A data warehouse stores materialized views of aggregate data derived from a fact table in order to minimize the query response time. One of the most important decisions in designing the data warehouse is the selection of materialized views. This paper presents an algorithm which provides appropriate views to be materialized while the goal is to minimize the query response time and maintenance cost. We use a data cube lattice, frequency of queries and updates on views, and view size to select views to be materialized using greedy algorithms.In spite of the simplicity, our algorithm selects views which give us better performance than views that selected by existing algorithms.
Abstract. XML indices are essential for efficiently processing XML queries which typically have predicates on both structures and values. Since the number of all possible structural and value indices is large even for a small XML document with a simple structure, XML DBMSs must carefully choose which indices to build. In this paper, we propose a tool, called XIST, that can be used by an XML DBMS as an index selection tool. XIST exploits XML structural information, data statistics, and query workload to select the most beneficial indices. XIST employs a technique that organizes paths that are evaluated to the same result into equivalence classes and uses this concept to reduce the number of paths considered as candidates for indexing. XIST selects a set of candidate paths and evaluates the benefit of an index on each candidate path on the basis of performance gains for non-update queries and penalty for update queries. XIST also recognizes that an index on a path can influence the benefit of an index on another path and accounts for such index interactions. We present an experimental evaluation of XIST and current XML index selection techniques, and show that the indices selected by XIST result in greater overall improvements in query response times and often require less disk space.
Abstract. As the popularity of eXtensible Markup Language (XML) continues to increase at an astonishing pace, data management systems for storing and querying large repositories of XML data are urgently needed. In this paper, we investigate an Object-Relational DBMS (ORDBMS) for storing and querying XML data. We present an algorithm, called XORator, for mapping XML documents to tables in an ORDBMS. An important part of this mapping is assigning a fragment of an XML document to a new XML data type. We demonstrate that using the XORator algorithm, an ORDBMS is usually more efficient than a Relational DBMS (RDBMS). Based on an actual implementation in DB2 V.7.2, we compare the performance of the XORator algorithm with a well-known algorithm for mapping XML data to an RDBMS. Our experiments show that the XORator algorithm requires less storage space, has much faster loading times, and in most cases can evaluate queries faster. The primary reason for this performance improvement is that the XORator algorithm results in a database that is smaller in size, and queries that usually have fewer number of joins.
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