Schema-agnostic entity retrieval in highly heterogeneous semi-structured environments

[no abstract

Expanding Database Keyword Search for Database Exploration

AbstractDatabase keyword search (DB KWS) has received a lot of attention in database research community. Although much of the research has been motivated by improving performance, recent research has also paid increased attention to its role in database contents exploration or data mining. In this paper we explore aspects related to DB KWS in two steps: First, we expand DB KWS by incorporating ontologies to better capture users’ intention. Furthermore, we examine how KWS or ontology-enriched KWS can offer useful hints for better understanding of the data and in-depth analysis of the data contents, or data mining

Supporting authoring of adaptive hypermedia

It is well-known that students benefit from personalised attention. However, frequently teachers are unable to provide this, most often due to time constraints. An Adaptive Hypermedia (AH) system can offer a richer learning experience, by giving personalised attention to students. The authoring process, however, is time consuming and cumbersome. Our research explores the two main aspects to authoring of AH: authoring of content and adaptive behaviour. The research proposes possible solutions, to overcome the hurdles towards acceptance of AH in education. Automation methods can help authors, for example, teachers could create linear lessons and our prototype can add content alternatives for adaptation. Creating adaptive behaviour is more complex. Rule-based systems, XML-based conditional inclusion, Semantic Web reasoning and reusable, portable scripting in a programming language have been proposed. These methods all require specialised knowledge. Hence authoring of adaptive behaviour is difficult and teachers cannot be expected to create such strategies. We investigate three ways to address this issue. 1. Reusability: We investigate limitations regarding adaptation engines, which influence the authoring and reuse of adaptation strategies. We propose a metalanguage, as a supplement to the existing LAG adaptation language, showing how it can overcome such limitations. 2. Standardisation: There are no widely accepted standards for AH. The IMSLearning Design (IMS-LD) specification has similar goals to Adaptive Educational Hypermedia (AEH). Investigation shows that IMS-LD is more limited in terms of adaptive behaviour, but the authoring process focuses more on learning sequences and outcomes. 3. Visualisation: Another way is to simplify the authoring process of strategies using a visual tool. We define a reference model and a tool, the Conceptual Adaptation Model (CAM) and GRAPPLE Authoring Tool (GAT), which allow specification of an adaptive course in a graphical way. A key feature is the separation between content, strategy and adaptive course, which increases reusability compared to approaches that combine all factors in one model

Lightweight information integration through partial mapping and query reformulation

[no abstract

Approximating expressive queries on graph-modeled data: The GeX approach

We present the GeX (Graph-eXplorer) approach for the approximate matching of complex queries on graph-modeled data. GeX generalizes existing approaches and provides for a highly expressive graph-based query language that supports queries ranging from keyword-based to structured ones. The GeX query answering model gracefully blends label approximation with structural relaxation, under the primary objective of delivering meaningfully approximated results only. GeX implements ad-hoc data structures that are exploited by a top-k retrieval algorithm which enhances the approximate matching of complex queries. An extensive experimental evaluation on real world datasets demonstrates the efficiency of the GeX query answering

No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-$k$ results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.Comment: 24 pages, 21 figures, 6 tables, submitted to The VLDB Journal for possible publicatio

Applications of flexible querying to graph data

Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information seeking requirements. There is a need therefore for flexible querying systems over graph data that can automatically make changes to the user's query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations

Effective Interpretation, Integration and Querying of Web Tables

Ph.DDOCTOR OF PHILOSOPH

CoVault: A Secure Analytics Platform

In a secure analytics platform, data sources consent to the exclusive use oftheir data for a pre-defined set of analytics queries performed by a specificgroup of analysts, and for a limited period. If the platform is secure under asufficiently strong threat model, it can provide the missing link to enablingpowerful analytics of sensitive personal data, by alleviating data subjects'concerns about leakage and misuse of data. For instance, many types of powerfulanalytics that benefit public health, mobility, infrastructure, finance, orsustainable energy can be made differentially private, thus alleviatingconcerns about privacy. However, no platform currently exists that issufficiently secure to alleviate concerns about data leakage and misuse; as aresult, many types of analytics that would be in the interest of data subjectsand the public are not done. CoVault uses a new multi-party implementation offunctional encryption (FE) for secure analytics, which relies on a uniquecombination of secret sharing, multi-party secure computation (MPC), anddifferent trusted execution environments (TEEs). CoVault is secure under a verystrong threat model that tolerates compromise and side-channel attacks on anyone of a small set of parties and their TEEs. Despite the cost of MPC, we showthat CoVault scales to very large data sizes using map-reduce based queryparallelization. For example, we show that CoVault can perform queries relevantto epidemic analytics at scale.<br