Intuitive querying of e-Health data repositories

At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. An integral part of the CLEF workbench is a tool to allow biomedical researchers and clinicians to query – in an intuitive way – the repository of patient data. This paper describes the CLEF query editing interface, which makes use of natural language generation techniques in order to alleviate some of the problems generally faced by natural language and graphical query interfaces. The query interface also incorporates an answer renderer that dynamically generates responses in both natural language text and graphics

Linked Data - the story so far

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward

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

Evaluation of Big Data Platforms for Industrial Process Data

When the number of IoT devices, as well as human activities on the Internet, has increased fast in recent years, data generated has also witnessed an exponential growth in volume. Therefore, various frameworks and software such as Cassandra, Hive, and Spark have been developed to store and explore this massive amount of data. In particular, the waves of Big Data have also reached the industrial businesses. As the number of sensors installed in machines and mills significantly increases, log data is generated from these devices in higher frequencies and enormously complex calculations are applied to this data. The thesis is aimed at evaluating how effectively the current Big Data frameworks and tools manipulate industrial Big Data, especially process data. After surveying several techniques and potential frameworks and tools, the thesis focuses on building a prototype of a data pipeline. The prototype must satisfy a set of use cases. The data pipeline contains several components including Spark, Impala, and Sqoop. Also, it uses Parquet as the file format and stores the Parquet files in S3. Several experiments were also conducted in AWS, to validate the requirements in the use cases. The workload used for these tests was around 690 GBs of Parquet files. This amount of data includes one million channels, divided into one thousand groups, and the data sampling rate was one data point per second. The results of the experiments show that the performance of current big data frameworks may fulfill the performance requirements and the features in the use cases and industrial businesses in general

TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS

This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies

Automated modelling assistance by integrating heterogeneous information sources

Model-Driven Engineering (MDE) uses models as its main assets in the software development process. The structure of a model is described through a metamodel. Even though modelling and meta-modelling are recurrent activities in MDE and a vast amount of MDE tools exist nowadays, they are tasks typically performed in an unassisted way. Usually, these tools cannot extract useful knowledge available in heterogeneous information sources like XML, RDF, CSV or other models and meta-models. We propose an approach to provide modelling and meta-modelling assistance. The approach gathers heterogeneous information sources in various technological spaces, and represents them uniformly in a common data model. This enables their uniform querying, by means of an extensible mechanism, which can make use of services, e.g., for synonym search and word sense analysis. The query results can then be easily incorporated into the (meta-)model being built. The approach has been realized in the Extremo tool, developed as an Eclipse plugin. Extremo has been validated in the context of two domains { production systems and process modelling { taking into account a large and complex industrial standard for classi cation and product description. Further validation results indicate that the integration of Extremo in various modelling environments can be achieved with low e ort, and that the tool is able to handle information from most existing technological spacesThis work was supported by the Ministry of Education of 1256 Spain (FPU grant FPU13/02698); the Spanish MINECO (TIN2014-52129-R);1257 the R&D programme of the Madrid Region (S2013/ICE-3006); the Austrian 1258 agency for international mobility and cooperation in education, science and re1259 search (OeAD) by funds from the Austrian Federal Ministry of Science, Research 1260 and Economy - BMWFW (ICM-2016-04969

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

A Pattern-based Foundation for Language-Driven Software Engineering

This work brings together two fundamental ideas for modelling, programming and analysing software systems. The first idea is of a methodological nature: engineering software by systematically creating and relating languages. The second idea is of a technical nature: using patterns as a practical foundation for computing. The goal is to show that the systematic creation and layering of languages can be reduced to the elementary operations of pattern matching and instantiation and that this pattern-based approach provides a formal and practical foundation for language-driven modelling, programming and analysis. The underpinning of the work is a novel formalism for recognising, deconstructing, creating, searching, transforming and generally manipulating data structures. The formalism is based on typed sequences, a generic structure for representing trees. It defines basic pattern expressions for matching and instantiating atomic values and variables. Horizontal, vertical, diagonal and hierarchical operators are different ways of combining patterns. Transformations combine matching and instantiating patterns and they are patterns themselves. A quasiquotation mechanism allows arbitrary levels of meta-pattern functionality and forms the basis of pattern abstraction. Path polymorphic operators are used to specify fine-grained search of structures. A range of core concepts such as layering, parsing and pattern-based computing can naturally be defined through pattern expressions. Three language-driven tools that utilise the pattern formalism showcase the applicability of the pattern-approach. Concat is a self-sustaining (meta-)programming system in which all computations are expressed by matching and instantiation. This includes parsing, executing and optimising programs. By applying its language engineering tools to its own meta-language, Concat can extend itself from within. XMF (XML Modeling Framework) is a browser-based modelling- and meta-modelling framework that provides flexible means to create and relate modelling languages and to query and validate models. The pattern functionality that makes this possible is partly exposed as a schema language and partly as a JavaScript library. CFR (Channel Filter Rule Language) implements a language-driven approach for layered analysis of communication in complex networked systems. The communication on each layer is visible in the language of an “abstract protocol” that is defined by communication patterns

The representation and management of evolving features in geospatial databases

Geographic features change over time, this change being the result of some kind of event or occurrence. It has been a research challenge to represent this data in a manner that reflects human perception. Most database systems used in geographic information systems (GIS) are relational, and change is either captured by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent diffculty of modelling geographic objects in relational tables. This diffculty is compounded when the necessary time dimension is introduced to model how those objects evolve. There is little doubt that the object-oriented (OO) paradigm holds signi cant advantages over the relational model when it comes to modelling real-world entities and spatial data, and it is argued that this contention is particularly true when it comes to spatio-temporal data. This thesis describes an object-oriented approach to the design of a conceptual model for representing spatio-temporal geographic data, called the Feature Evolution Model (FEM), based on states and events. The model was used to implement a spatio-temporal database management system in Oracle Spatial, and an interface prototype is described that was used to evaluate the system by enabling querying and visualisation

Querying industrial stream-temporal data: An ontology-based visual approach

An increasing number of sensors are being deployed in business-critical environments, systems, and equipment; and stream a vast amount of data. The operational efficiency and effectiveness of business processes rely on domain experts’ agility in interpreting data into actionable business information. A domain expert has extensive domain knowledge but not necessarily skills and knowledge on databases and formal query languages. Therefore, centralised approaches are often preferred. These require IT experts to translate the information needs of domain experts into extract-transform-load (ETL) processes in order to extract and integrate data and then let domain experts apply predefined analytics. Since such a workflow is too time intensive, heavy-weight and inflexible given the high volume and velocity of data, domain experts need to extract and analyse the data of interest directly. Ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality. Moreover, recent ontology-based data access (OBDA) technologies enable end users to formulate their information needs into queries using a set of terms defined in an ontology. Ontological queries could then be translated into SQL or some other database query languages, and executed over the data in its original place and format automatically. To this end, this article reports an ontology-based visual query system (VQS), namely OptiqueVQS, how it is extended for a stream-temporal query language called STARQL, a user experiment with the domain experts at Siemens AG, and STARQL’s query answering performance over a proof of concept implementation for PostgreSQL