8 research outputs found
RDFViewS: A Storage Tuning Wizard for RDF Applications
In recent years, the significant growth of RDF data used in numerous
applications has made its efficient and scalable manipulation an important
issue. In this paper, we present RDFViewS, a system capable of choosing the
most suitable views to materialize, in order to minimize the query response
time for a specific SPARQL query workload, while taking into account the view
maintenance cost and storage space constraints. Our system employs practical
algorithms and heuristics to navigate through the search space of potential
view configurations, and exploits the possibly available semantic information -
expressed via an RDF Schema - to ensure the completeness of the query
evaluation
View Selection in Semantic Web Databases
We consider the setting of a Semantic Web database, containing both explicit
data encoded in RDF triples, and implicit data, implied by the RDF semantics.
Based on a query workload, we address the problem of selecting a set of views
to be materialized in the database, minimizing a combination of query
processing, view storage, and view maintenance costs. Starting from an existing
relational view selection method, we devise new algorithms for recommending
view sets, and show that they scale significantly beyond the existing
relational ones when adapted to the RDF context. To account for implicit
triples in query answers, we propose a novel RDF query reformulation algorithm
and an innovative way of incorporating it into view selection in order to avoid
a combinatorial explosion in the complexity of the selection process. The
interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201
Automatic physical database design : recommending materialized views
This work discusses physical database design while focusing on the problem of selecting materialized views for improving the performance of a database system. We first address the satisfiability and implication problems for mixed arithmetic constraints. The results are used to support the construction of a search space for view selection problems. We proposed an approach for constructing a search space based on identifying maximum commonalities among queries and on rewriting queries using views. These commonalities are used to define candidate views for materialization from which an optimal or near-optimal set can be chosen as a solution to the view selection problem. Using a search space constructed this way, we address a specific instance of the view selection problem that aims at minimizing the view maintenance cost of multiple materialized views using multi-query optimization techniques. Further, we study this same problem in the context of a commercial database management system in the presence of memory and time restrictions. We also suggest a heuristic approach for maintaining the views while guaranteeing that the restrictions are satisfied. Finally, we consider a dynamic version of the view selection problem where the workload is a sequence of query and update statements. In this case, the views can be created (materialized) and dropped during the execution of the workload. We have implemented our approaches to the dynamic view selection problem and performed extensive experimental testing. Our experiments show that our approaches perform in most cases better than previous ones in terms of effectiveness and efficiency
Моделі та інформаційні технології проектування і управління в складних системах
Мета роботи: розробка теоретико-методологічних і науково-практичних основ розробки інформаційних технологій управління та інформаційних систем на потреб галузей суспільного виробництва та соціальної сфери
Multi-Objective Materialized View Selection in Data-Intensive Flows
In this thesis we present Forge, a tool for automating multi-objective materialization of intermediate results in data-intensive flows, driven by a set of different quality objectives. We report initial evaluation results, showing the feasibility and efficiency of our approach