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 Query Languages 
1. Abstract of Cited Paper (BIB001):  In this paper, we present a temporal extension of the SPARQL query language for RDF graphs. The new language is based on a temporal RDF database model employing triple timestamping with temporal elements, which best preserves the scalability property enjoyed by triple storage technologies, especially in a multi-temporal setting. The proposed SPARQL extensions are aimed at embedding several features of the TSQL2 consensual language designed for temporal relational databases. 
2. Abstract of Cited Paper (BIB002):  Continuous SPARQL (C-SPARQL) is a new language for continuous queries over streams of RDF data. CSPARQL queries consider windows, i.e., the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with knowledge evolving over time. Examples of such application domains include real-time reasoning over sensors, urban computing, and social semantic data. In this paper, we present the C-SPARQL language extensions in terms of both syntax and examples. Finally, we discuss existing applications that already use C-SPARQL and give an outlook on future research opportunities. 
3. Abstract of Cited Paper (BIB003):  Continuous SPARQL (C-SPARQL) is proposed as new language for continuous queries over streams of RDF data. It covers a gap in the Semantic Web abstractions which is needed for many emerging applications, including our focus on Urban Computing. In this domain, sensor-based information on roads must be processed to deduce localized traffic conditions and then produce traffic management strategies. Executing C-SPARQL queries requires the effective integration of SPARQL and streaming technologies, which capitalize over a decade of research and development; such integration poses several nontrivial challenges. In this paper we (a) show the syntax and semantics of the C-SPARQL language together with some examples; (b) introduce a query graph model which is an intermediate representation of queries devoted to optimization; (c) discuss the features of an execution environment that leverages existing technologies; (d) introduce optimizations in terms of rewriting rules applied to the query graph model, so as to efficiently exploit the execution environment; and (e) show evidence of the effectiveness of our optimizations on a prototype of execution environment. 
4. Abstract of Cited Paper (BIB004):  This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time. C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of examples, relative to urban computing applications, that systematically cover the SPARQL extensions. The expression of meaningful queries over streaming data is strictly connected to the availability of aggregation primitives, thus C-SPARQL also includes extensions in this respect. 
5. Abstract of Cited Paper (BIB005):  Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  In this paper, we provide a survey on the models and query languages for temporally annotated RDF. In most of the works, a temporally annotated RDF ontology is essentially a set of RDF triples associated with temporal constraints, where, in the simplest case, a temporal constraint is a validity temporal interval. However, a temporally annotated RDF ontology may also be a set of triples connecting resources with a specific lifespan, where each of these triples is also associated with a validity temporal interval. Further, a temporal RDF ontology may be a set of triples connecting resources as they stand at specific time points. Several query languages for temporally annotated RDF have been proposed, where most of which extend SPARQL or translate to SPARQL. Some of the works provide experimental results while the rest are purely theoretical. 
8. Abstract of Cited Paper (BIB008):  The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations - the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects. 
9. Abstract of Cited Paper (BIB009):  Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose 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. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes. 
10. Abstract of Cited Paper (BIB010):  The availability of streaming information is progressively increasing, thanks to the knowledge management technologies such as ontologies that captures knowledge about this information. The semantic web initiative provides standards such as RDF and OWL, the two most powerful Semantic Web knowledge representation languages. The OWL adds semantics to schemas, and allows for more expressivity than RDF. As far as we know, there are existing querying services, designed for an swering queries over streaming data in RDF format, but there is none for the OWL EL ontological stream, in order to address this short comings for OWL EL ontological streams, in this paper, we focus primarily on the problem of querying and reasoning over OWL ontological streams, specifically how to design and evaluate Continuous SPARQL query over OWL EL ontological streams, in which the queries are evaluated under more expressive semantics of OWL. We address semantic issues, by introducing the add and erase semantics, maintaining relevant erasure for special use. We propose a general and flexible architecture, for querying more expressive OWL ontological streams with the help of a stream reasoner in near real time. This is our first step towards the design and implementation of such a system.

Literature Review:
SPARQL language  is a kind of query language developed for RDF. It can easily query in RDF files. In the Semantic Sensor Web, we need to query the continuous stream data, but SPARQL language can't effectively work. SPARQL language must be extended to achieve the goal of the continuous query of the stream data. The earliest query languages are T-SPARQL BIB002 , TA-SPARQL BIB001 BIB008 , EP-SPARQL BIB006 , C-SPARQL BIB003 BIB004 BIB005 , and stSPARQL   , etc. They all use RDF stream as the data model. The tuples of RDF streams are annotated by timestamp. T-SPARQL is a temporal extension of the SPARQL query language for RDF graphs. It is the RDF database model based on time, and this model uses the triple timestamp with time elements. The query language came from a time query language TSQL2 and inherited part time expression and user friendliness of TSQL2. T-SPARQL is the extension of SPARQL query language in terms of time data types, functions and operators, and it enhances properties of TSQL2. In the coming research, T-SPARQL query engine is devoted to develop to make the queries of the time RDF graphs more convenient. Time-annotated RDF (TA-RDF) is an extension of the RDF model where resources are optionally annotated with a time value. Along with TA-RDF, Time-annotated SPARQL (TA-SPARQL) is designed to express the one-time time-dependent queries common when handling streams and time-varying data. TA-SPARQL goes beyond other query languages for streams in RDF presented previously and base on sliding windows, and allows to exploit the richer temporal relationships represented by the semantic model BIB001 . The TA-RDF and TA-SPARQL have been implemented in a prototype service, integrated with the NCSA Tupelo semantic middleware which can make the stream data and other data integrate and perform unified query. EP-SPARQL is the SPARQL extension query language of event processing. It can query complex events and conduct stream reasoning. EP-SPARQL is put forward in order to be able to query based on stream. It considers the time situation asserted by triples and specifies the complex events through the time position of real-time stream, and use background ontology for stream reasoning. C-SPARQL is the expansion of SPARQL language. It can realize continuous query of RDF data stream. C-SPARQL query puts forward the concept of window. It can query the latest tuples of data stream and observe the continuous data streams. C-SPARQL manages RDF stream tuples locally in data model, at the same time converts the unbounded stream data to a bounded sequence using time window, then the standard operators can be used. Okure, A.S. BIB011 proposes architecture which can query more OWL ontology stream which carry the latest real-time time stream reasoning. The stRDF data model and the stSPARQL query language are developed by Manolis Koubarakis   . stRDF adds the elements of time and space in RDF triples, so it can express spatio-temporal data. stSPARQL is an expansion of SPARQL, and it can query stRDF data. stSPARQL provides a new type of variables: Spatial variables, temporal variables. It can query valid time of triples. In SemsorGrid4Env project, Kostis Kyzirakos Strabon  proposed Strabon, which is a fully implemented semantic geospatial database system. Strabon can be used to store linked geospatial data expressed in stRDF and query them using stSPARQL. The query premise of the above query languages is the existence of the RDF file, namely, the query object is RDF files. But the query based on ontology has another form that is RDF files are not generated. From the user's perspective, SPARQL query language is used to query, and virtual query statements are generated. The SPARQL query languages are transformed to SNEEql query statements aiming at stream data through R2RML or S2O mapping language, and the stream data stored in the relational database can be queried directly. The query results are transformed using the mapping language, so query results are acquired BIB009  BIB007 . In this method, SPARQLStream is the using query language BIB010 . SPARQL Stream is a language that extends C-SPARQL and SNEEql for continuous query processing over streaming data. They all are the idea based on RDF stream, and timestamps are annotated in RDF stream. In SPARQL Stream , these streams are virtual, and they generate query results relying on original data stream. In order to convert SPARQL Stream queries expressed by ontology to the queries according to data sources, a set of mapping must be specified. SNEEql queries that conduct against the data source can be acquired by the mapping, which can use SQL statements to query, and query results can be obtained. The Morph-streams module for SPARQL Stream is a java library that enables the execution of SPARQL Stream queries, using different underlying DSMS or CEP. This tool allows posing SPARQL Stream queries to an existing data source using R2RML mappings.