Latha S. Colby

Extending XQuery for analytics

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.

Red Brick Vista/sup TM/: aggregate computation and management

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.

On the effectiveness of flexible querying heuristics for XML data

The ability to perform effective XML data retrieval in the absence of schema knowledge has recently received considerable attention. The majority of relevant proposals employs heuristics that identify groups of meaningfully related nodes using information extracted from the input data. These heuristics are employed to effectively prune the search space of all possible node combinations and their popularity is evident by the large number of such heuristics and the systems that use them. However, a comprehensive study detailing the relative merits of these heuristics has not been performed thus far. One of the challenges in performing this study is the fact that these techniques have been proposed within different and not directly comparable contexts. In this paper, we attempt to fill this gap. In particular, we first abstract the common selection problem that is tackled by the relatedness heuristics and show how each heuristic addresses this problem. We then identify data categories where the assumptions made by each heuristic are valid and draw insights on their possible effectiveness. Our findings can help systems implementors understand the strengths and weaknesses of each heuristic and provide simple guidelines for the applicability of each one.

SEDA: a system for search, exploration, discovery, and analysis of XML Data

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.