XML-based information mediation with MIX

This paper describes Sucxent, a novel system for the storage and querying of XML data using a relational database system (RDBMS). The relational schema proposed under Sucxent does not require the knowledge of the DTDs of the XML documents being stored. In this approach we store only the leaf nodes and their text values together with their ancestor list. Doing so will eliminate θ ( )-joins to answer XPath-expression based queries. θ-joins account for a major performance bottleneck in current approaches for storing XML data in an RDBMS. We also compare the performance of our system to two existing approaches for relational storage of XML documents, XRel and XParent, to demonstrate the effectiveness of our approach. We show that our system performs significantly better than XRel and XParent in terms of insertion, extraction and querying of XML documents.

Converting XML DTDs to UML diagrams for conceptual data integration

XML data can be modeled as node-labeled graphs and XML queries can be expressed by structural relationships between labeled elements. XML query evaluation has been addressed using mainly database, and in some cases graph search, techniques. We propose an alternative method that models and solves such queries as constraint satisfaction problems (CSPs). We describe common constraint types occurring in XML queries and show how query evaluation can benefit from methods for preprocessing and solving CSPs. We identify an important non-binary constraint that is a common module of XML queries and describe a generalized arc consistency algorithm with low cost that can ensure polynomial query evaluation. Finally, we demonstrate that maintaining the consistency of such non-binary constraints can greatly accelerate search in intractable queries that include referential relationships.

Recently, ontology-based data access is more and more employed in the context of data integration. That is, to the usual data layer of an information system we superimpose a conceptual layer to be exposed to the client. In this paper, we discuss a way to query XML data over conceptual layer using Description Logics. Given a XML data file, we first map the XML DTD to a corresponding relational schema preserving the original semantics of the XML DTD, and at the meantime, we store the XML data into the relational database. After that, we create an ontology described in Description Logics with respect to the relational schema. When a Description Logic query is posted to the ontology TBox, we map it to SQL query with respect to the relational database. In this way, we can query XML data using Description Logics. The main contribution of this thesis can be seen as the following two parts: 1. We proposed a mapping from XML DTD to relational schema with constraints preserving the original XML DTD semantics. 2. We described a framework of querying XML data using Description Logics, more precisely, the DL-LiteA logic.

XQuery has been recently proposed by W3C as a standardized query language for XML. Because of its complexity, optimizing XQuery is difficult and requires more complicated optimization schemes than those for traditional query languages. This paper first proposes an XML data model and query algebra to provide a semantics and a means of optimization for the XQuery language. Following the language algebra, algebraic transformation (with optimization rules) is presented. Finally, physical algebraic operators are also defined to specify queries in this algebra into physical query plans.

Active XML (AXML) research: Survey on the representation, system architecture, data exchange mechanism and query evaluation

With new aviation market entrants such as Urban Air Mobility (UAM) vehicles, the role aviation will take in seamless, end-to-end multimodal transportation is evolving. Travelers will likely be able to request on-demand, point-to-point transportation through air taxis in the not-to-distant future. Previous multimodal research efforts have focused on surface urban mobility, where multiple modes of ground transportation compete with and complement each other, ignoring the growing role that aviation will play going forward. Existing public transportation data exchange models, tailored for their respective systems, likewise, have not yet accounted for this looming transportation transformation nor for widespread multimodal end-to-end, seamless transporting. To date, the FAA has succeeded in establishing a robust data exchange architecture to support traditional air transportation, but to facilitate this evolution of the public airspace system with respect to UAM and other emerging air vehicle systems, new and revised data exchange models are necessary. Existing data exchange models in need of revision include the International Civil Aviation Organization's (ICAO) Flight Information Exchange Model (FIXM) and the General Transit Feed Specification (GTFS) [2], [3]. New data exchange models in need of creation include the Passenger Information Exchange Model (PIXM), the Operation Information Exchange Model (OIXM), and Vehicle Information Exchange Model (VIXM) [1]. The research here explored these data models, identifying and defining preliminary conceptual data elements necessary to support seamless, end-to-end mobility. The conceptual model herein proposed is holistic and considers all modes of transportation – walk, car, bus, rail, boat, air, etc. - and looks to incorporate the most promising transportation data exchange models from among these transportation systems.A realistic multimodal travel scenario subject to disruptive events (such as severe weather) was developed for the purpose of identifying the necessary exchange of data to reduce the impact of the disruptive events on the traveler. This work helped inform the next steps in the project, the definition of multimodal data exchange models and the development of a corresponding multimodal transportation conceptual data model. The scenario includes Urban Air Mobility (UAM) vehicles that are intended to serve as short haul providers operating in a congested urban metro environment to bypass surface traffic congestion. New aviation market entrants, such as UAM, will likely not operate entirely within a traditional gate to gate model and are eventually likely to use a model similar to on-demand surface transportation such as ride share. Uber Elevate has already been proposing this type of application for their future air taxis [4]. Accommodating these new aviation market entrants will require further extensions to the FIXM (a second package for Unmanned Aircraft System (UAS) is already planned by the FIXM committee), adoption and enhancement of existing industry data exchange models, and entirely new data models. GTFS is the industry-leading and international de facto public transportation data exchange standard and has an extension called "flex" for supporting on-demand mobility, though has been limited to surface applications thus far [3], [5]. Alongside FIXM and GTFS, new models are necessary to facilitate seamless, end-to-end mobility. Proposed models include the Vehicle Information Exchange Model (VIXM), the Passenger Information Exchange Model (PIXM), and the Operator Information Exchange Model (OIXM) [1].This paper serves as an initial exploration of a data exchange model for future of transportation. The authors envision a future where a traveler can easily plan, dynamically re-plan, and track the real-time progress of a multimodal trip from the convenience of a smartphone application. The authors include a description of a future smartphone application to help illustrate the concept (Section 5). Section 6 provides a brief history of multimodal data exchange initiatives. Section 7 includes a brief discussion of next steps and recommendations for continued research.

Data cubes for OLAP (On‐Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path‐oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real‐world data integration tasks. Several Lowest Common Ancestor (LCA)‐based XML query evaluation strategies have recently been introduced to provide a more structure‐independent way to access XML documents. We shall, however, show that this approach leads in the context of certain—not uncommon—types of XML documents to undesirable results. This article introduces a novel high‐level data extraction primitive that utilizes the purpose‐built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.

The increasing global demand on freshwater is resulting in nations improving their terrestrial water monitoring and reporting systems to better understand the availability, and quality, of this valuable resource. A barrier to this is the inability for stakeholders to share information relating to water observations data: traditional hydrological information systems have relied on internal custom data formats to exchange data, leading to issues in data integration and exchange. Organisations are looking to information standards to assist in data exchange, integration and interpretation to lower costs in use, and re-use, of monitoring data. The WaterML2.0 Standards Working Group (SWG), working within the Open Geospatial Consortium (OGC) and in cooperation with the joint OGC-World Meteorological Organisation (WMO) Hydrology Domain Working Group (HDWG), has developed an open standard for the exchange of water observation data. The focus of the standard is time-series data, commonly used for hydrological applications such as flood forecasting, environmental reporting and hydrological infrastructure, where a lack of standards inhibits efficient re-use and automation. This paper describes the development methodology and principles of WaterML2.0, key parts of its information model, implementation scenarios, evaluation and future work. WaterML2.0 was adopted by the OGC as an official standard in September 2012.

State-subsidized programs develop medical data integration centers in Germany. To get infection disease (ID) researchers involved in the process of data sharing, common interests and minimum data requirements were prioritized. In 06/2019 we have initiated the German Infectious Disease Data Exchange (iDEx) project. We have developed and performed an online survey to determine prioritization of requests for data integration and exchange in ID research. The survey was designed with three sub-surveys, including a ranking of 15 data categories and 184 specific data items and a query of available 51 data collecting systems. A total of 84 researchers from 17 fields of ID research participated in the survey (predominant research fields: gastrointestinal infections n=11, healthcare-associated and antibiotic-resistant infections n=10, hepatitis n=10). 48% (40/84) of participants had experience as medical doctor. The three top ranked data categories were microbiology and parasitology, experimental data, and medication (53%, 52%, and 47% of maximal points, respectively). The most relevant data items for these categories were bloodstream infections, availability of biomaterial, and medication (88%, 87%, and 94% of maximal points, respectively). The ranking of requests of data integration and exchange is diverse and depends on the chosen measure. However, there is need to promote discipline-related digitalization and data exchange.

XML data is likely to be widely used as a data exchange format but users also need to store and query XML data. The purpose of this panel is to explore whether and how to best provide this functionality.

The ability to summarize information from many XML data sources is becoming crucial. Problems that need to be addressed are related to different representations of data and different levels of generality/specificity of data. In this paper we propose to solve these problems by creating a methodology for efficient XML schema diagram transformations in static and dynamic situations. We focus on a graphical approach for XML schema diagram transformations and XML data querying. This approach allows us to design XML processing system for diverse XML documents. In the paper we classify schema diagram changes based on adding/removing/modifying element attributes, and changing the structure of the elements.

Intuitively, data management and data integration tools should be well-suited for exchanging information in a semantically meaningful way. Unfortunately, they suffer from two significant problems: they typically require a comprehensive schema design before they can be used to store or share information, and they are difficult to extend because schema evolution is heavyweight and may break backwards compatibility. As a result, many small-scale data sharing tasks are more easily facilitated by nondatabase-oriented tools that have little support for semantics. The goal of the peer data management system (PDMS) is to address this need: we propose the use of a decentralized, easily extensible data management architecture in which any user can contribute new data, schema information, or even mappings between other peer's schemas. PDMSs represent a natural step beyond data integration systems, replacing their single logical schema with an interlinked collection of semantic mappings between peer's individual schemas. We consider the problem of schema mediation in a PDMS. Our first contribution is a flexible language for mediating between peer schemas, which extends known data integration formalisms to our more complex architecture. We precisely characterize the complexity of query answering for our language. Next, we describe a reformulation algorithm for our language that generalizes both global-as-view and local-as-view query answering algorithms. Finally, we describe several methods for optimizing the reformulation algorithm, and an initial set of experiments studying its performance.

The growing number of XML documents leads to the need for appropriate XML querying algorithms which are able to utilize the specific characteristics of XML documents. A labelling scheme is fundamental to processing XML queries efficiently. They are used to determine structural relationships between elements corresponding to query nodes in twig pattern queries (TPQs). This article presents a design and implementation of a new indexing technique which exploits the property of prime numbers to identify Parent-Child (P-C) relationships in TPQs during query evaluation. The Child Prime Label (CPL, for short) approach can be efficiently incorporated within the existing labelling schemes. Here, we propose a novel twig matching algorithm based on the well known TwigStack algorithm [3], which applies the CPL approach and focuses on reducing the overhead of storing useless elements and performing unnecessary join operations. Our performance evaluation demonstrates that the new algorithm significantly outperforms the previous approaches.

Speeding up query evaluation in large XML repositories becomes a challenging and all-important problem with vast XML-related applications arising. In this paper, we present SCALER, an efficient algorithm for XML query answering based on UDFTS and effective twig structure matching scheme. UDFTS not only constructs a one-to-one correspondence between trees and sequences but also maintains critical parent-child relationships for twig structure matching. With SCALER, XML queries can be performed by subsequence matching without breaking twigs into sub paths and evaluating these paths individually. Thus, costly join operations can be avoided elegantly. We also show the correctness of query answering by eliminating false dismissals and false alarms naturally in SCALER. By a thorough experimental study on various real-life data, we prove the efficiency and scalability of SCALER over the previous known alternative.