inform map production, revision and evaluation. The great potential of these developments is, however, often limited by concerns. The latter span issues from the nature of the citizens through the way data are collected and shared to the quality and trustworthiness of the data. This book reports on some of the key issues connected with the use of citizen sensors in mapping. It arises from a European Co-operation in Science and Technology (COST) Action, which explored issues linked to topics ranging from citizen motivation, data acquisition, data quality and the use of citizen derived data in the production of maps that rival, and sometimes surpass, maps arising from authoritative agencies