We present a native XML database management system, Sedna, which is implemented from scratch as a full-featured database management system for storing large amounts of XML data. We believe that the key contribution of this system is an improved schema-based clustering storage strategy efficient for both XML querying and updating, and powered by a novel memory management technique. We position our approach with respect to state-of-the-art methods.

In CIDR 2009, we presented a collection of requirements for SciDB, a DBMS that would meet the needs of scientific users. These included a nested-array data model, science-specific operations such as regrid, and support for uncertainty, lineage, and named versions. In this paper, we present an overview of SciDB's key features and outline a demonstration of the first version of SciDB on data and operations from one of our lighthouse users, the Large Synoptic Survey Telescope (LSST).

The measure of similarity between objects is a very useful tool in many areas of computer science, including information retrieval. SimRank is a simple and intuitive measure of this kind, based on graph-theoretic model. SimRank is typically computed iteratively, in the spirit of PageRank. However, existing work on SimRank lacks accuracy estimation of iterative computation and has discouraging time complexity. In this paper we present a technique to estimate the accuracy of computing SimRank iteratively. This technique provides a way to find out the number of iterations required to achieve a desired accuracy when computing SimRank. We also present optimization techniques that improve the computational complexity of the iterative algorithm from O ( n 4 ) to O ( n 3 ) in the worst case. We also introduce a threshold sieving heuristic and its accuracy estimation that further improves the efficiency of the method. As a practical illustration of our techniques we computed SimRank scores on a subset of English Wikipedia corpus, consisting of the complete set of articles and category links.

Mediation is an important application of XML. The MIX mediator uses Document Type Definitions (DTDs) to assist the user in query formulation and query processors in running queries more efficiently. We provide an algorithm for inferring the view DTD from the view definition and the source DTDs. We develop a metric of the quality of the inference algorithm's view DTD by formalizing the notions of soundness and tightness. Intuitively, tightness is similar to precision, i.e., it deteriorates when "many" objects described by the view DTD can never appear as content of the view. In addition we show that DTDs have some inherent deficiencies that prevent the development of tight DTDs. We propose "DTDs with specialization" as a way to resolve this problem.

The MIX mediator system, MIXm, is developed as part of the MIX Project at the San Diego Supercomputer Center, and the University of California, San Diego.1 MIXm uses XML as the common model for data exchange. Mediator views are expressed in XMAS (XML Matching And Structuring Language), a declarative XML query language. To facilitate user-friendly query formulation and for optimization purposes, MIXm employs XML DTDs as a structural description (in effect, a “schema”) of the exchanged data. The novel features of the system include: