Keyword search in XML repositories is a powerful tool for interactive data exploration. Much work has recently been done on making XML search aware of relationship information embedded in XML document structure, but without a clear winner in all data and query scenarios. Furthermore, due to its imprecise nature, search results cannot easily be analyzed and summarized to gain more insights into the data. We address these shortcomings with SEDA: a system for Search, Exploration, Discovery, and Analysis of XML Data. SEDA is based on a paradigm of search and user interaction to help users start with simple keyword-style querying and perform rich analysis of XML data by leveraging both the content and structure of the data. SEDA is an interactive system that allows the user to refine her query iteratively to explore the XML data and discover interesting relationships. SEDA first employs a top-k algorithm to compute the most relevant top-k answers fast, and returns tuples of nodes ranked by relevance. SEDA provides several novel data structures and techniques for efficient top-k computation over graph-structured XML data. SEDA also computes all the contexts in which the query terms are found and all the connection paths that connect the query terms in the XML data. These two summaries enable the user to refine her query by disambiguating the contexts and connections relevant to her query. With the user feedback, the system has enough information to compute all query results, not just the top-k. From the complete results, SEDA automatically deduces a star schema, which is then instantiated with the query results and augmented with additional values required for a well-defined data cube. The tables computed at this step are input into an OLAP engine for further analysis.

Traditional query processing techniques based on static query optimization are ineffective in applications where statistics about the data are unavailable at the start of query execution or where the data characteristics are skewed and change dynamically. Several adaptive query processing techniques have been proposed in recent years to overcome the limitations of static query optimizers through either explicit re-optimization of plans during execution or by using a row-routing based approach. In this paper, we present a novel method for processing pipelined join plans that dynamically arranges the join order of both inner and outer-most tables at run-time. We extend the Eddies concept of "moments of symmetry" to reorder indexed nested-loop joins, the join method used by all commercial DBMSs for building pipelined query plans for applications for which low latencies are crucial. Unlike row-routing techniques, our approach achieves adaptability by changing the pipeline itself which avoids the bookkeeping and routing decision associated with each row. Operator selectivities monitored during query execution are used to change the execution plan at strategic points, and the change of execution plans utilizes a novel and efficient technique for avoiding duplicates in the query results. Our prototype implementation in a commercial DBMS shows a query execution speedup of up to 8 times.

XQuery is a query language under development by the W3C XML Query Working Group. The language contains constructs for navigating, searching, and restructuring XML data. With XML gaining importance as the standard for representing business data, XQuery must support the types of queries that are common in business analytics. One such class of queries is OLAP-style aggregation queries. Although these queries are expressible in XQuery Version 1, the lack of explicit grouping constructs makes the construction of these queries non-intuitive and places a burden on the XQuery engine to recognize and optimize the implicit grouping constructs. Furthermore, although the flexibility of the XML data model provides an opportunity for advanced forms of grouping that are not easily represented in relational systems, these queries are difficult to express using the current XQuery syntax. In this paper, we provide a proposal for extending the XQuery FLWOR expression with explicit syntax for grouping and for numbering of results. We show that these new XQuery constructs not only simplify the construction and evaluation of queries requiring grouping and ranking but also enable complex analytic queries such as moving-window aggregation and rollups along dynamic hierarchies to be expressed without additional language extensions.

Aggregate query processing in large data warehouses is computationally intensive. Precomputation is an approach that can be used to speed up aggregate queries. However, in order to make precomputation a truly viable solution to the aggregate query processing problem, it is important to identify the best set of aggregates to precompute and to use these precomputed aggregates effectively. The Red Brick aggregate computation and management system (Red Brick Vista) provides a complete server integrated solution to these problems.

Materialized views and view maintenance are becoming increasingly important in practice. In order to satisfy different data currency and performance requirements, a number of view maintenance policies have been proposed. Immediate maintenance involves a potential refresh of the view after every update to the deriving tables. When staleness of views can be tolerated, a view may be refreshed periodically or (on-demand) when it is queried. The maintenance policies that are chosen for views have implications on the validity of the results of queries and affect the performance of queries and updates. In this paper, we investigate a number of issues related to supporting multiple views with different maintenance policies.

Materialized views and view maintenance are important for data warehouses, retailing, banking, and billing applications. We consider two related view maintenance problems: 1) how to maintain views after the base tables have already been modified, and 2) how to minimize the time for which the view is inaccessible during maintenance.Typically, a view is maintained immediately, as a part of the transaction that updates the base tables. Immediate maintenance imposes a significant overhead on update transactions that cannot be tolerated in many applications. In contrast, deferred maintenance allows a view to become inconsistent with its definition. A refresh operation is used to reestablish consistency. We present new algorithms to incrementally refresh a view during deferred maintenance. Our algorithms avoid a state bug that has artificially limited techniques previously used for deferred maintenance.Incremental deferred view maintenance requires auxiliary tables that contain information recorded since the last view refresh. We present three scenarios for the use of auxiliary tables and show how these impact per-transaction overhead and view refresh time. Each scenario is described by an invariant that is required to hold in all database states. We then show that, with the proper choice of auxiliary tables, it is possible to lower both per-transaction overhead and view refresh time.

We present a language for querying list-based complex objects. The language is shown to express precisely the polynomial-time generic list-object functions. The iteration mechanism of the language is based on a new approach wherein, in addition to the list over which the iteration is performed, a second list is used to control the number of iteration steps. During the iteration, the intermediate results can be moved to the output list as well as reinserted into the list being iterated over. A simple syntactic constraint allows the growth rate of the intermediate results to be tightly controlled which, in turn, restricts the expressiveness of the language to PTIME.

The nested relational model provides a better way to represent complex objects than the (flat) relational model, by allowing relations to have relation-valued attributes. A recursive algebra for nested relations that allows tuples at all levels of nesting in a nested relation to be accessed and modified without any special navigational operators and without having to flatten the nested relation has been developed. In this algebra, the operators of the nested relational algebra are extended with recursive definitions so that they can be applied not only to relations but also to subrelations of a relation. In this paper, we show that queries are more efficient and succinct when expressed in the recursive algebra than in languages that require restructuring in order to access subrelations of relations. We also show that most of the query optimization techniques that have been developed for the relational algebra can be easily extended for the recursive algebra and that queries are more easily optimizable when expressed in the recursive algebra than when they are expressed in languages like the non-recursive algebra.