On Defining SPARQL with Boolean Tensor Algebra

The Resource Description Framework (RDF) represents information as subject-predicate-object triples. These triples are commonly interpreted as a directed labelled graph. We propose an alternative approach, interpreting the data as a 3-way Boolean tensor. We show how SPARQL queries - the standard queries for RDF - can be expressed as elementary operations in Boolean algebra, giving us a complete re-interpretation of RDF and SPARQL. We show how the Boolean tensor interpretation allows for new optimizations and analyses of the complexity of SPARQL queries. For example, estimating the size of the results for different join queries becomes much simpler

The accessibility dimension for structured document retrieval

Structured document retrieval aims at retrieving the document components that best satisfy a query, instead of merely retrieving pre-defined document units. This paper reports on an investigation of a tf-idf-acc approach, where tf and idf are the classical term frequency and inverse document frequency, and acc, a new parameter called accessibility, that captures the structure of documents. The tf-idf-acc approach is defined using a probabilistic relational algebra. To investigate the retrieval quality and estimate the acc values, we developed a method that automatically constructs diverse test collections of structured documents from a standard test collection, with which experiments were carried out. The analysis of the experiments provides estimates of the acc values

Evaluation of optimization techniques for aggregation

Aggregations are almost always done at the top of operator tree after all selections and joins in a SQL query. But actually they can be done before joins and make later joins much cheaper when used properly. Although some enumeration algorithms considering eager aggregation are proposed, no sufficient evaluations are available to guide the adoption of this technique in practice. And no evaluations are done for real data sets and real queries with estimated cardinalities. That means it is not known how eager aggregation performs in the real world. In this thesis, a new estimation method for group by and join combining traditional estimation method and index-based join sampling is proposed and evaluated. Two enumeration algorithms considering eager aggregation are implemented and compared in the context of estimated cardinality. We find that the new estimation method works well with little overhead and that under certain conditions, eager aggregation can dramatically accelerate queries