We motivate, formalize and investigate the notions of data quality assessment and data quality query answering as context dependent activities. Contexts for the assessment and usage of a data source at hand are modeled as collections of external databases, that can be materialized or virtual, and mappings within the collections and with the data source at hand. In this way, the context becomes "the complement" of the data source wrt a data integration system. The proposed model allows for natural extensions, like considering data quality predicates, and even more expressive ontologies for data quality assessment.

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, and one based on DOM. Finally, we sketch a language for updates, and show that the cost of updating the index is compatible with real-world requirements.

Different models have been proposed recently for representing temporal data, tracking historical information, and recovering the state of the document as of any given time, in XML documents. We address the problem of 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. We describe in detail the indexing scheme, denoted TempIndex, and compare its performance against both a system based on a nontemporal path index, and one based on DOM.

Abstract-This paper introduces AxPRE summaries, a formalism that allows exploring the (semi-)structure of large XML collections. AxPRE summaries are implemented in a tool, DescribeX, that supports visualizing XML collections via summaries that can be interactively refined using a powerful and descriptive axis path regular expression language. Experimental results on gigabyte collections have shown that this flexibility does not come at the expense of efficiency.

Abstract. The world is changing, and so must the data that describes its history. Not surprisingly, considerable research effort has been spent in Databases along this direction, covering topics such as temporal models and schema evolution. A topic that has not received much attention, however, is that of concept evolution. For example, Germany (instance-level concept) has evolved several times in the last century as it went through different governance structures, then split into two national entities that eventually joined again. Likewise, a caterpillar is transformed into a butterfly, while a mother becomes two (maternally-related) entities. As well, the concept of Whale (a class-level concept) changed over the past two centuries thanks to scientific discoveries that led to a better understanding of what the concept entails. In this work, we present a formal framework for modeling, querying and managing such evolution. In particular, we describe how to model the evolution of a concept, and how this modeling can be used to answer historical queries of the form "How has concept X evolved over period Y". Our proposal extends an RDF-like model with temporal features and evolution operators. Then we provide a query language that exploits these extensions and supports historical queries.