DescribeX: A Framework for Exploring and Querying XML Web Collections

This thesis introduces DescribeX, a powerful framework that is capable of describing arbitrarily complex XML summaries of web collections, providing support for more efficient evaluation of XPath workloads. DescribeX permits the declarative description of document structure using all axes and language constructs in XPath, and generalizes many of the XML indexing and summarization approaches in the literature. DescribeX supports the construction of heterogeneous summaries where different document elements sharing a common structure can be declaratively defined and refined by means of path regular expressions on axes, or axis path regular expression (AxPREs). DescribeX can significantly help in the understanding of both the structure of complex, heterogeneous XML collections and the behaviour of XPath queries evaluated on them. Experimental results demonstrate the scalability of DescribeX summary refinements and stabilizations (the key enablers for tailoring summaries) with multi-gigabyte web collections. A comparative study suggests that using a DescribeX summary created from a given workload can produce query evaluation times orders of magnitude better than using existing summaries. DescribeX's light-weight approach of combining summaries with a file-at-a-time XPath processor can be a very competitive alternative, in terms of performance, to conventional fully-fledged XML query engines that provide DB-like functionality such as security, transaction processing, and native storage.Comment: PhD thesis, University of Toronto, 2008, 163 page

Supporting queries spanning across phases of evolving artifacts using Steiner forests

The problem of managing evolving data has attracted considerable research attention. Researchers have focused on the modeling and querying of schema/instance-level structural changes, such as, ad-dition, deletion and modification of attributes. Databases with such a functionality are known as temporal databases. A limitation of the temporal databases is that they treat changes as independent events, while often the appearance (or elimination) of some structure in the database is the result of an evolution of some existing structure. We claim that maintaining the causal relationship between the two structures is of major importance since it allows additional reason-ing to be performed and answers to be generated for queries that previously had no answers. We present here a novel framework for exploiting the evolution relationships between the structures in the database. In particu-lar, our system combines different structures that are associated through evolution relationships into virtual structures to be used during query answering. The virtual structures define “possible” database instances, in a fashion similar to the possible worlds in the probabilistic databases. The framework includes a query answering mechanism that allows queries to be answered over these possible databases without materializing them. Evaluation of such queries raises many interesting technical challenges, since it requires the discovery of Steiner forests on the evolution graphs. On this prob-lem we have designed and implemented a new dynamic program-ming algorithm with exponential complexity in the size of the input query and polynomial complexity in terms of both the attribute and the evolution data sizes

ToXin, an indexing scheme for XML data

grantor: University of TorontoAbstract not supplied.M.Sc

DescribeX: A Framework for Exploring and Querying XML Web Collections

The nature of semistructured data in web collections is evolving. Even when XML web documents are valid with regard to a schema, the actual structure of such documents exhibits significant variations across collections for several reasons: an XML schema may be very lax (e.g., to accommodate the flexibility needed to represent collections of documents in RSS feeds), a schema may be large and different subsets used for different documents (e.g., this is common in industry standards like UBL), or open content models may allow arbitrary schemas to be mixed (e.g., RSS extensions like those used for podcasting). A schema alone may not provide sufficient information for many data management tasks that require knowledge of the actual structure of the collection. Web applications (such as processing RSS feeds or web service messages) rely on XPath-based data manipulation tools. Web developers need to use XPath queries effectively on increasingly larger web collections containing hundreds of thousands of XML documents. Even when tasks only need to deal with a single document at a time, developers benefit from understanding the behaviour of XPath expressions across multiple documents (e.g., what will a query return when run over the thousands of hourly feeds collected during the last few months?). Dealing with the (highly variable) structure of such web collections poses additional challenges. This thesis introduces DescribeX, a powerful framework that is capable of describing arbitrarily complex XML summaries of web collections, providing support for more efficient evaluation of XPath workloads. DescribeX permits the declarative description of document structure using all axes and language constructs in XPath, and generalizes many of the XML indexing and summarization approaches in the literature. DescribeX supports the construction of heterogenous summaries where different document elements sharing a common structure can be declaratively defined and refined by means of path regular expressions on axes, or axis path regular expression (AxPREs). DescribeX can significantly help in the understanding of both the structure of complex, heterogeneous XML collections and the behaviour of XPath queries evaluated on them. Experimental results demonstrate the scalability of DescribeX summary refinements and stabilizations (the key enablers for tailoring summaries) with multi-gigabyte web collections. A comparative study suggests that using a DescribeX summary created from a given workload can produce query evaluation times orders of magnitude better than using existing summaries. DescribeX’s light-weight approach of combining summaries with a file-at-a-time XPath processor can be a very competitive alternative, in terms of performance, to conventional fully-fledged XML query engines that provide DB-like functionality such as security, transaction processing, and native storage.Ph

Temporal XML: Modeling, Indexing, and Query Processing

In this paper we address the problem of modeling and implementing temporal data in XML. We propose a data model for tracking historical information in an XML document and for recovering the state of the document as of any given time. We study the temporal constraints imposed by the data model, and present algorithms for validating a temporal XML document against these constraints, along with methods for fixing inconsistent documents. In addition, we discuss different ways of mapping the abstract representation into a temporal XML document, and introduce TXPath, a temporal XML query language that extends XPath 2.0. In the second part of the paper, we present our approach for summarizing and indexing temporal XML documents. In particular we show that by indexing continuous paths, i.e., paths that are valid continuously during a certain interval in a temporal XML graph, we can dramatically increase query performance. To achieve this, we introduce a new class of summaries, denoted TSummary, that adds the time dimension to the wellknown path summarization schemes. Within this framework, we present two new summaries: LCP and Interval summaries. The indexing scheme, denoted TempIndex, integrates these summaries with additional data structures. We give a query processing strategy based on TempIndex and a type of ancestor-descendant encoding, denoted temporal interval encoding. We present a persistent implementation of TempIndex, and a comparison against a system based on a non-temporal path index

The Picasso Project

Building on Statistics Canada’s metadata-driven architectural principle and metadata strategy themes: drive, make available, structure and manage, Picasso is an enterprise solution for statistical data and metadata management. Automated business rules will ensure metadata is gathered uniformly, adhering to common architecture, governance and policy instruments. Picasso, a three-year project launched in 2015, replaces local solutions with a hub for managing metadata for all surveys, administrative files and record linkage projects; a data service centre function for all ‘fit for use’ data files; and enterprise search and discovery using metadata to facilitate reuse of information. New tools and components include a metadata designer with an entity lifecycle management and registration process. The solution architecture is based on a hybrid relational/semantic graph (RDF) core registry and repository with a data model driven by standard vocabularies, e.g. SKOS/XKOS, PROV-O, and reference models, e.g. GSIM, DDI 4 and SDMX. Picasso component and external systems interact with the RDF core via a Data Access Layer and Entity Services to access metadata entities via Common Information Exchange Models. Standard vocabularies and models ensure efficient information exchange internally and to external users through the Agency’s website and Research Data Centres