A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Query Tools and Methods <s> Despite the recent surge of research in query processing over data streams, little attention has been devoted to defining precise semantics for continuous queries over streams. We first present an abstract semantics based on several building blocks: formal definitions for streams and relations, mappings among them, and any relational query language. From these basics we define a precise interpretation for continuous queries over streams and relations. We then propose a concrete language, CQL (for Continuous Query Language), which instantiates the abstract semantics using SQL as the relational query language and window specifications derived from SQL-99 to map from streams to relations. We have implemented most of the CQL language in a Data Stream Management System at Stanford, and we have developed a public repository of data stream applications that includes a wide variety of queries expressed in CQL. <s> BIB001 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Query Tools and Methods <s> Wepresent anovel sensor network queryprocessing architecture that(a)covers allthequeryoptimization phases thatarerequired tomap a declarative querytoexecutable code;and(b)doessofora moreexpressive querylanguage thanhasheretofore beensupported oversensor networks. The architecture isfounded ontheviewthatasensor network truly is adistributed computing infrastructure, albeit averyconstrained one.As such,we address theproblemofhow todevelop a comprehensive optimizer foranexpressive declarative continuous querylanguage overacquisitional streams asoneoffinding extensions toaclassical distributed queryprocessing architecture thatcontend withthepeculiarities ofsensornetworks asan environment fordistributed computing. <s> BIB002 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Query Tools and Methods <s> 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. <s> BIB003
As above mentioned, sensor data stream are an unbounded sequence values, and each value has a timestamp to represent the value of happened time. People often are interest in data values of the latest stream data, while they don't concerned about the previous tuples. Traditional SQL query adapts to the storage data, while the stream query requires continuous long-term queries. In order to realize the stream query, a few continuous query languages based on SQL appears. Such as CQL BIB001 , SNEEql BIB002 , Esper EPL  , etc. These languages have ability to deal with time constraints and the infinite characteristics of stream. They can put forward the concept of the window and use traditional relational operators to execute continuous query BIB003 .