Querying XML documents typically involves both tree-based navigation and pattern matching similar to that used in structured information retrieval domains. In this paper, we show that for good performance, a native XML query processing system should support query plans that mix these two processing paradigms. We describe our prototype native XML system, and report on experiments demonstrating that even for simple queries, there are a number of options for how to combine tree-based navigation and structural joins based on information retrieval-style inverted lists, and that these options can have widely varying performance. We present ways of transparently using both techniques in a single system, and provide a cost model for identifying efficient combinations of the techniques. Our preliminary experimental results prove the viability of our approach.
There has been an information explosion in fields of science such as high energy physics, astronomy, environmental sciences and biology. There is a critical need for automated systems to manage scientific applications and data. Database technology is well-suited to handle several aspects of workflow management. Contemporary workflow systems are built from multiple, separately developed components and do not exploit the full power of DBMSs in handling data of large magnitudes. We advocate a holistic view of a WFMS that includes not only workflow modeling but planning, scheduling, data management and cluster management. Thus, it is worthwhile to explore the ways in which databases can be augmented to manage workflows in addition to data. We present a language for modeling workflows that is tightly integrated with SQL. Each scientific program in a workflow is associated with an active table or view. The definition of data products is in relational format, and invocation of programs and querying is done in SQL. The tight coupling between workflow management and data-manipulation is an advantage for data-intensive scientific programs.
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The encrypted execution of database queries promises powerful security protections, however users are currently unlikely to benefit without significant expertise. In this demonstration, we illustrate a simple workflow enabling users to design secure executions of their queries. The DataStorm system demonstrated simplifies both the design and execution of encrypted execution plans, and represents progress toward the challenge of developing a general planner for encrypted query execution.
As relational database management systems are applied to non-traditional domains such as scientific data management, there is an increasing need to support queries with semantics that differ from those appropriate for traditional RDBMS applications. Two interesting ideas currently being explored in the DBMS community are ranking query results (e.g., top-k computations) and, more recently, "match joins." In this paper we combine these two ideas and study weighted match joins, in which (a) like match joins, each tuple joins with at most one matching tuple, and (b) like top-k joins, the system attempts to provide a set of answer tuples that maximizes a weight function. We explore exact and approximate strategies for evaluating weighted match joins. Using a prototype implementation in PostgreSQL, we explore the performance characteristics of these strategies. Our results suggest that the DBMS optimization-based approach of providing several implementations of an operator and then choosing an appropriate one at run time can be useful in computing weighted match joins.
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