Biblio

Functional dependencies (FDs) are widely applied in data management tasks. Since FDs on data are usually unknown, FD discovery techniques are studied for automatically finding hidden FDs from data. In this paper, we develop techniques to dynamically discover FDs in response to changes on data. Formally, given the complete set Σ of minimal and valid FDs on a relational instance r, we aim to find the complete set Σ$^\textrm\textbackslashprime$ of minimal and valid FDs on røplus\textbackslashDelta r, where \textbackslashDelta r is a set of tuple insertions and deletions. Different from the batch approaches that compute Σ$^\textrm\textbackslashprime$ on røplus\textbackslashDelta r from scratch, our dynamic method computes Σ$^\textrm\textbackslashprime$ in response to \textbackslashtriangle\textbackslashuparrow. by leveraging the known Σ on r, and avoids processing the whole of r for each update from \textbackslashDelta r. We tackle dynamic FD discovery on røplus\textbackslashDelta r by dynamic hitting set enumeration on the difference-set of røplus\textbackslashDelta r. Specifically, (1) leveraging auxiliary structures built on r, we first present an efficient algorithm to update the difference-set of r to that of røplus\textbackslashDelta r. (2) We then compute Σ$^\textrm\textbackslashprime$, by recasting dynamic FD discovery as dynamic hitting set enumeration on the difference-set of røplus\textbackslashDelta r and developing novel techniques for dynamic hitting set enumeration. (3) We finally experimentally verify the effectiveness and efficiency of our approaches, using real-life and synthetic data. The results show that our dynamic FD discovery method outperforms the batch counterparts on most tested data, even when \textbackslashDelta r is up to 30 % of r.

In this paper, we propose a novel protocol to represent the human factor on a blockchain environment. Our approach allows single or groups of humans to propose data in blocks which cannot be validated automatically but need human knowledge and collaboration to be validated. Only if human-based consensus on the correctness and trustworthiness of the data is reached, the new block is appended to the blockchain. This human approach significantly extends the possibilities of blockchain applications on data types apart from financial transaction data.

Nowadays, video surveillance systems are part of our daily life, because of their role in ensuring the security of goods and people this generates a huge amount of video data. Thus, several research works based on the ontology paradigm have tried to develop an efficient system to index and search precisely a very large volume of videos. Due to their semantic expressiveness, ontologies are undoubtedly very much in demand in recent years in the field of video surveillance to overcome the problem of the semantic gap between the interpretation of the data extracted from the low level and the high-level semantics of the video. Despite its good expressiveness of semantics, a classical ontology may not be sufficient for good handling of uncertainty, which is however commonly present in the video surveillance domain, hence the need to consider a new ontological approach that will better represent uncertainty. Fuzzy logic is recognized as a powerful tool for dealing with vague, incomplete, imperfect, or uncertain data or information. In this work, we develop a new ontological approach based on fuzzy logic. All the relevant fuzzy concepts such as Video\_Objects, Video\_Events, Video\_Sequences, that could appear in a video surveillance domain are well represented with their fuzzy Ontology DataProperty and the fuzzy relations between them (Ontology ObjectProperty). To achieve this goal, the new fuzzy video surveillance ontology is implemented using the fuzzy ontology web language 2 (fuzzy owl2) which is an extension of the standard semantic web language, ontology web language 2 (owl2).

To ensure a secure Cloud-based Electronic Health Record (EHR) system, we need to encrypt data and impose field-level access control to prevent malicious usage. Since the attributes of the Users will change with time, the encryption policies adopted may also vary. For large EHR systems, it is often necessary to search through the encrypted data in realtime and perform client-side computations without decrypting all patient records. This paper describes our novel cloud-based EHR system that uses Attribute Based Encryption (ABE) combined with Semantic Web technologies to facilitate differential access to an EHR, thereby ensuring only Users with valid attributes can access a particular field of the EHR. The system also includes searchable encryption using keyword index and search trapdoor, which allows querying EHR fields without decrypting the entire patient record. The attribute revocation feature is efficiently managed in our EHR by delegating the revision of the secret key and ciphertext to the Cloud Service Provider (CSP). Our methodology incorporates advanced security features that eliminate malicious use of EHR data and contributes significantly towards ensuring secure digital health systems on the Cloud.

The evolution of Service-Oriented Computing (SOC) provides more efficient software development methods for building and engineering new value-added service-based applications. SOC is a computing paradigm that relies on Web services as fundamental elements. Research and technical advancements in Web services composition have been considered as an effective opportunity to develop new service-based applications satisfying complex requirements rapidly and efficiently. In this paper, we present a novel approach enhancing the composition of semantic Web services. The novelty of our approach, as compared to others reported in the literature, rests on: i) mapping user's/organization's requirements with Business Process Modeling Notation (BPMN) and semantic descriptions using ontologies, ii) considering functional requirements and also different types of non-functional requirements, such as quality of service (QoS), quality of experience (QoE), and quality of business (QoBiz), iii) using Formal Concept Analysis (FCA) technique to select the optimal set of Web services, iv) considering composability levels between sequential Web services using Relational Concept Analysis (RCA) technique to decrease the required adaptation efforts, and finally, v) validating the obtained service-based applications by performing an analytical technique, which is the monitoring. The approach experimented on an extended version of the OWLS-TC dataset, which includes more than 10830 Web services descriptions from various domains. The obtained results demonstrate that our approach allows to successfully and effectively compose Web services satisfying different types of user's functional and non-functional requirements.

Internet technology has changed how people work, live, communicate, learn and entertain. The internet adoption is rising rapidly, thus creating a new industrial revolution named "Industry 4.0". Industry 4.0 is the use of automation and data transfer in manufacturing technologies. It fosters several technological concepts, one of these is the Internet of Things (IoT). IoT technology is based on a big network of machines, objects, or people called "things" interacting together to achieve a common goal. These things are continuously generating vast amounts of data. Data understanding, processing, securing and storing are significant challenges in the IoT technology which restricts its development. This paper presents a new reference IoT model for future smart IoT solutions called Cloud Web of Things (CloudWoT). CloudWoT aims to overcome these limitations by combining IoT with edge computing, semantic web, and cloud computing. Additionally, this work is concerned with the security issues which threatens data in IoT application domains.

Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients' end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR's access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.

The Semantic Web can be used to enable the interoperability of IoT devices and to annotate their functional and nonfunctional properties, including security and privacy. In this paper, we will show how to use the ontology and JSON-LD to annotate connectivity, security and privacy properties of IoT devices. Out of that, we will present our prototype for a lightweight, secure application level protocol wrapper that ensures communication consistency, secrecy and integrity for low cost IoT devices like the ESP8266 and Photon particle.

The Semantic Web today is a web that allows for intelligent knowledge retrieval by means of semantically annotated tags. This web also known as Intelligent web aims to provide meaningful information to man and machines equally. However, the information thus provided lacks the component of trust. Therefore we propose a method to embed trust in semantic web documents by the concept of provenance which provides answers to who, when, where and by whom the documents were created or modified. This paper demonstrates the same using the Manchester approach of provenance implemented in a University Ontology.

Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.

Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing these vulnerabilities at a global scale becomes an inherently difficult task since many of the existing resources required for such analysis still rely on proprietary knowledge representation. In this research, we introduce an ontology-based knowledge modeling approach that can eliminate such information silos. More specifically, we focus on linking security knowledge with other software knowledge to improve traceability and trust in software products (APIs). Our approach takes advantage of the Semantic Web and its reasoning services, to trace and assess the impact of security vulnerabilities across project boundaries. We present a case study, to illustrate the applicability and flexibility of our ontological modeling approach by tracing vulnerabilities across project and resource boundaries.

The Sensor Web is evolving into a complex information space, where large volumes of sensor observation data are often consumed by complex applications. Provenance has become an important issue in the Sensor Web, since it allows applications to answer “what”, “when”, “where”, “who”, “why”, and “how” queries related to observations and consumption processes, which helps determine the usability and reliability of data products. This paper investigates characteristics and requirements of provenance in the Sensor Web and proposes an interoperable approach to building a provenance model for the Sensor Web. Our provenance model extends the W3C PROV Data Model with Sensor Web domain vocabularies. It is developed using Semantic Web technologies and thus allows provenance information of sensor observations to be exposed in the Web of Data using the Linked Data approach. A use case illustrates the applicability of the approach.

Semantic Web has brought forth the idea of computing with knowledge, hence, attributing the ability of thinking to machines. Knowledge Graphs represent a major advancement in the construction of the Web of Data where machines are context-aware when answering users' queries. The English Knowledge Graph was a milestone realized by Google in 2012. Even though it is a useful source of information for English users and applications, it does not offer much for the Arabic users and applications. In this paper, we investigated the different challenges and opportunities prone to the life-cycle of the construction of the Arabic Knowledge Graph (AKG) while following some best practices and techniques. Additionally, this work suggests some potential solutions to these challenges. The proprietary factor of data creates a major problem in the way of harvesting this latter. Moreover, when the Arabic data is openly available, it is generally in an unstructured form which requires further processing. The complexity of the Arabic language itself creates a further problem for any automatic or semi-automatic extraction processes. Therefore, the usage of NLP techniques is a feasible solution. Some preliminary results are presented later in this paper. The AKG has very promising outcomes for the Semantic Web in general and the Arabic community in particular. The goal of the Arabic Knowledge Graph is mainly the integration of the different isolated datasets available on the Web. Later, it can be used in both the academic (by providing a large dataset for many different research fields and enhance discovery) and commercial sectors (by improving search engines, providing metadata, interlinking businesses).

The usage of Information and Communication Technologies (ICTs) pervades everyday's life. If it is true that ICT contributed to improve the quality of our life, it is also true that new forms of (cyber)crime have emerged in this setting. The diversity and amount of information forensic investigators need to cope with, when tackling a cyber-crime case, call for tools and techniques where knowledge is the main actor. Current approaches leave to the investigator the chore of integrating the diverse sources of evidence relevant for a case thus hindering the automatic generation of reusable knowledge. This paper describes an architecture that lifts the classical phases of a digital forensic investigation to a knowledge-driven setting. We discuss how the usage of languages and technologies originating from the Semantic Web proposal can complement digital forensics tools so that knowledge becomes a first-class citizen. Our architecture enables to perform in an integrated way complex forensic investigations and, as a by-product, build a knowledge base that can be consulted to gain insights from previous cases. Our proposal has been inspired by real-world scenarios emerging in the context of an Italian research project about cyber security.

Smart Spaces are composed of heterogeneous sensors and devices that collect and share information. This information may contain personal information of the users. Thus, securing the data and preserving the privacy are of paramount importance. In this paper, we propose techniques for information security and privacy protection for Smart Spaces based on the Smart-M3 platform. We propose a) a security framework, and b) a context-aware role-based access control scheme. We model our access control scheme using ontological techniques and Web Ontology Language (OWL), and implement it via CLIPS rules. To evaluate the efficiency of our access control scheme, we measure the time it takes to check the access rights of the access requests. The results demonstrate that the highest response time is approximately 0.2 seconds in a set of 100000 triples. We conclude that the proposed access control scheme produces low overhead and is therefore, an efficient approach for Smart Spaces.

There has been an ongoing trend toward collaborative software development using open and shared source code published in large software repositories on the Internet. While traditional source code analysis techniques perform well in single project contexts, new types of source code analysis techniques are ermerging, which focus on global source code analysis challenges. In this article, we discuss how the Semantic Web, can become an enabling technology to provide a standardized, formal, and semantic rich representations for modeling and analyzing large global source code corpora. Furthermore, inference services and other services provided by Semantic Web technologies can be used to support a variety of core source code analysis techniques, such as semantic code search, call graph construction, and clone detection. In this paper, we introduce SeCold, the first publicly available online linked data source code dataset for software engineering researchers and practitioners. Along with its dataset, SeCold also provides some Semantic Web enabled core services to support the analysis of Internet-scale source code repositories. We illustrated through several examples how this linked data combined with Semantic Web technologies can be harvested for different source code analysis tasks to support software trustworthiness. For the case studies, we combine both our linked-data set and Semantic Web enabled source code analysis services with knowledge extracted from StackOverflow, a crowdsourcing website. These case studies, we demonstrate that our approach is not only capable of crawling, processing, and scaling to traditional types of structured data (e.g., source code), but also supports emerging non-structured data sources, such as crowdsourced information (e.g., StackOverflow.com) to support a global source code analysis context.