You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query 
Chapter Title:  The Mapping from Stream Data to Ontology 
1. Abstract of Cited Paper (BIB001):  Sensor networks are often deployed with the purpose of providing data to large-scale information management and GIS systems, or to collect measurements for specific scientific experiments. The benefits of such use are clear and widely accepted. The reuse of observations in low-cost, lightweight, web applications and mashups is a further compelling use case for sensor networks, but requires provision of data through simple mechanisms, readily accessible, that are quick to develop with. To enable the latter while maintaining support for larger applications and, to increase information utility, links to and from other datasets, we propose a domain-driven approach that embodies REST and Linked Data principles using a common semantic framework that underpins a separation of concerns between domain models, sensor observation infrastructure, and Application Programming Interfaces (APIs) while maintaining information consistency. We describe a reusable, reconfigurable, web service that realises this design and can be deployed to provide access to multiple sources of sensor information, including databases and streaming data, with flexible semantic configuration of the API and domain mapping. 
2. Abstract of Cited Paper (BIB002):  Sensor network deployments are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical properties in real time. However, in large-scale deployments it becomes hard to effectively exploit the data captured by the sensors, since there is no precise information about what devices are available and what properties they measure. Even when metadata is available, users need to know low-level details such as database schemas or names of properties that are specific to a device or platform. Therefore the task of coherently searching, correlating and combining sensor data becomes very challenging. We propose an ontology-based approach, that consists in exposing sensor observations in terms of ontologies enriched with semantic metadata, providing information such as: which sensor recorded what, where, when, and in which conditions. For this, we allow defining virtual semantic streams, whose ontological terms are related to the underlying sensor data schemas through declarative mappings, and can be queried in terms of a high level sensor network ontology. 
3. Abstract of Cited Paper (BIB003):  The Internet of Things is rapidly developing in recent years. A number of devices connect with the Internet. Hence, the interoperability, which is the access and interpretation of unambiguous data, is strongly needed by distributed and heterogeneous devices. The semantics promotes the interoperability in the Internet of Things using ontology to provide precise definition of concepts and relations. In this paper, we demonstrate the importance of the semantics in three aspects: firstly, the semantics means that the machines could understand and respond to the human command. Secondly, the semantics is mainly reflected in the ontology of the physical world. Thirdly, the semantics is important for interoperability, data integration and reasoning. Then, we introduce a semantic approach to construct an environment observation system in the Internet of Things. The environment observation system contains three major components, the first is the ontology of the environment observation system, the second is the semantic map of the environment observation system, and the last is exposing the observation data. Finally, the system publishes the environment observation data on the Web successfully. The environment observation system with semantics provides a better service in the Internet of Things 
4. Abstract of Cited Paper (BIB004):  Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. ::: Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. ::: Our specific contributions are: • A representation of sensor data time series that captures gradient information ::: that is useful to characterize types of sensor data. ::: • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series. 
5. Abstract of Cited Paper (BIB005):  We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting, $\mathcal{T}$ -mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality. 
6. Abstract of Cited Paper (BIB006):  Despite the signicant number of existing tools, incorporating data from multiple sources and dierent formats into the Linked Open Data cloud remains complicated. No mapping formalisation exists to dene how to map such heterogeneous sources into rdf in an integrated and interoperable fashion. This paper introduces the rml mapping language, a generic language based on an extension over r2rml, the w3c standard for mapping relational databases into rdf. Broadening r2rml’s scope, the language becomes source-agnostic and extensible, while facilitating the denition of mappings of multiple heterogeneous sources. This leads to higher integrity within datasets and richer interlinking among resources.

Literature Review:
The core issue of semantic data integration process is to find and generate the mapping, so how to realize the mapping from the sensor stream data to the ontology is the issue which we need to solve. In this issue, we must consider an important thing that the sensor data is the stream data. The stream data is an infinite sequence of tuples and with timestamp, so in the mapping process, we must consider the time characteristic. The mapping from sensor stream data to the ontology has two main methods: (1) Hard coding method: The correspondence between stream data and ontology is defined in programs. Different fields and different stream data format need different coding. The stream data will be generated to RDF files according to the program coding. (2) Mapping language method. The mapping languages can express the mappings from relational databases to RDF datasets. They describe the mapping relationships between the basic elements (such as relationship or attributes etc.,) of the relational database and the basic elements (such as concepts, attributes and relationship etc.,) of the RDFS/OWL ontology. They will provide a set of well-defined semantic primitives to describe the mapping relationships. The mapping of stream data and the ontology can be added manually or automatically by programming. Most of the mapping languages have mapping processor which can fill the data of the relational databases in the ontology automatically. The frequently-used mapping languages are: D2RQ BIB002 BIB004 , R 2 O BIB001 and R2RML BIB003 BIB005  , RML BIB007 , Ontop BIB006 , S 2 O [23].