Web Semantics in Cloud Computing

Total Page:16

File Type:pdf, Size:1020Kb

Web Semantics in Cloud Computing International Journal of Scientific & Engineering Research, Volume 7, Issue 2, February-2016 ISSN 2229-5518 378 Review: Web Semantics in Cloud Computing Rupali A. Meshram, Assistant professor, CSE Department, PRMIT&R, Badnera Komal R. Hole, Assistant professor, CSE Department, PRMIT&R, Badnera Pranita P. Deshmukh, Assistant professor, CSE Department, PRMIT&R, Badnera Roshan A. karwa, Assistant professor, CSE Department, PRMIT&R, Badnera Abstract – on web service and the web services are more efficient This paper shows how cloud computing on the so as to attract more users. background of Semantic Web is going to important. Many cloud computing services are Cloud computing has become interesting area both in implementing on distributed web environment and academia and industry. Semantic Web has been an provides virtualized resources, which focuses on some important research area in academic and industrial aspects like the semantic ambiguity, distributed researchers. Many applications will need to work with semantic reasoning, distributed semantic models large amounts of data, one particular application would construction, distributed metadata storage, parallel certainly not exist without the capability of accessing and semantic computing, etc. It also introduces many real processing arbitrary amounts of metadata: search world applications like semantic analysis in science engines that locate the data and services that other computing, cloud resource discovery, accurate applications need. To solve this problem, semantic web advertise recommendation, and web context services used with cloud computing. In this paper we understanding in commercial services. present the use of well established semantic technologies The Semantic Web is gaining immense in Cloud computing. The purpose of this report is to give popularity. Tim Berners Lee’s vision of the Semantic an overview of semantic web services and semantic web Web or Web 3.0 is to transform the World Wide Web technologies in cloud computing. into an intelligent web system of structured, linked data Keyword: Semantic Web Services, Semantic Web which can be queried and inferred as a whole by the Technologies, Cloud Computing, Semantic Web computers themselves. Interoperable applications hosted on the web as service for customers and these I. Introduction web services are designed to be automatically IJSERdiscovered by software agents and exchange data Now a day, Internet provides many more services such amongst themselves. In internet, the cloud computing as instant message, sharing videos, web search, e- platform is another business model, where hardware, commerce platforms, online video conference, and software, applications and tools all will be use as image processing, and various other types of third- public. The main purpose of this paper is to provide a party services on the Web. This require huge amount of research survey on semantic web technologies and memory space on web database to improve online semantic web services. performance and the demand for real-time applications The paper is organized as follows section 2 and high-speed data processing. Because of this, many contains brief introduction for cloud computing to organizations causes a problem to construct large provide the base of this concept. Section 3 describes datacenter to satisfy their demand. As a solution to Semantic Web with their technologies and services. these problems, cloud computing technology used by Section 4 concludes this study. various companies. The companies which provide web services no longer need large capital outlays in II Cloud computing hardware and software to deploy their services. Those companies can just buy these “hardware” and The term "cloud" is group of network in computer. The “software” as cloud computing services on the Web. word "cloud" often refers to the Internet and more To build large datacenter required more money to build precisely to some datacenter full of servers that is their services on cloud. Cloud computing provides on- connected to the Internet. In internet, Cloud computing demand self-service, , measured service, resource is a kind of Internet-based computing, where shared pooling, broad network access and rapid elasticity[1]. resources, information and data are provided to Developers can easily implement their creative ideas computers on-demand. At the time Google started in 1998, its business increased so rapidly that the internet IJSER © 2016 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 7, Issue 2, February-2016 ISSN 2229-5518 379 technologies are not enough to process the huge new proposed cloud computing model for large scale amount of data in acceptable manner. To solve this graph processing. It can avoid multiple iterations in problem, they develop its own file system Google File MapReduce framework and give stable and scalable System (GFS) [3], and built its parallel computing performance. Pig-latin [7], a SQL-like and data flow environment MapReduce [4] based on GFS. These language can be implemented on Pig [8] to perform technologies which are later called “cloud computing” database-like functionality. turned out to be high efficient, stable and reliable. To access web service, firstly user has to login According to "The NIST Definition of Cloud to the websites who provides cloud service then they Computing" [2], cloud computing is “the delivery of can select any web service that they needed. Users can computing as a service rather than a product, whereby set up their real-time applications on cloud by using shared resources, software, and information are different virtual resources like hard disks, CPU provided to computers and other devices as a utility processors and memories and they can run their web (like the electricity grid) over a network (typically the services application on the platforms of cloud service. Internet)”. In cloud computing everything can be If users want to develop any application then, they can provided as service, including infrastructure, hardware just adopt the existing software services in the cloud. platform, software, etc. The cloud computing model III Semantic Web can be mainly classified into three layers [2]: Cloud Software as a Service (SaaS), Cloud Platform as a "Semantic Web" was coined by Tim Berners-Lee ( Service (PaaS), and Cloud Infrastructure as a Service inventor of the "W3C"). The Semantic web is a (IaaS). In SaaS, the capability provided to the polymorphism of similar data on web. The Semantic consumer is to use the provider’s applications running Web is an extension of the Web through standards by on a Cloud Infrastructure. This is an alternative to the W3C. Tim Berners-Lee defines the Semantic Web locally run applications. The Google Docs is an as "a web of data that can be processed directly and example of SaaS. In PaaS, users can have flexibility indirectly by machines". The Semantic Web provides a and more choices. The consumer can deploy onto the common framework that allows data to be shared and cloud Infrastructure consumer-created or application reused across application, enterprise, and community created using different programming languages and boundaries. Since the inception of the World Wide tools supported by the provider. The Google Apps Web in 1990 by Tim Berners Lee, it has been a large Engine and Microsoft Azure are some examples of databaes of documents and nowadays the percentage of PaaS. IaaS also provides flexibility and choices. The documents is growing very rapidly. The information consumer can choose networks, processing, storage from these huge documents can be aggregated and and other fundamental computing resources where the inferred quickly, they don’t have much use. Human consumer is able to deploy and run software, which can readers cannot read huge amount of documents include operating sysIJSERtems and applications. Amazon’s retrieved by the previously used search engines based Elastic Compute Cloud (based on Amazon machine on keyword searches. To solve this problem Tim Image) is an example of IaaS. If the cloud computing is Berners Lee’s vision is to transform this World Wide based on Internet and available to the general public, it Web into an intelligent web system or Semantic Web is referred as public cloud. The cloud service providers [10], [11], [12], [13], [14], [15], [16], [17] which will can decide adopting which deployment models based allow concept searches rather than old concept of on the application goals of the cloud service keyword searches. Semantic Web or Web 3.0 consumers. Many companies create own cloud: eBay technologies will transform disconnected text provides their own opensource PaaS platform turmeric, documents on the web into a global database of IBM released “Blue Cloud” service while Microsoft structured, linked data. These large volumes of linked calls its cloud service platform “Azure” and Yahoo data in global database will no longer be only for also develop non-structure data storage base “Mobstor” human consumption but for quick machine processing. and data storage & processing platform “Sherpa”. The semantic web includes web services, In 2004, the most famous opersource cloud resources, semantic relations, etc, can be identified computing framework Hadoop began to build. Hadoop with Uniform Resource Identifier (URI). It is used to was derived from Google's MapReduce and Google avoid semantic ambiguities and is convenient to make File System (GFS) and is adopted by IBM, Yahoo and version control.
Recommended publications
  • Metadata for Semantic and Social Applications
    etadata is a key aspect of our evolving infrastructure for information management, social computing, and scientific collaboration. DC-2008M will focus on metadata challenges, solutions, and innovation in initiatives and activities underlying semantic and social applications. Metadata is part of the fabric of social computing, which includes the use of wikis, blogs, and tagging for collaboration and participation. Metadata also underlies the development of semantic applications, and the Semantic Web — the representation and integration of multimedia knowledge structures on the basis of semantic models. These two trends flow together in applications such as Wikipedia, where authors collectively create structured information that can be extracted and used to enhance access to and use of information sources. Recent discussion has focused on how existing bibliographic standards can be expressed as Semantic Metadata for Web vocabularies to facilitate the ingration of library and cultural heritage data with other types of data. Harnessing the efforts of content providers and end-users to link, tag, edit, and describe their Semantic and information in interoperable ways (”participatory metadata”) is a key step towards providing knowledge environments that are scalable, self-correcting, and evolvable. Social Applications DC-2008 will explore conceptual and practical issues in the development and deployment of semantic and social applications to meet the needs of specific communities of practice. Edited by Jane Greenberg and Wolfgang Klas DC-2008
    [Show full text]
  • From the Semantic Web to Social Machines
    ARTICLE IN PRESS ARTINT:2455 JID:ARTINT AID:2455 /REV [m3G; v 1.23; Prn:25/11/2009; 12:36] P.1 (1-6) Artificial Intelligence ••• (••••) •••–••• Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint From the Semantic Web to social machines: A research challenge for AI on the World Wide Web ∗ Jim Hendler a, , Tim Berners-Lee b a Tetherless World Constellation, RPI, United States b Computer Science and AI Laboratory, MIT, United States article info abstract Article history: The advent of social computing on the Web has led to a new generation of Web Received 24 September 2009 applications that are powerful and world-changing. However, we argue that we are just Received in revised form 1 October 2009 at the beginning of this age of “social machines” and that their continued evolution and Accepted 1 October 2009 growth requires the cooperation of Web and AI researchers. In this paper, we show how Available online xxxx the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research. © 2009 Elsevier B.V. All rights reserved. Much has been written about the profound impact that the World Wide Web has had on society. Yet it is primarily in the past few years, as more interactive “read/write” technologies (e.g. Wikis, blogs and photo/video sharing) and social network- ing sites have proliferated, that the truly profound nature of this change is being felt. From the very beginning, however, the Web was designed to create a network of humans changing society empowered using this shared infrastructure.
    [Show full text]
  • A New Generation Digital Content Service for Cultural Heritage Institutions
    A New Generation Digital Content Service for Cultural Heritage Institutions Pierfrancesco Bellini, Ivan Bruno, Daniele Cenni, Paolo Nesi, Michela Paolucci, and Marco Serena Distributed Systems and Internet Technology Lab, DISIT, Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, Italy [email protected] http://www.disit.dsi.unifi.it Abstract. The evolution of semantic technology and related impact on internet services and solutions, such as social media, mobile technologies, etc., have de- termined a strong evolution in digital content services. Traditional content based online services are leaving the space to a new generation of solutions. In this paper, the experience of one of those new generation digital content service is presented, namely ECLAP (European Collected Library of Artistic Perform- ance, http://www.eclap.eu). It has been partially founded by the European Commission and includes/aggregates more than 35 international institutions. ECLAP provides services and tools for content management and user network- ing. They are based on a set of newly researched technologies and features in the area of semantic computing technologies capable of mining and establishing relationships among content elements, concepts and users. On this regard, ECLAP is a place in which these new solutions are made available for inter- ested institutions. Keywords: best practice network, semantic computing, recommendations, automated content management, content aggregation, social media. 1 Introduction Traditional library services in which the users can access to content by searching and browsing on-line catalogues obtaining lists of references and sporadically digital items (documents, images, etc.) are part of our history. With the introduction of web2.0/3.0, and thus of data mining and semantic computing, including social media and mobile technologies most of the digital libraries and museum services became rapidly obsolete and were constrained to rapidly change.
    [Show full text]
  • Enterprise Data Classification Using Semantic Web Technologies
    Enterprise Data Classification Using Semantic Web Technologies David Ben-David1, Tamar Domany2, and Abigail Tarem2 1 Technion – Israel Institute of Technology, Haifa 32000, Israel [email protected] 2 IBM Research – Haifa, University Campus, Haifa 31905, Israel {tamar,abigailt}@il.ibm.com Abstract. Organizations today collect and store large amounts of data in various formats and locations. However they are sometimes required to locate all instances of a certain type of data. Good data classification allows marking enterprise data in a way that enables quick and efficient retrieval of information when needed. We introduce a generic, automatic classification method that exploits Semantic Web technologies to assist in several phases in the classification process; defining the classification requirements, performing the classification and representing the results. Using Semantic Web technologies enables flexible and extensible con- figuration, centralized management and uniform results. This approach creates general and maintainable classifications, and enables applying se- mantic queries, rule languages and inference on the results. Keywords: Semantic Techniques, RDF, Classification, modeling. 1 Introduction Organizations today collect and store large amounts of data in various formats and locations. The data is then consumed in many places, sometimes copied or cached several times, causing valuable and sensitive business information to be scattered across many enterprise data stores. When an organization is required to meet certain legal or regulatory requirements, for instance to comply with regulations or perform discovery during civil litigation, it becomes necessary to find all the places where the required data is located. For example, if in order to comply with a privacy regulation an organization is required to mask all Social Security Numbers (SSN) when delivering personal information to unauthorized entities, all the occurrences of SSN must be found.
    [Show full text]
  • A Multimodal Approach for Automatic WS Semantic Annotation
    MATAWS: A Multimodal Approach for Automatic WS Semantic Annotation Cihan Aksoy1,2, Vincent Labatut1, Chantal Cherifi1,3 and Jean-François Santucci3, 1 Galatasaray University, Computer Science Department, Ortaköy/İstanbul, Turkey 2 TÜBİTAK, Gebze/Kocaeli, Turkey 3 University of Corsica, Corte, France [email protected] Abstract. Many recent works aim at developing methods and tools for the processing of semantic Web services. In order to be properly tested, these tools must be applied to an appropriate benchmark, taking the form of a collection of semantic WS descriptions. However, all of the existing publicly available collections are limited by their size or their realism (use of randomly generated or resampled descriptions). Larger and realistic syntactic (WSDL) collections exist, but their semantic annotation requires a certain level of automation, due to the number of operations to be processed. In this article, we propose a fully automatic method to semantically annotate such large WS collections. Our approach is multimodal, in the sense it takes advantage of the latent semantics present not only in the parameter names, but also in the type names and structures. Concept-to-word association is performed by using Sigma, a mapping of WordNet to the SUMO ontology. After having described in details our annotation method, we apply it to the larger collection of real-world syntactic WS descriptions we could find, and assess its efficiency. Keywords: Web Service, Semantic Web, Semantic Annotation, Ontology, WSDL, OWL-S. 1 Introduction The semantic Web encompasses technologies which can make possible the generation of the kind of intelligent documents imagined ten years ago [1].
    [Show full text]
  • Semantic Computing
    SEMANTIC COMPUTING Lecture 12: Ontology Learning: Introduction Dagmar Gromann International Center For Computational Logic TU Dresden, 19 December 2018 Overview • Defining ontology • Introduction to ontology learning Dagmar Gromann, 19 December 2018 Semantic Computing 2 What is an ontology? Dagmar Gromann, 19 December 2018 Semantic Computing 3 What is an ontology? • Ontology (no plural = uncountable): philosophical study of being (existence, reality, categories of beings, etc.) • ontology (ontologies = countable): “formal, explicit specification of shared conceptualizations.’ (Studer et al. 1998: 186) • computational artifact designed with a purpose in mind • represented in a formal language to allow for the processing, reusing, and sharing of knowledge among humans and machines Studer, Rudi, Benjamins, Richard V., and Fensel, Dieter (1998), ’Knowledge Engineering: Principles and Methods’, Data & Knowledge Engineering, 25 (1-2), 161-198. Dagmar Gromann, 19 December 2018 Semantic Computing 4 Specification? Formal? A formal, explicit specification of shared conceptualizations; ideally an ontology: • is a model of (some aspect of) the world • captures a shared understanding of the domain of interest, a shared conceptualization • defines a vocabulary relevant to the domain and interpreted the same way by different users • specifies the meaning of the vocabulary in an explicit manner and often in a formal specification language Two main parts: • structure of the model = set of axioms • particular objects and situations = set of instances Dagmar Gromann,
    [Show full text]
  • Exploiting Semantic Web Knowledge Graphs in Data Mining
    Exploiting Semantic Web Knowledge Graphs in Data Mining Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universit¨atMannheim presented by Petar Ristoski Mannheim, 2017 ii Dekan: Dr. Bernd Lübcke, Universität Mannheim Referent: Professor Dr. Heiko Paulheim, Universität Mannheim Korreferent: Professor Dr. Simone Paolo Ponzetto, Universität Mannheim Tag der mündlichen Prüfung: 15 Januar 2018 Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many in- formation systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations be- tween them, which can be utilized in various natural language processing, infor- mation retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks.
    [Show full text]
  • Semantic Data Mining: a Survey of Ontology-Based Approaches
    Semantic Data Mining: A Survey of Ontology-based Approaches Dejing Dou Hao Wang Haishan Liu Computer and Information Science Computer and Information Science Computer and Information Science University of Oregon University of Oregon University of Oregon Eugene, OR 97403, USA Eugene, OR 97403, USA Eugene, OR 97403, USA Email: [email protected] Email: [email protected] Email: [email protected] Abstract—Semantic Data Mining refers to the data mining tasks domain knowledge can work as a set of prior knowledge of that systematically incorporate domain knowledge, especially for- constraints to help reduce search space and guide the search mal semantics, into the process. In the past, many research efforts path [8], [9]. Further more, the discovered patterns can be have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge cleaned out [49], [48] or made more visible by encoding them engineering has enriched the family of domain knowledge, espe- in the formal structure of knowledge engineering [76]. cially formal semantics and Semantic Web ontologies. Ontology is To make use of domain knowledge in the data mining pro- an explicit specification of conceptualization and a formal way to cess, the first step must account for representing and building define the semantics of knowledge and data. The formal structure the knowledge by models that the computer can further access of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce and process. The proliferation of knowledge engineering (KE) general concepts of semantic data mining.
    [Show full text]
  • Ontology Engineering for the Design and Implementation of Personal Pervasive Lifestyle Support Michael A
    Ontology Engineering for the Design and Implementation of Personal Pervasive Lifestyle Support Michael A. van Bekkum Hans-Ulrich Krieger Mark A. Neerincx TNO DFKI GmbH TNO Kampweg 5, Stuhlsatzenhausweg 3 Kampweg 5, 3769 DE, Soesterberg, Netherlands D-66123 Saarbrücken, Germany 3769 DE, Soesterberg, Netherlands [email protected] [email protected] [email protected] Frank Kaptein Bernd Kiefer Rifca Peters Delft University of Technology DFKI GmbH Delft University of Technology P.O. Box 5, Stuhlsatzenhausweg 3 P.O. Box 5, 2600 AA, Delft, Netherlands D-66123 Saarbrücken, Germany 2600 AA, Delft, Netherlands [email protected] [email protected] [email protected] Stefania Racioppa DFKI GmbH Stuhlsatzenhausweg 3 D-66123 Saarbrücken, Germany [email protected] ABSTRACT 1. INTRODUCTION The PAL project1 is developing an embodied conversational agent In Europe, an increasing number of about 140,000 children (<14 (robot and its avatar), and applications for child-agent activities year) have Type 1 Diabetes Mellitus (T1DM) [4]. The PAL that help children from 8 to 14 years old to acquire the required project develops an Embodied Conversational Agent (ECA: robot knowledge, skills, and attitude for adequate diabetes self- and its avatar) and several applications for child-agent activities management. Formal and informal caregivers can use the PAL (e.g., playing a quiz and maintaining a timeline with the agent) system to enhance their supportive role for this self-management that help these children to enhance their self-management . learning process. We are developing a common ontology (i) to PAL is part of a joint cognitive system in which humans and support normative behavior in a flexible way, (ii) to establish mutual understanding in the human-agent system, (iii) to integrate agents share information and learn to improve self-management.
    [Show full text]
  • Leveraging Protection and Efficiency of Query Answering in Heterogenous RDF Data Using Blockchain
    Leveraging protection and efficiency of query answering in heterogenous RDF data using blockchain Sara Hosseinzadeh Kassani, Kevin A. Schneider, Ralph Deters Department of Computer Science, University of Saskatchewan, Canada [email protected], [email protected], [email protected] Abstract— In recent years, the digital world has experienced a massive amount of data being captured in various domain due to improvements in technology. Accordingly, big data management has emerged for storing, managing and extracting valuable knowledge from collected data. Due to the explosion of the amount of data, developing tools for accurate and timely integration of inherently heterogeneous data has become a critical need. In the first part of this study, we focus on a semantic data integration approach with a case study of a plant ontology to provide a uniform query interface between users and different data sources. In the second part of this study, we propose a distributed Hyperledger-based architecture to ensure data security and privacy preservation in a semantic data integration framework. Data privacy and security can potentially be violated by unauthorized users, or malicious entities. The proposed view layer architecture between heterogeneous data sources and user interface layer using distributed Hyperledger can ensure only authorized users have access to the data sources in order to protect the system against unauthorized violation and determine the degree of users’ permission for read and write access. Keywords— semantic data integration, blockchain, security, query processing 1. Introduction The World Wide Web (WWW), invented by Tim Berners Lee, is considered as one of the main sources for accessing information and is based primarily on Hypertext Markup Language (HTML), Uniform Resource Locator (URL), and Hypertext Transfer Protocol (HTTP) [1].
    [Show full text]
  • Computing for the Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web
    Wright State University CORE Scholar The Ohio Center of Excellence in Knowledge- Kno.e.sis Publications Enabled Computing (Kno.e.sis) 2010 Computing for the Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web Amit P. Sheth Wright State University - Main Campus, [email protected] Follow this and additional works at: https://corescholar.libraries.wright.edu/knoesis Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Sheth, A. P. (2010). Computing for the Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web. IEEE Internet Computing, 14 (1), 88-91. https://corescholar.libraries.wright.edu/knoesis/543 This Article is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. Amit Sheth, "Computing for Human Experience: Semantics empowered Sensors, Services, and Social Computing on Ubiquitous Web," in IEEE Internet Computing - Vision Issue (V. Cerf and M. Singh, Eds.), vol. 14, no. 1, pp. 88-91, Jan./Feb. 2010. Semantics & Services Computing for Human Experience Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web Amit Sheth • Wright State University n his influential paper “The Computer for focusing on humans interacting with a technol- the 21st Century,” Mark Weiser talked about ogy or system, CHE will feature technology-rich I making machines fit the human environ- human surroundings that often initiate interac- ment instead of forcing humans to enter the tions.
    [Show full text]
  • A Semantic Engine for Internet of Things: Cloud, Mobile Devices and Gateways
    A Semantic Engine for Internet of Things: Cloud, Mobile Devices and Gateways Amelie Gyrard, Soumya Kanti Datta, Christian Bonnet Karima Boudaoud Mobile Communication Rainbow team Eurecom Laboratoire I3S-CNRS/UNSA Biot, France Biot, France Email: fgyrard, dattas, [email protected] Email: [email protected] Abstract—Semantics is becoming a requirement in Internet of The semantic engine addresses such requirements. The Things (IoT). Recent works underline future challenges for IoT: main novelty of this work is to show that the semantic engine (1) integrate semantic web technologies, (2) provide interoper- can be integrated in different components of IoT architectures: ability, (3) interpret IoT data, (4) ease the development of IoT cloud, mobile devices and gateways to fulfill the requirements applications, and (5) fit to the different requirements of people mentioned above of different applications and the needs of and applications. In this paper, we design a semantic engine to various existing projects. meet these requirements and integrate it in different components available in IoT architectures. To the best of our knowledge, the The rest of the paper is structured as follows. Section II main novelty of this paper is the semantic engine flexible enough presents the state of the art and clearly explains the limitations. for different IoT architectures where semantics is integrated in: Section III describes semantic-based IoT architectures for (1) cloud, (2) devices such as smart phones or tablets, and (3) M2M gateways. As proof of concepts, we discuss deployment cloud, mobile devices and gateways. Section IV is focused on of the proposed semantic engine at cloud systems and mobile proof of concepts.
    [Show full text]