XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Designing visual analytics methods for massive collections of movement data

Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may facilitate synergetic work of computer and human whereby the unique capabilities of each “partner” can be utilized. We suggest a systematic approach to defining what methods and techniques, and what ways of linking them, can appropriately support such a work. The main idea is that software tools prepare and visualize the data so that the human analyst can detect various types of patterns by looking at the visual displays. To facilitate the detection of patterns, we must understand what types of patterns may exist in the data (or, more exactly, in the underlying phenomenon). This study focuses on data describing movements of multiple discrete entities that change their positions in space while preserving their integrity and identity. We define the possible types of patterns in such movement data on the basis of an abstract model of the data as a mathematical function that maps entities and times onto spatial positions. Then, we look for data transformations, computations, and visualization techniques that can facilitate the detection of these types of patterns and are suitable for very large data sets – possibly too large for a computer's memory. Under such constraints, visualization is applied to data that have previously been aggregated and generalized by means of database operations and/or computational techniques

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

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

Evolution of Wikipedia's Category Structure

Wikipedia, as a social phenomenon of collaborative knowledge creating, has been studied extensively from various points of views. The category system of Wikipedia, introduced in 2004, has attracted relatively little attention. In this study, we focus on the documentation of knowledge, and the transformation of this documentation with time. We take Wikipedia as a proxy for knowledge in general and its category system as an aspect of the structure of this knowledge. We investigate the evolution of the category structure of the English Wikipedia from its birth in 2004 to 2008. We treat the category system as if it is a hierarchical Knowledge Organization System, capturing the changes in the distributions of the top categories. We investigate how the clustering of articles, defined by the category system, matches the direct link network between the articles and show how it changes over time. We find the Wikipedia category network mostly stable, but with occasional reorganization. We show that the clustering matches the link structure quite well, except short periods preceding the reorganizations.Comment: Preprint of an article submitted for consideration in Advances in Complex Systems (2012) http://www.worldscinet.com/acs/, 19 pages, 7 figure

Patterns of Learning Object Reuse in the Connexions Repository

Doctoral Dissertation abstract: Since the term learning object was first published, there has been either an explicit or implicit expectation of reuse. There has also been a lot of speculation about why learning objects are, or are not, reused. This study quantitatively examined the actual amount and type of learning object use, to include reuse, modification, and translation, within a single open educational resource repositoryâConnexions. The results indicate that about a quarter of used objects are subsequently reused, modified, or translated. While these results are repository specific, they represent an important first step in providing an empirical evaluation of the frequency and some reasons for reuse, as well as establishing metrics and terminology for future studies

Ergatis: a web interface and scalable software system for bioinformatics workflows

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users

Capture-based Automated Test Input Generation

Testing object-oriented software is critical because object-oriented languages have been commonly used in developing modern software systems. Many efficient test input generation techniques for object-oriented software have been proposed; however, state-of-the-art algorithms yield very low code coverage (e.g., less than 50%) on large-scale software. Therefore, one important and yet challenging problem is to generate desirable input objects for receivers and arguments that can achieve high code coverage (such as branch coverage) or help reveal bugs. Desirable objects help tests exercise the new parts of the code. However, generating desirable objects has been a significant challenge for automated test input generation tools, partly because the search space for such desirable objects is huge. To address this significant challenge, we propose a novel approach called Capture-based Automated Test Input Generation for Objected-Oriented Unit Testing (CAPTIG). The contributions of this proposed research are the following. First, CAPTIG enhances method-sequence generation techniques. Our approach intro-duces a set of new algorithms for guided input and method selection that increase code coverage. In addition, CAPTIG efficently reduces the amount of generated input. Second, CAPTIG captures objects dynamically from program execution during either system testing or real use. These captured inputs can support existing automated test input generation tools, such as a random testing tool called Randoop, to achieve higher code coverage. Third, CAPTIG statically analyzes the observed branches that had not been covered and attempts to exercise them by mutating existing inputs, based on the weakest precon-dition analysis. This technique also contributes to achieve higher code coverage. Fourth, CAPTIG can be used to reproduce software crashes, based on crash stack trace. This feature can considerably reduce cost for analyzing and removing causes of the crashes. In addition, each CAPTIG technique can be independently applied to leverage existing testing techniques. We anticipate our approach can achieve higher code coverage with a reduced duration of time with smaller amount of test input. To evaluate this new approach, we performed experiments with well-known large-scale open-source software and discovered our approach can help achieve higher code coverage with fewer amounts of time and test inputs