Temporal Query Answering in DL-Lite with Negation

Ontology-based query answering augments classical query answering in databases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We investigate temporal query answering w.r.t. ontologies formulated in DL-Lite, a family of description logics that captures the conceptual features of relational databases and was tailored for efficient query answering. We consider a recently proposed temporal query language that combines conjunctive queries with the operators of propositional linear temporal logic (LTL). In particular, we consider negation in the ontology and query language, and study both data and combined complexity of query entailment.

Practical Querying of Temporal Data via OWL 2 QL and SQL:2011

We develop a practical approach to querying temporal data stored in temporal SQL:2011 databases through the semantic layer of OWL 2 QL ontologies. An interval-based temporal query language (TQL), which we propose for this task, is defined via naturally characterizable combinations of temporal logic with conjunctive queries. This foundation warrants well-defined semantics and formal properties of TQL querying. In particular, we show that under certain mild restrictions the data complexity of query answering remains in AC$^0$, i.e., as in the usual, nontemporal case. On the practical side, TQL is tailored specifically to offer maximum expressivity while preserving the possibility of reusing standard first-order rewriting techniques and tools for OWL 2 QL.

Intelligent Querying Techniques for Sensor Data Fusion

Sensor data fusion imposes a number of novel requirements on query languages and query processing techniques. A spatial/temporal query language called SQL has been proposed to support the retrieval of multimedia information from multiple sources and databases. This chapter investigates intelligent querying techniques including fusion techniques, multimedia data transformations, interactive progressive query building and SQL query processing techniques using sensor data fusion. The authors illustrate and discuss tasks and query patterns for information fusion, provide a number of examples of iterative queries and show the effectiveness of SQL in a command-action scenario.

Query Operators in Temporal XML Databases

The amount of data available in XML is rapidly increasing and at the same time the price of mass storage is rapidly decreasing, and this makes it possible to store larger amounts of data. The contents of a database or data warehouse are seldom static. New documents are created, documents are deleted and, more important, documents are updated. In many cases, one wants to be able to search in historical (old) versions, retrieve documents that were valid at a certain time, query changes to documents, and so forth. (Note that although this process is somewhat similar to general document versioning maintenance, the aspect of time makes possibilities and appropriate solutions different.) The “easiest” way to do this is to store all versions of all documents in the database and use a middleware layer to convert temporal query language statements into conventional statements, executed by an underlying database system (an example of such a system is TeXOR; Nørvåg, Limstrand, & Myklebust, 2003). Although this approach makes the introduction of temporal support easier, it can be difficult to achieve good performance: temporal query processing is in general costly, and the cost of storing the complete document versions can be high. Thus, a temporal XML database system is necessary.