Tools, Languages, Methodologies for Representing Semantics on the Web of Things
Description
The various aspects studied in this book include Multi-Model Multi-Platform (SHM3P) databases for the IoT, clustering techniques for discovery services for the semantic IoT, dynamic security testing methods for the Semantic Web of Things, Semantic Web-enabled IoT integration for a smart city, IoT security issues, the role of the Semantic Web of Things in Industry 4.0, the integration of the Semantic Web and the IoT for e-health, smart healthcare systems to monitor patients, Semantic Web-based ontologies for the water domain, science fiction and searching for a job.
Shikha Mehta is Associate Professor in the Department of CSE & IT, Jaypee Institute of Information Technology, India. Her research interests include machine/deep learning algorithms, nature-inspired computing and social networks analytics.
Sanju Tiwari is Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico, DAAD Post-Doc-Net AI Fellow and PhD co-supervisor at Rai University, India, and has worked as a post-doctoral researcher in OEG, Universidad Politecnica de Madrid, Spain. Her research interests include artificial intelligence, knowledge graphs and ontology engineering.
Patrick Siarry is Professor in automatics and informatics at University Paris Est Créteil, France. His research interests include the design of stochastic global optimization heuristics and their applications to various engineering fields. M.A. Jabbar is Professor and Head of the Department of CSE (AI & ML), Vardhaman College of Engineering, India. His research interests include artificial intelligence, Big Data analytics, bio-informatics and machine learning.
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Persons
Sanju Tiwari is Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico, DAAD Post-Doc-Net AI Fellow and PhD co-supervisor at Rai University, India, and has worked as a post-doctoral researcher in OEG, Universidad Politecnica de Madrid, Spain. Her research interests include artificial intelligence, knowledge graphs and ontology engineering.
Patrick Siarry is Professor in automatics and informatics at University Paris Est Créteil, France. His research interests include the design of stochastic global optimization heuristics and their applications to various engineering fields. M.A. Jabbar is Professor and Head of the Department of CSE (AI & ML), Vardhaman College of Engineering, India. His research interests include artificial intelligence, Big Data analytics, bio-informatics and machine learning.
Content
Preface xi
Shikha MEHTA, Sanju TIWARI, Patrick SIARRY and M.A JABBAR
Chapter 1 The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT 1
Sven GROPPE, Jinghua GROPPE and Tobias GROTH
1.1 Introduction 1
1.2 Databases for multi-model data 5
1.3 Platforms 7
1.4 Variations of SHM3P DBMS 13
1.5 What are the benefits of SHM3P databases for IoT? 14
1.5.1 Data storage and placement 14
1.5.2 Data processing 15
1.5.3 IoT applications 15
1.6 Summary and conclusions 16
1.7 References 16
Chapter 2 A Systematic Review of Ontologies for the Water Domain 21
Sanju TIWARI and Raúl GARCÍA-CASTRO
2.1 Introduction 21
2.2 Literature review 23
2.2.1 Features in the water domain 23
2.2.2 Semantic models in the water domain 24
2.2.3 A comprehensive review of ontologies in the water domain 24
2.3 Applications of ontologies in the water domain 32
2.4 Discussion and conclusion 35
2.5 References 36
Chapter 3 Semantic Web Approach for Smart Health to Enhance Patient Monitoring in Resuscitation 41
Fatima Zahra AMARA, Mounir HEMAM, Meriem DJEZZAR and Moufida MAIMOUR
3.1 Introduction 42
3.2 Background 43
3.2.1 Semantic Web 43
3.2.2 SSN (Semantic Sensor Network) ontology 44
3.3 IoT Smart Health applications and semantics 45
3.4 Proposed approach and implementation 46
3.4.1 Knowledge representation 47
3.4.2 Ontology evaluation 51
3.4.3 Reasoning and querying 51
3.4.4 Linked Data 55
3.5 Conclusion 56
3.6 References 57
Chapter 4 Role of Clustering in Discovery Services for the Semantic Internet of Things 61
Shachi SHARMA
4.1 Introduction 61
4.2 Discovery services in IoT 64
4.2.1 Directory-based architectures 64
4.2.2 Directory-less architectures 66
4.3 Semantic-based architectures 67
4.3.1 Search engine-based 67
4.3.2 ONS DNS-based 68
4.4 Discovery services and clustering 68
4.5 Clustering methods in IoT 69
4.6 Conclusion 71
4.7 References 71
Chapter 5 Dynamic Security Testing Techniques for the Semantic Web of Things: Market and Industry Perspective 75
Dhananjay SINGH CHAUHAN, Gaurav CHOUDHARY, Shishir Kumar SHANDILYA and Vikas SIHAG
5.1 Introduction 75
5.2 Related studies 77
5.3 Background of dynamic security testing techniques 79
5.3.1 Black Box testing techniques 80
5.4 DAST using static analysis 82
5.4.1 Current implementation 82
5.5 DAST using user session 84
5.5.1 Current implementation 84
5.6 DAST using Extended Tainted Mode Model 86
5.6.1 Current implementation 87
5.7 Current issues and research directions 88
5.8 Conclusion 89
5.9 References 89
Chapter 6 SciFiOnto: Modeling, Visualization and Evaluation of Science Fiction Ontologies Based on Indian Contextualization with Automatic Knowledge Acquisition 93
Gerard DEEPAK, Ayush A KUMAR and Sheeba J PRIYADARSHINI
6.1 Introduction 94
6.2 Literature survey 97
6.2.1 Formulation and modeling of ontologies for varied domains of importance 97
6.2.2 Auxiliary automatic and semi-automatic models in ontology synthesis 97
6.2.3 Ontology-driven systems and applications 98
6.2.4 Automatic Knowledge Acquisition systems 99
6.2.5 Science fiction as an independent domain of existence 99
6.3 Modeling and evaluation of the ontology 100
6.3.1 Ontology modeling 100
6.3.2 Ontology visualization 104
6.3.3 Ontology evaluation 107
6.4 Automatic Knowledge Acquisition model 111
6.4.1 System architecture 111
6.4.2 Acquisition algorithm 113
6.5 Conclusion 119
6.6 References 119
Chapter 7 Semantic Web-Enabled IoT Integration for a Smart City 123
Ronak PANCHAL and Fernando ORTIZ-RODRIGUEZ
7.1 Introduction: Semantic Web and sensors 123
7.2 Motivation and challenge 124
7.3 Literature review 124
7.4 Implementation of forest planting using SPARQL queries 125
7.4.1 Architecture sketch with conceptual diagram 125
7.4.2 Implementation ontology from the dataset 126
7.4.3 Technologies and tools 129
7.5 Conclusion 136
7.6 References 136
Chapter 8 Heart Rate Monitoring Using IoT and AI 139
Kalpana MURUGAN, Cherukuri NIKHIL KUMAR, Donthu Sai SUBASH and Sangam DEVA KISHORE REDDY
8.1 Introduction 140
8.2 Literature survey 142
8.3 Heart rate monitoring system 145
8.4 Results and discussion 149
8.5 Conclusion and future works 152
8.6 References 152
Chapter 9 IoT Security Issues and Its Defensive Methods 155
Keshavi NALLA and Seshu VARDHAN POTHABATHULA
9.1 Introduction 155
9.2 IoT security architecture 158
9.2.1 Typical IoT architecture 158
9.2.2 Centralized and distributed approaches over the IoT security architecture 161
9.2.3 IoT security architecture based on blockchain 163
9.2.4 Internet of Things security architecture: trust zones and boundaries 164
9.2.5 Threat modeling in IoT security architecture 168
9.3 Specific security challenges and approaches 170
9.3.1 Identity and authentication 170
9.3.2 Access control 171
9.3.3 Protocol and network security 172
9.3.4 Privacy 172
9.3.5 Trust and governance 173
9.3.6 Fault tolerance 173
9.4 Methodologies used for securing the systems 174
9.4.1 PKI and digital certificates 174
9.4.2 Network security 174
9.4.3 API security 174
9.4.4 Network access control 175
9.4.5 Segmentation 175
9.4.6 Security gateways 175
9.4.7 Patch management and software updates 175
9.5 Conclusion 176
9.6 References 176
Chapter 10 Elucidating the Semantic Web of Things for Making the Industry 4.0 Revolution a Success 179
Deepika CHAUDHARY and Jaiteg SINGH
10.1 Introduction 179
10.2 Correlation of the Semantic Web of Things with IR4.0 180
10.2.1 Smart machines 181
10.2.2 Smart products 182
10.2.3 Augmented operators 182
10.2.4 The Web of Things 183
10.2.5 Semantic Web of Things 184
10.3 Smart manufacturing system and ontologies 185
10.3.1 Vertical level integration 185
10.3.2 Horizontal level of integration 185
10.3.3 End-to-end integration 185
10.4 Literature survey 188
10.5 Conclusion and future work 190
10.6 References 190
Chapter 11 Semantic Web and Internet of Things in e-Health for Covid-19 195
ANURAG and Naren JEEVA
11.1 Introduction 196
11.2 Dataset 197
11.3 Application of IoT for Covid-19 198
11.3.1 Continuous real-time remote monitoring 198
11.3.2 Remote monitoring using W-kit 198
11.3.3 Early identification and monitoring 198
11.3.4 Continuous and reliable health monitoring 198
11.3.5 ANN-assisted patient monitoring 199
11.3.6 City lockdown monitoring 199
11.3.7 Technologies for tracking and tracing 199
11.3.8 Tracking and tracing suspected cases 199
11.3.9 Anonymity preserving contact tracing model 200
11.3.10 Cognitive radio-based IoT architecture 200
11.3.11 Analyzing reasons for the outbreak 200
11.3.12 Analyzing Covid-19 cases using disruptive technology 200
11.3.13 Post-Covid applications 201
11.4 Semantic Web applications for Covid-19 201
11.4.1 Ontological approach for drug development 202
11.4.2 Early detection and diagnosis 202
11.4.3 Knowledge-based pre-diagnosis system 202
11.4.4 Semantic-based searching for online learning resources 203
11.4.5 Ontology-based physiological monitoring of students 203
11.4.6 Analysis of clinical trials 203
11.4.7 Data annotation of EHRs 204
11.4.8 Disease pattern study 204
11.4.9 Surveillance in primary care 204
11.4.10 Performance assessment of healthcare services 205
11.4.11 Vaccination drives and rollout strategies 205
11.5 Limitations and challenges of IoT and SW models 205
11.6 Discussion 206
11.7 Conclusion 206
11.8 References 207
Chapter 12 Development of a Semantic Web Enabled Job_Search Ontology System 211
Hina J CHOKSHI, Dhaval VYAS and Ronak PANCHAL
12.1 Introduction 211
12.1.1 Ontology 212
12.1.2 Importance of ontology 213
12.1.3 Semantic Web and its solutions 214
12.1.4 Online recruitment scenarios 214
12.2 Review of the related work done for online recruitment 215
12.3 Design of "SearchAJob" ontology for the IT domain 217
12.3.1 Ontology structure 218
12.4 Implementing the proposed ontology 222
12.4.1 Architecture of semantics-based job ontology 223
12.5 Benefits of Semantic Web enabled SearchAJob system 231
12.6 Conclusion and future scope 232
12.7 References 233
List of Authors 237
Index 241
1
The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT
To overcome the difficulties of today's zoo of data models stored and processed in companies, multi-model databases offer a simple way to access and query the data stored using different models. In contrast to other data models, the semantic model introduces an additional abstraction layer for reasoning purposes, offering superior possibilities for data integration. Hence, the semantic model is best suited to act as a glue between different data models. Today's companies are using various platforms such as mobile devices, web, desktops, servers (hardware-accelerated by GPU (Graphical Processing Unit), FPGA (Field-programmable gate array) and in the future, QPU), clouds and post-clouds (e.g. fog and edge computing) to run their applications and databases. In this chapter, we discuss the possibilities for the Internet of Things (IoT) of so called semantic hybrid multi-model multi-platform databases, which use semantic technologies as glue to integrate different data models and run on various platforms, offering the best features of the various data models and platforms.
1.1. Introduction
Today companies use data in various data formats (see Figure 1.1). Web shops are connected with relational databases containing customers and their orders. To exchange data, product catalogs of companies are serialized and transmitted in the XML, JSON or RDF data formats. Graph data is frequently processed due to the importance of social networks today. Unstructured data dominates in social media, like in wikis. Due to their simple way of retrieving the data by just using keys, key-value stores are widely used. Schema-free or schema-less databases are preferred ways to store unstructured data, because they do not require the data to stay in the inflexible corset of a schema. Document stores even support complex data formats under the absence of schemas. The data are hence stored according to and processed using different models (multi-model data (see Lu and Holubová (2019))). The big challenge for today's companies are the synchronization and integration of their multi-model data into a single view of and for the customer (see Kotorov (2003)). Multi-Model Database Management Systems (MM-DBMSs) offer the management of different data models in one single database (see Lu and Holubová (2019)). The alternative architecture principle is polyglot persistence, where applications use several databases at the same time to handle multi-model data (Leberknight 2008). The big disadvantages are that it inherits the limitations of different databases, for example queries and rules are only optimized within one database, but not across connected Database Management Systems (DBMSs) (see Groppe and Groppe (2020) and Groppe (2021)). In Groppe (2021), we propose to use the semantic data model for unifying the other data models: the semantic data model supports ontologies as an additional abstraction layer, which best suits the data integration purposes of other data models.
Figure 1.1. Various data models used in today's companies.
Traditionally mainly running on parallel servers, today DBMSs are operating on various different platforms such as mobile devices, web, desktops, servers (maybe additionally hardware accelerated by GPUs, FPGAs and emergent technologies such as quantum computing), clouds and post-clouds (e.g. fog and edge computing) offering execution environments to run DBMSs1.
Recent trends in programming languages like Kotlin (see JetBrains s.r.o. (2020)) include multi-platform development support to share common code between different platforms such as desktop, server, web, mobile and IoT. In this way the development costs are drastically reduced for a DBMS running on multiple platforms. For example, the Semantic Web DBMS luposdate3000 developed in Kotlin is designed to run fast on parallel servers utilizing the Java virtual machine (JVM) (see Warnke et al. (2021)), and also offers a web app that runs completely in the web browser (see Groppe et al. (2021a,b)). Furthermore, another target platform is the IoT, where luposdate3000 is running on the edge (see Warnke (2022)) with efficient indexing schemes, as proven by experiments with the simulator SIMORA (see Warnke et al. (2022)).
By connecting all of the pieces together (M3P/HM3P/SHM3P), DBMSs are defined as follows (see Groppe (2021)):
DEFINITION 1.1 (M3P/HM3P/SHM3P DBMS).-
A multi-model multi-platform database management system (M3P DBMS) is an MM-DBMS that can be executed on different platforms. A hybrid M3P (HM3P) DBMS spans over different platforms in operation. A Semantic HM3P (SHM3P) DBMS supports a (global) semantic layer (for querying and reasoning purposes) over all platforms of an HM3P DBMS.
Today's M3P DBMSs are usually developed for platforms of the same type (like servers running windows or linux, see Groppe and Groppe (2020) and Groppe (2021)). Only few of them support hybrid clouds, which integrate a (locally installed) private cloud with a public cloud2. In contrast, we envision SHM3P DBMSs operating over platforms of different types (such as IoT and hardware-accelerated parallel servers). In this way the features of different types of databases developed for different platforms can be supported (such as energy-saving on IoT devices and high throughput on servers). For Semantic DBMS, advanced global reasoning capabilities spanning over all platforms need to be developed. Hence, SHM3P databases support any data model at any platform by tightly integrating them with a semantic layer (see Groppe (2021)). For an example installation, see Figure 1.2.
Figure 1.2. SHM3P database spanning over multiple platforms. Here, an SHM3P database replaces an IoT database in an Industry 4.0 scenario (using edge-computing), a GPU-accelerated parallel database (on a parallel server) for archiving and generating long-term statistics of the IoT data, which is further supported by a quantum computer for query and reasoning optimization, a database in the cloud for natural language processing tasks and a mobile database (on mobile devices and infrastructure) for monitoring and controlling the production line in the company. Platforms are marked using italic font. Green text marks discussion about reasoning in these scenarios
(source: Groppe (2021)).
1.2. Databases for multi-model data
Polyglot persistence uses different databases supporting different data models (and maybe running on different platforms) within one application (see Leberknight (2008)). There is a need for federated query languages to formulate queries over heterogeneous data stores within one single query. Examples for federated query languages include the following:
- CloudMdsQL (see Kolev et al. (2016)), which can be used to formulate queries over SQL and NoSQL databases integrated in a prototype with the support of global optimization, and push operations down to the integrated SQL and NoSQL databases as much as possible.
- Zhu and Risch (2011) propose a system to query cloud-based NoSQL such as Google's Bigtable and relational databases with the Google Bigtable query language GQL.
- Apache Drill3 supports interactive ad hoc analysis of large-scale datasets with low-latency handling up to petabytes of data spread across thousands of servers. Drill's optimization techniques include leveraging the datastore's internal processing capabilities in query plans and considering data locality for best query performance.
The integration of diverse data sources by using database connectors (like JDBC drivers) is widely used in commercial multi-store products such as IBM BigInsights, Microsoft HDInsight and Oracle Bigdata Appliance, as well as in open source projects like PrestoDB4. The semantic integration is done in Tatooine (see Bonaque et al. (2016)) using a semantic layer as glue between databases for different data models. However, data processing is limited in all of these polystores, because they do not fully support the optimization of queries across the integrated, but independent data sources.
There is a long history of federation databases (see Hammer and McLeod (1979)) and multi-databases (see Smith et al. (1981)). Their architectures contain a mediator between different autonomous databases. This mediator integrates different databases and data sources by reformulating queries according to a global schema. The reformulated queries follow the native schemes of the integrated databases evaluating these queries. Today, some research efforts about federating databases follow the polyglot persistence approach: for example, DBMS+ (see Lim et al. (2013)) provides unified declarative processing for the integration of several processing and database platforms. Location transparency is offered by BigDAWG (see Elmore et al. (2015)), while running queries against its different integrated systems PostgreSQL, SciDB and Accumulo.
Multi-Model Databases: A multi-model database is one single database for multiple data models, which fully integrates a backend to offer advanced performance, scalability and fault tolerance (see Lu et al. (2018)). Object-Relational DataBase Management Systems (ORDBMSs) were one of the first of this type supporting various data models such as relational, text, XML, spatial and object. ORDBMSs are based on relational databases...
