Efficient evaluation of generalized path pattern queries on XML data

Finding the occurrences of structural patterns in XML data is a key operation in XML query processing. Existing algorithms for this operation focus almost exclusively on path-patterns or tree-patterns. Requirements in flexible querying of XML data have motivated recently the introduction of query languages that allow a partial specification of path-patterns in a query. In this paper, we focus on the efficient evaluation of partial path queries, a generalization of path pattern queries. Our approach explicitly deals with repeated labels (that is, multiple occurrences of the same label in a query). We show that partial path queries can be represented as rooted dags for which a topological ordering of the nodes exists. We present three algorithms for the efficient evaluation of these queries under the indexed streaming evaluation model. The first one exploits a structural summary of data to generate a set of path-patterns that together are equivalent to a partial path query. To evaluate these path-patterns, we extend PathStack so that it can work on path-patterns with repeated labels. The second one extracts a spanning tree from the query dag, uses a stack-based algorithm to find the matches of the root-to-leaf paths in the tree, and merge-joins the matches to compute the answer. Finally, the third one exploits multiple pointers of stack entries and a topological ordering of the query dag to apply a stack-based holistic technique. An analysis of the algorithms and extensive experimental evaluation shows that the holistic algorithm outperforms the other ones

Exploiting Hierarchical Clustering in Evaluating Multidimensional Aggregation Queries

Multidimensional aggregation queries constitute the single most important class of queries for data warehousing applications and decision support systems. The bottleneck in the evaluation of these queries is the join of the usually huge fact table with the restricted dimension tables (star-join). Recently,a multidimensional hierarchical clustering schema for star schemas is suggested. Subsequently,query evaluation plans for multidimensional queries appeared that essentially implement a star join as a multidimensional range restriction. We present a number of transformations for such plans. The transformations place grouping/aggregation operations before joins and safely prune aggregated tuples. They can be applied at no or minimal extra I/O cost. We show how these transformations can be used to construct a new evaluation plan for grouping/aggregation queries over multidimensional hierarchically clustered schemas. The new plan improves previous results by grouping and aggregating tuples and by excluding aggregated tuples from further consideration at an early stage of the computation of a query

Detecting redundancy in Data Warehouse Evolution

. A Data Warehouse #DW# can be abstractly seen as a set of materialized views de#ned over a set of remote data sources. A DWis intended to satisfy a set of queries. The views materialized in a DW relate to each other in a complex manner, through common subexpressions, in order to guarantee high query performance and low view maintenance cost. DWs are time varying. As time passes new materialized views are added in order to satisfy new queries or for performance reasons while old queries are dropped. The evolution of a DW can result in a redundant set of materialized views. In this paper we address the problem of detecting redundant views in a given DW view selection, that is, views that can be removed from the DW without negatively a#ecting the query evaluation or the view maintenance process. Using an AND#OR dag representation for multiple queries and views, we #rst provide a method for detecting materialized views that are not needed in the process of propagating source..

Updating Object Oriented Schema Structures Viewed as Logical Theories

We consider Object Oriented schema structures supporting isa relationships and multiple inheritance extended with features such as disjointness of classes and class intersection inclusion into other classes as well as negations of these statements. We provide an advisor that supplies the user with possible update alternatives when trying to modify the schema. Formally we investigate the problem of updating object schema structures formalized as first order monadic theories. Based on a formal system, we provide incremental update algorithms. We show the problem to be co-NP-complete in general. By introducing a rule-goal (hyper)graph, nontrivial conditions are presented by case which guarantee its reduction to the polynomial level. Keywords: Object Oriented schema, Incremental updating, Monadic theory, Algorithms, Complexity. Research carried out as part of the ERCIM Fellowship Program and financed by the Commission of the European Communities 1 Introduction In the domain of Object ..

Designing Data Warehouses

A Data Warehouse (DW) is a database that collects and stores data from multiple remote and heterogeneous information sources. When a query is posed, it is evaluated locally, without accessing the original information sources. In this paper we deal with the issue of designing a DW, in the context of the relational model, by selecting a set of views to materialize in the DW. First, we briefly present a theoretical framework for the DW design problem, which concerns the selection of a set of views that (a) fit in the space allocated to the DW, (b) answer all the queries of interest, and (c) minimize the total query evaluation and view maintenance cost. We then formalize the DW design problem as a state space search problem by taking into account multiquery optimization over the maintenance queries (i.e. queries that compute changes to the materialized views) and the use of auxiliary views for reducing the view maintenance cost. Finally, incremental algorithms and heuristics for pruning the..