22 research outputs found

    Big Data in Management Research. Exploring New Avenues

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    Big Data in Management Research

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    Digital computers entered our homes, landed on our desktops, slipped into our pockets, and have seemingly become ubiquitous. At an ever faster pace, these devices have become highly interconnected and interoperable. Consequently, our archives, our work, our actions, and our interactions are increasingly digitalized and stored in databases or made accessible via the Internet. This data, generally characterized by high volume, variety, and velocity (i.e., accumulation rate), has come to be called ā€œBig Dataā€. As of yet, Big Data has seldom been utilized in management research. Therefore, this dissertation explores the opportunities that Big Data brings for management scholars and describes three distinct projects that show how Big Data can be utilized in management research

    Big Data in Management Research. Exploring New Avenues

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    Keyword-Based Querying for the Social Semantic Web

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    Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches

    Extending XQuery for Semantic Web Reasoning

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    Abstract. In this paper we investigate an extension of the XQuery language for querying and reasoning with OWL-style ontologies. The proposed extension incorporates new primitives (i.e. boolean operators) in XQuery for the querying and reasoning with OWL-style triples in such a way that XQuery can be used as query language for the Semantic Web. In addition, we propose a Prolog-based implementation of the extension

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

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    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 W3Cā€™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 ā€œWeb of Dataā€

    Reasoning & Querying ā€“ State of the Art

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    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

    A Lightweight Framework for Universal Fragment Composition

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    Domain-specific languages (DSLs) are useful tools for coping with complexity in software development. DSLs provide developers with appropriate constructs for specifying and solving the problems they are faced with. While the exact definition of DSLs can vary, they can roughly be divided into two categories: embedded and non-embedded. Embedded DSLs (E-DSLs) are integrated into general-purpose host languages (e.g. Java), while non-embedded DSLs (NE-DSLs) are standalone languages with their own tooling (e.g. compilers or interpreters). NE-DSLs can for example be found on the Semantic Web where they are used for querying or describing shared domain models (ontologies). A common theme with DSLs is naturally their support of focused expressive power. However, in many cases they do not support nonā€“domain-specific component-oriented constructs that can be useful for developers. Such constructs are standard in general-purpose languages (procedures, methods, packages, libraries etc.). While E-DSLs have access to such constructs via their host languages, NE-DSLs do not have this opportunity. Instead, to support such notions, each of these languages have to be extended and their tooling updated accordingly. Such modifications can be costly and must be done individually for each language. A solution method for one language cannot easily be reused for another. There currently exist no appropriate technology for tackling this problem in a general manner. Apart from identifying the need for a general approach to address this issue, we extend existing composition technology to provide a language-inclusive solution. We build upon fragment-based composition techniques and make them applicable to arbitrary (context-free) languages. We call this process for the composition techniquesā€™ universalization. The techniques are called fragment-based since their view of componentsā€” reusable software units with interfacesā€”are pieces of source code that conform to an underlying (context-free) language grammar. The universalization process is grammar-driven: given a base language grammar and a description of the compositional needs wrt. the composition techniques, an adapted grammar is created that corresponds to the specified needs. The result is thus an adapted grammar that forms the foundation for allowing to define and compose the desired fragments. We further build upon this grammar-driven universalization approach to allow developers to define the nonā€“domain-specific component-oriented constructs that are needed for NE-DSLs. Developers are able to define both what those constructs should be, and how they are to be interpreted (via composition). Thus, developers can effectively define language extensions and their semantics. This solution is presented in a framework that can be reused for different languages, even if their notion of ā€˜componentsā€™ differ. To demonstrate the approach and show its applicability, we apply it to two Semantic Web related NE-DSLs that are in need of component-oriented constructs. We introduce modules to the rule-based Web query language Xcerpt and role models to the Web Ontology Language OWL
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