Information access needs to be uncomplicated, users rather use incorrect data which is easily received than correct information which is harder to obtain. Querying bibliographic metadata from digital libraries mainly supports simple textual queries. A user's demand for answering more sophisticated queries could be fulfilled by the usage of SQL. As such means are highly complex and challenging even for trained programmers, a domain-specific query language is needed to provide a straightforward way to access data. In this paper we present SchenQL, a simple query language focused on bibliographic metadata in the area of computer science while using the vocabulary of domain-experts. By facilitating a plain syntax and fundamental aggregate functions, we propose an easy-to-learn domain-specific query language capable of search and exploration. It is suitable for domain-experts as well as casual users while still providing the possibility to answer complicated queries. A user study with computer scientists directly compared our query language to SQL and clearly demonstrated SchenQL's suitability and usefulness for given queries as well as users' acceptance.
Further research applies QLs in a way that users do not interact with it directly in using the system but in their back end. In many cases, graph-like structured data of heterogeneous networks is used to locate information semantically relevant to a unspecific query @cite_16 @cite_31 . Such an indirection could be a future step in the development of SchenQL.
@cite_31 @cite_16
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We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person's job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a "star" query would be, "find a CEO who has strong interactions with a Manager, a Lawyer,and an Accountant, or another structure as close to that as possible". Similarly, a "loop" query could help spot a money laundering ring. Traditional SQL-based methods, as well as more recent graph indexing methods, will return no answer when an exact match does not exist. This is the first main feature of our method. It can find exact-, as well as near-matches, and it will present them to the user in our proposed "goodness" order. For example, our method tolerates indirect paths between, say, the "CEO" and the "Accountant" of the above sample query, when direct paths don't exist. Its second feature is scalability. In general, if the query has nq nodes and the data graph has n nodes, the problem needs polynomial time complexity O(n n q), which is prohibitive. Our G-Ray ("Graph X-Ray") method finds high-quality subgraphs in time linear on the size of the data graph. Experimental results on the DLBP author-publication graph (with 356K nodes and 1.9M edges) illustrate both the effectiveness and scalability of our approach. The results agree with our intuition, and the speed is excellent. It takes 4 seconds on average fora 4-node query on the DBLP graph. This paper introduces query-feature graphs, or QF-graphs. QF-graphs encode associations between high-level descriptions of user goals (articulated as natural language search queries) and the specific features of an interactive system relevant to achieving those goals. For example, a QF-graph for the GIMP graphics manipulation software links the query "GIMP black and white" to the commands "desaturate" and "grayscale." We demonstrate how QF-graphs can be constructed using search query logs, search engine results, web page content, and localization data from interactive systems. An analysis of QF-graphs shows that the associations produced by our approach exhibit levels of accuracy that make them eminently usable in a range of real-world applications. Finally, we present three hypothetical user interface mechanisms that illustrate the potential of QF-graphs: search-driven interaction, dynamic tooltips, and app-to-app analogy search. Abstract With the proliferation of smart grids, traditional utilities are struggling to handle the increasing amount of metering data. Outsourcing the metering data to heterogeneous distributed systems has the potential to provide efficient data access and processing. In an untrusted heterogeneous distributed system environment, employing data encryption prior to outsourcing can be an effective way to preserve user privacy. However, how to efficiently query encrypted multidimensional metering data stored in an untrusted heterogeneous distributed system environment remains a research challenge. In this paper, we propose a high performance and privacy-preserving query (P2Q) scheme over encrypted multidimensional big metering data to address this challenge. In the proposed scheme, encrypted metering data are stored in the server of an untrusted heterogeneous distributed system environment. A Locality Sensitive Hashing (LSH) based similarity search approach is then used to realize the similarity query. To demonstrate utility of the proposed LSH-based search approach, we implement a prototype using MapReduce for the Hadoop distributed environment. More specifically, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-based Encryption (CP-ABE) policy is then used to control access to the search results. Therefore, only the requester with an authorized query attribute can obtain the correct secret keys to retrieve the metering data. We then prove that the P2Q scheme achieves data confidentiality and preserves the data owner’s privacy in a semi-trusted cloud. In addition, our evaluations demonstrate that the P2Q scheme can significantly reduce response time and provide high search efficiency without compromising on search quality (i.e. suitable for multidimensional big data search in heterogeneous distributed system, such as cloud storage system). We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answering over structured and interlinked data collected from many disparate sources on the Web. In particular, we study distributed indexing methods for graph-structured data and parallel query evaluation methods on a cluster of computers. We evaluate the system on a dataset with 430 million statements collected from the Web, and provide scale-up experiments on 7 billion synthetically generated statements.