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Good testing coverage of novel database techniques, such as multidimensional histograms or changes in the execution engine, is a complex problem. In this work, we argue that this task requires generating query instances, not randomly, but based on a given set of constraints. Specifically, obtaining query instances that satisfy cardinality constraints on their subexpressions is an important challenge. We show that this problem is inherently hard, and develop heuristics that effectively find approximate solutions. Index Terms--Query generation, database testing, cardinality constraints.

Much of business XML data has accompanying XSD specifications. In many scenarios, 'shredding' such XML data into a relational storage is a popular paradigm. Optimizing evaluation of XPath queries over such XML data requires paying careful attention to both the logical and physical designs of the relational database where XML data is shredded. None of the existing solutions has taken into account physical design of the generated relational database. In this paper, we study the interplay of logical and physical design and conclude that 1) solving them independently leads to suboptimal performance and 2) there is substantial overlap between logical and physical designs: some well-known logical design transformations generate the same mappings as physical design. Furthermore, existing search algorithms are inefficient to search the extremely large space of logical and physical design combinations. We propose a search algorithm that carefully avoids searching duplicated mappings and utilizes the workload information to further prune the search space. Experimental results confirm the effectiveness of our approach. Index Terms--XML, physical design, relational databases.

We study the index selection problem: Given a workload consisting of SQL statements on a database, and a user-specified storage constraint, recommend a set of indexes that have the maximum benefit for the given workload. We present a formal statement for this problem and show that it is computationally 'hard' to solve or even approximate it. We develop a new algorithm for the problem which is based on treating the problem as a knapsack problem. The novelty of our approach lies in an LP (linear programming) based method that assigns benefits to individual indexes. For a slightly modified algorithm, that does more work, we prove that we can give instance specific guarantees about the quality of our solution. We conduct an extensive experimental evaluation of this new heuristic and compare it with previous solutions. Our results demonstrate that our solution is more scalable while achieving comparable quality. Index Terms--Index selection, approximation, hardness result, NP-hardness, knapsack, linear programming, scalability.

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A query on these attributes will typically request not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, which indicates how well the object matches the selection condition (ranking). Furthermore, unlike in the relational model, users may just want the k top-ranked objects for their selection queries for a relatively small k. In addition to the differences in the query model, another peculiarity of multimedia repositories is that they may allow access to the attributes of each object only through indexes. In this paper, we investigate how to optimize the processing of top-k selection queries over multimedia repositories. The access characteristics of the repositories and the above query model lead to novel issues in query optimization. In particular, the choice of the indexes used to search the repository strongly influences the cost of processing the filtering condition. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we present an efficient algorithm that solves the problem optimally with respect to our cost model and execution space when the predicates in the query are independent. We also show, that the problem of optimizing top-k selection queries can be viewed, in many cases, as that of evaluating more traditional selection conditions. Thus, both problems can be viewed together as an extended filtering problem to which techniques of query processing and optimization may be adapted. Index Terms--Top-k query processing, multimedia databases, information search, information retrieval.

In multiuser multimedia information systems (e.g., movie-on-demand, digital-editing), scheduling the retrievals of continuous media objects becomes a challenging task. This is because of both intra and inter lobject time dependencies. Intraobject time dependency refers to the real-time display requirement of a continuous media object. Interobject time dependency is the temporal relationships defined among multiple continuous media objects. In order to compose tailored multimedia presentations, a user might define complex time dependencies among multiple continuous media objects with various lengths and display bandwidths. Scheduling the retrieval tasks corresponding to the components of such a presentation in order to respect both inter and intra task time dependencies is the focus of this study. To tackle this task scheduling problem (CRS), we start with a simpler scheduling problem (ARS) where there is no inter task time dependency (e.g., movie-on-demand). Next, we investigate an augmented version of ARS (termed ARS+) where requests reserve displays in advance (e.g., reservation-based movie-on-demand). Finally, we extend our techniques proposed for ARS and ARS+ to address the CRS problem. We also provide formal definition of these scheduling problems and proof of their NP-hardness. Index Terms--Composite objects, continuous media retrieval, multimedia, real-time scheduling, storage servers, temporal relationships.

Statistics play a key role in influencing the quality of plans chosen by a database query optimizer. In this paper, we identify the statistics that are essential for an optimizer. We introduce novel techniques that help significantly reduce the set of statistics that need to be created without sacrificing the quality of query plans generated. We discuss how these techniques can be leveraged to automate statistics management in databases. We have implemented and experimentally evaluated our approach on Microsoft SQL Server 7.0. Index Terms--Statistics, workload, query optimizer, query plan.