XML Data Sources Research Articles

Considering Structural and Vocabulary Heterogeneity in XML Query: FPTPQ and Holistic Evaluation.

The integration of XML data sources which have different schemas/DTD can originate structural and vocabular heterogeneity. In this context, it is difficult to write satisfiable queries. As a solution, many Information Systems focus on building approximate evaluation techniques for exact queries. As a project, we build flexible and preference XML query languages and associated evaluation algorithms. In this paper, we propose the Flexible Preference Tree Pattern Query (FPTPQ), a new TPQ that allows multiple items/names (resp. paths) for the same node, in order to integrate (resp. to locate) all the different instances of the database nodes. The FPTPQ enable to have preference nodes and ordering operators among label items and paths. We also provide a holistic algorithm that evaluates the FPTPQ and capitalises the preferences to determine the best available solutions. Illustrations and experimentations are realized to show the effectiveness of our solutions.

Auto{API} – A Web-Based Tool for Specification of an API Endpoint to Return JSON Data From an XML Source

auto{API} is a web-based tool for the specification and management of API endpoints to online XML data sources. The APIs generated return the data in JSON format. The tool allows users to specify the field names to be used in the JSON data and to control which elements of the original data set are returned. Options are provided for the returned data to skip null values and to flatten the structure that is retrieved. auto{API} is written in Python/Flask and is available under the MIT licence. The software is available from GitHub (https://github.com/aamoore/autoAPI) and can be seen deployed at https://autoapi-app.herokuapp.com/?http://apis.opendatani.gov.uk/translink/3042AA.xml.

An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM

Data classification for distributed and heterogeneous XML data sources is always an open challenge. A considerable number of algorithms for classification of XML documents have been proposed in the literature. Yet, the existing approaches fall short in ability to classify the fuzzy XML documents. In this paper, we provide a KPCA-KELM classification framework for the fuzzy XML documents based on Kernel Extreme Learning Machine (KELM). Firstly, we propose a novel fuzzy XML document tree model to represent fuzzy XML documents. Secondly, we employ an effective vector space model to represent the semantic structure of fuzzy XML documents based on the proposed fuzzy XML document tree model. Thirdly, we classify the fuzzy XML document using KELM after feature extraction using Kernel Principal Component Analysis (KPCA). The corresponding experimental results demonstrate that our proposed KPCA-KELM approach shortens the training time while maintaining the same level of accuracy as the state-of-the-art baseline models.

Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels

This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. The second part of the paper shows, through a comprehensive set of the spatial extension to SPARQL (stSPARQL) queries, how the endpoint can be exploited.

AuMixDw: Towards an automated hybrid approach for building XML data warehouses

In this paper, we present a mixed approach for building XML data warehouses from both XML data sources and user requirements. Our proposed approach aims at obtaining a unique multidimensional schema of theXML data warehouse. The approach follows three steps. During the first step, an intermediate SBVR model extended with template rules is used to accommodate a data warehousing system and to facilitate the automatic identification of facts and dimensions from the user requirements. After modelling XML data sources in UML, the second step corresponds to identifying candidate DW schemata from such data sources. The third step compares these candidate schemata with the reference model obtained from the user requirements. In this step, we propose to adapt similarity metric-extended Boolean models (BIR) and to use them in order to measure, rank and select the most appropriate data warehouse schema. Such a schema should best describe the data sources and exhaustively cover all the needed user requirements. To demonstrate our approach, we present a case study of the bibliographic database dblp.

Semantic Similarity Measure of Fuzzy XML DTDs with Extreme Learning Machine.

Data integration for distributed and heterogeneous XML data sources is still an open challenging, and XML DTD matching is crucial task in this process. A considerable amount of algorithms for comparing XML DTDs have been proposed in the literature. Yet, the existing approaches fall short in ability to identify semantic similarities in fuzzy XML DTDs. To fill this gap, in this paper, we provide an approach to cope with semantic similarities in the fuzzy XML DTDs. The present paper makes two major contributions. First, we propose a novel fuzzy XML DTD tree model to represent fuzzy XML DTD. Second, based on the proposed tree model, we present an effective algorithm based on Extreme Learning Machine (ELM) to synthesize the semantic similarities between fuzzy XML DTDs. The corresponding computational experimental results demonstrate that our proposed approach has a prominent high performance.

Tree pattern matching in heterogeneous fuzzy XML databases

Dealing with heterogeneous data underlying fuzzy XML databases is challenging for any task of document management and knowledge discovery, since the structural heterogeneity and uncertainty of the large number of XML data sources make it difficult to effectively answer the structured query, especially the tree-pattern query. To address this issue, we propose a novel framework for managing fuzzy XML queries in a heterogeneous environment in this paper. In particular, we devise a holistic algorithm for matching tree-patterns over heterogeneous fuzzy XML data. Our approach adopts a compact stack technique and generates the matches by one scan on the relevant data associated with the tree-pattern, which eliminates re-scanning unnecessary portions of XML documents and redundant intermediate results. Finally, a comprehensive experimental evaluation conducted on real and synthetic data sets is carried out to show the significance of our approach as a solution for querying heterogeneous data in fuzzy XML documents.

Towards a hybrid relational and XML benchmark for loosely-coupled distributed data sources

There are known benchmarks for the performance evaluation of relational and XML databases. However, there is an increasing demand for database applications that require access to heterogeneous loosely-coupled distributed data sources. This paper presents a hybrid benchmark based on TPC-H where the data sources are heterogeneous. Specifically, the paper describes the design of the relational and XML data sources as well as the query redesign in the LINQ query language, which supports queries over heterogeneous data sources. The results of a performance evaluation of the hybrid benchmark over various database products is included for untyped and typed XML with and without clearing the database cache.

Transforming XML documents to OWL ontologies: A survey

The aims of XML data conversion to ontologies are the indexing, integration and enrichment of existing ontologies with knowledge acquired from these sources. The contribution of this paper consists in providing a classification of the approaches used for the conversion of XML documents into OWL ontologies. This classification underlines the usage profile of each conversion method, providing a clear description of the advantages and drawbacks belonging to each method. Hence, this paper focuses on two main processes, which are ontology enrichment and ontology population using XML data. Ontology enrichment is related to the schema of the ontology (TBox), and ontology population is related to an individual (Abox). In addition, the ontologies described in these methods are based on formal languages of the Semantic Web such as OWL (Ontology Web Language) or RDF (Resource Description Framework). These languages are formal because the semantics are formally defined and take advantage of the Description Logics. In contrast, XML data sources are without formal semantics. The XML language is used to store, export and share data between processes able to process the specific data structure. However, even if the semantics is not explicitly expressed, data structure contains the universe of discourse by using a qualified vocabulary regarding a consensual agreement. In order to formalize this semantics, the OWL language provides rich logical constraints. Therefore, these logical constraints are evolved in the transformation of XML documents into OWL documents, allowing the enrichment and the population of the target ontology. To design such a transformation, the current research field establishes connections between OWL constructs (classes, predicates, simple or complex data types, etc.) and XML constructs (elements, attributes, element lists, etc.). Two different approaches for the transformation process are exposed. The instance approaches are based on XML documents without any schema associated. The validation approaches are based on the XML schema and document validated by the associated schema. The second approaches benefit from the schema definition to provide automated transformations with logic constraints. Both approaches are discussed in the text.

Semantic RDF Based Integration Framework for Heterogeneous XML Data Sources

A significant amount of data on the Web is in the XML format or may easily be converted to XML or to its variations. XML is still the most appropriate language for data interchange and serialization. In this paper, a new framework which can integrate any heterogeneous XML data sources is presented. Each data source is translated into semantically meaningful regular expressions without changing original data source. Proposed framework has two major phases for data preparation. In the first phase, each data source is processed to obtain regular expressions which accommodate with the design choices that made in target by utilizing known global semantic vocabulary as an input. The second phase combines these regular expressions to get a global schema by preserving the original source data. A regular expression generator tool which produces regular expressions by regarding vocabulary and an integrator tool box which integrates and processes regular expressions, are also introduced.

Detecting common subexpressions for multiple query optimization over loosely-coupled heterogeneous data sources

The research presented in this paper supports the identification of common subexpressions as candidates for potential materialized views that form the basis of multiple query optimization in a loosely-coupled distributed system where query expressions access heterogeneous data sources, including relations and data-centric XML. This paper introduces a unifying mixed multigraph formalism to represent SQL, XQuery, and LINQ queries in a common query graph model and a heuristics-based algorithm to detect common subexpressions. The identified common subexpressions represent an opportunity for defining a materialized view to avoid repeating computation. The common subexpressions may access only relations, only XML, or a combination of relations and XML. The mixed multigraph model and the heuristic rules presented in this paper have distinguished advantages over the existing approaches that consider only relational or XML data sources individually. The mixed multigraph model can present SQL, XQuery, and LINQ queries in a single graph model and the heuristic rules are designed to consider the identical and subsumed conditions at the same time. A prototype implementation of the algorithm illustrates the applicability of the approach using various examples from the research literature as well as scenarios over a Criminal Justice enterprise that include common subexpressions across relational and XML data sources.

An XSketch-based spelling suggestion approach for XML keyword search

Purpose – The purpose of this paper is to study the spelling suggestion (SS) problem for extensible markup language (XML) keyword search, which provides users with alternative queries that may better express users search intention. Design/methodology/approach – To return the suggested queries more efficiently, the authors evaluate the quality of the query by estimating the selectivity and quality of each query pattern. The selectivity estimation is based on the XSketch synopsis, which summarizes the structure and value distribution of the original XML data source. The authors propose an approach to generating the top-K query candidates. Findings – Experiments with real datasets verify the effectiveness and efficiency of the authors' approach. Originality/value – The authors proposed a SS approach based on the XSketch summary.

Approximate joins for XML at label level

In heterogeneous XML data sources, the same real-world object may not be represented exactly the same. Thus approximate join techniques are often applied, in which XML documents are joined based on similarity. In previous XML join methods, researchers consider each XML label as a unit and entirely disregard the similarity between different labels. However, real-world data sets are often ‘dirty’. The labels should be also approximately matched in the join.To improve the join quality, our approach considers both XML structure and node label similarity by applying two tailored similarity measures. Min-hash, a probabilistic hash function, is employed to achieve scalability. Extensive experiments confirm that the join quality is fundamentally improved when the label similarity is considered and our join efficiency is even higher than some of the most efficient methods.

Data preparation for KDD through automatic reasoning based on description logic

Without data preparation, data mining algorithms cannot operate on data within the knowledge discovery in databases (KDD) process. In fact, the success of later KDD phases largely depends on the data preparation stage. The use of mechanisms for automatically preparing data saves a lot of time and resources within the KDD process. These resources will then be available for use at later, less automatable stages, for example, during results interpretation.We have proposed a general-purpose mechanism applicable to multiple domains in order to improve the data preparation phase in the KDD process. This mechanism processes and automatically converts input data to a suitable format for the application of different data preparation techniques based on a known syntax. It is based on the use of description logic. Taking a generic UML2 data model as a reference, this mechanism is able to check whether any XML data source whatsoever can be transformed and modelled as a subsumption or instance of the above UML2 model. Thus it automatically identifies a consistent, non-ambiguous and finite set of XLST transformations which are used to prepare the data for the application of data mining techniques, obviating the need to expend resources on the preliminary preparation and formatting stage.The proposed mechanism was applied on structurally complex data from four different domains. In order to test the validity of the proposal, we have applied data mining techniques to extract knowledge from the prepared data. The sound results of applying our proposal to several different domains confirm that it is applicable to any XML data source, as well as being correct, computationally efficient and saving time during the data preparation phase.

Semantically Interoperable XML Data.

XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups.

Representing and Reasoning About XML with Ontologies

The eXtensible Markup Language (XML) has reached a wide acceptance as the relevant standardization for representing and exchanging data on the Web. Unfortunately, XML covers the syntactic level but lacks semantics, and thus cannot be directly used for the Semantic Web. Currently, finding a way to utilize XML data for the Semantic Web is challenging research. As we have known that ontology can formally represent shared domain knowledge and enable semantics interoperability. Therefore, in this paper, we investigate how to represent and reason about XML with ontologies. Firstly, we give formalized representations of XML data sources, including Document Type Definitions (DTDs), XML Schemas, and XML documents. On this basis, we propose formal approaches for transforming the XML data sources into ontologies, and we also discuss the correctness of the transformations and provide several transformation examples. Furthermore, following the proposed approaches, we implement a prototype tool that can automatically transform XML into ontologies. Finally, we apply the transformed ontologies for reasoning about XML, so that some reasoning problems of XML may be checked by the existing ontology reasoners.

Structured content-aware discovery for improving XML data consistency

With the explosive growth of heterogeneous XML sources, data inconsistency has become a serious problem that leads to ineffective business operations and poor decision-making. To address such inconsistency, XML functional dependencies (XFDs) have been proposed to constrain the data integrity of a source. Unfortunately, existing approaches to XFDs have insufficiently addressed data inconsistency arising from both semantic and structural inconsistencies inherent in heterogeneous XML data sources. This paper proposes a novel approach, called SCAD, to discover anomalies from a given source, which is essential to address prevalent inconsistencies in XML data. Our contribution is twofold. First, we introduce a new type of path and value-based data constraint, called XML Conditional Structural Dependency (XCSD), whereby (i) the paths in XCSD approximately represent groups of similar paths in sources to express constraints on objects with diverse structures; while (ii) the values bound to particular elements express constraints with conditional semantics. XCSD can capture data inconsistency disregarded by XFDs.Second, our proposed SCAD is used to discover XCSDs from a given source. Our approach exploits the semantics of data structures to detect similar paths from the sources, from which a data summary is constructed as an input for the discovery process. This aims to avoid returning redundant data rules due to structural inconsistencies. During the discovery process, SCAD employs semantics hidden in the data values to discover XCSDs. To evaluate our proposed approach, experiments and case studies were conducted on synthetic datasets which contain structural diversity causing XML data inconsistency. The experimental results show that SCAD can discover more dependencies and the dependencies found convey more meaningful semantics than those of the existing XFDs.

Special issue on Semantic Information Management guest editorial

Barely more than 10 years after the birth of the World Wide Web, the Global Information Infrastructure is daily reality. In spite of the many applications in all domains of our societies: e-business, e-commerce, e-learning, e-science, and e-government, for instance, and in spite of the tremendous advances by engineers and scientists, the seamless integration of information and services remains a major challenge. The current shared vision for the future is one of semantically rich information and service oriented architecture for global information systems. This vision is at the convergence of progress in technologies such as XML, Web services, RDF, OWL, of multimedia, multimodal, and multilingual information retrieval, and of distributed, mobile and ubiquitous computing. Semantic Information Management (SIM) can be identified as the discipline that studies the integration and management of multi-modal information from distributed, heterogeneous, and autonomous sources based on their meaning. SIM is deemed necessary since there is an increasing tendency of inter-business and inter-applications sharing of data. The data can be written and stored using different standard and format. Hence, we need a platform to manage the data so that it can be universally understood. SIM as a research has variety research challenges. The first challenge is the identification of suitable meta-model that can ensure the quality of the integrated data. The next challenge is the adoption of ever-increasing number of standards used by enterprises. To comply with standards, enterprises have to represent their data uniquely and thus, the challenge to integrate them with data from other enterprises increases. XML has become a standard data format widely used in many organizations, thus leading to a growing need for exchanging and integrating multiple XML data sources across different application systems. XML has been regarded as a de-facto standard for data exchanges in enterprises. Integrating these sources requires an integration of their schemas. The main challenge is schema integration where the semantic is maintained. Aligning with the previous challenge, many enterprises these days have to comply with various government and professional regulations in managing their data. It makes the integration of data from multi-discipline enterprises and cross-regulated regions even more difficult. Another challenge is the ability to trace back the integrated information to its original source and format. The issue of forward and backward compatibility is far more complex than in traditional data integration research domain. The issue becomes more complex in a social network setting. Social networks consist mainly of groups of inter-connected people. This has played an important role in changing the way people interact with each other. Social networks analysis focuses on studying patterns of communication and exchange of information between people, which may influence not only on the individuals who adopt them, but also on the societies and organizations that enclose them. Since social network is not only heterogeneous, SIM in social networks imposes even a greater challenge, since each social group may have their own information semantic, and therefore, semantic in a global information exchange is a complex problem. The availability of cloud computing makes SIM even more challenging. Cloud computing has been increasingly regarded as a revolution in the computing practices through the concept of utility computing, whereby computing services are provided as utilities. There are various number of services provided by the cloud, from computing power, storage, to various software applications. SIM in the cloud will provide Information as a Service, a totally novel concept. The main challenge will be to manage such information so that the G. Kotsis (*) : I. Khalil Department of Telecooperation, Johannes Kepler University Linz, Altenberger Strasse 69, 4040, Linz, Austria e-mail: gabriele.kotsis@jku.at

XBridge-Mobile: efficient XML keyword search on mobile web data

Mobile computing over intelligent mobile is affecting human's habits of obtaining information over Internet, especially keyword search. Most of previous keyword search works are mainly focused on traditional web data sources, in which the performance can be improved by adding more computing power and/or building more offline-computed index. However, it is very challenging to apply the traditional keyword search methods to mobile web-based keyword search because mobile computing has many different features, e.g., frequent disconnections, variety of bandwidths, limited power of mobile devices, limited data size to be downloaded, etc.. To address this challenge, in this paper we design an adaptive mobile-based XML keyword search approach, called XBridge-Mobile, that can derive the semantics of a keyword query and generate a set of effective structured patterns by analyzing the given keyword query and the schemas of XML data sources. Each structured pattern represents one of user's possible search intentions. The patterns will be firstly sent to the mobile client from web server. And then, the mobile client can select some interested patterns to load the results. By doing this, we can reduce the communication cost a lot between web server and mobile client because only the derived patterns and a few results need to be transferred, not all the keyword search results, by which we can save lots of expenses when the downloaded data is priced. In addition, we can economically maintain the frequent structured pattern queries in the mobile device, which can further reduce the expense of downloading data. At last, we analyze and propose a ranking function to measure the quality of keyword search results, design a set of algorithms to optimize mobile keyword search based on the maintained structured patterns, and present the experimental study of XBridge-Mobile with real XML datasets.

An overview of XML warehouse design approaches and techniques

XML – the Extensible Markup Language has recently emerged as a standard for information representation and exchange on the internet, because of its portability, versatility and extensibility. As the large increase in the availability of such data, there is a need of an appropriate storage and an integration solution for future analysis and decision-making. Data warehouses systems provide a suitable means to organise and manage XML data for decision-making. However, XML data warehouse has some special requirements such as mixed content and irregular structure of XML data sources, which make existing traditional data warehouse approaches and techniques unsuitable. In this paper, we give an overview of current state of the art concerning how XML technologies can be combined with data warehousing systems, by the presentation of the most relevant XML warehousing approaches proposed in the literature.