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.
Numerous works come from the area of . Examples for SQL-like domain-unspecific QLs on heterogeneous networks are BiQL @cite_34 which is suitable for network analysis and focuses on create update functions or SnQL @cite_13 , a social network query language specialized on transformation and construction operations. Other SQL-like QLs which are not restricted on a domain, focus on graph traversal while being document-centric @cite_11 or social network analysis while being person-centric @cite_14 . Some SQL-like query languages operate on RDF @cite_21 or bibliographic databases using the MARC format @cite_28 . While SchenQL is as structured as these formalized query languages, it does not depend on complicated SQL-like syntax. We argue that SQL is too complex to be effectively operated by casual users and hardly even by computer scientists. Our DSL is neither person-centric nor document-centric but both. Its focal point is the retrieval and exploration of data contrasting the creation transformation focus of several of the presented languages.
@cite_14 @cite_28 @cite_21 @cite_34 @cite_13 @cite_11
2131817760 2015574804 2524963930 1539983305 1482791530 2022015062
With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. Moreover, recursive aggregate functions make it possible to implement more graph algorithms that cannot be implemented in Datalog. We evaluated SociaLite by running nine graph algorithms in total; eight for social network analysis (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) and one for biological network analysis (Eulerian cycles). We use two real-life social graphs, LiveJournal and Last.fm, for the evaluation as well as one synthetic graph. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50 percent for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, with only 16 percent overhead for the largest benchmark, and 25 percent overhead for the worst case benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to perform network analysis easily and efficiently. RDF, the current standard for the representation of Web metadata, is based on a simple graph model that ensures logical interoperability between metadata formats. This model is however too limited to represent natively complex metadata structures such as sets, lists, structured property values and reified triples, which are simulated using blank nodes and built-in vocabularies. This situation tend to blur the semantics of RDF graphs and complicate the expression of queries. In this article, we extend RDF with n-ary statements and statement nesting, so that complex metadata structures are expressed natively. A query language over this extended RDF is also proposed. One of the key steps in data analysis is the exploration of data. For traditional relational data, this process is facilitated by relational database management systems and the aggregates and rankings they can compute. However, for the exploration of graph data, relational databases may not be most practical and scalable. Many tasks related to exploration of information networks involve computation and analysis of connections (e.g. paths) between concepts. Traditional relational databases offer no specific support for performing such tasks. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. In this chapter we present the BiQL data model for representing and manipulating information networks. The BiQL data model consists of two parts: a data model describing objects, link, domains and networks, and a query language describing basic network manipulations. The main focus here lies on data preparation and data analysis, and less on data mining or knowledge discovery tasks directly. Social Network (SN) data has become ubiquitous, demanding advanced and exible means to represent, transform and query such data. In addition to the intrinsic challenges of querying graph data is the requirement that networks be restructured, and thus that new values be created. To address these, we introduce a dedicated data model and query language, SNQL, founded on previous research on graph databases and on the experience of SN researchers. Technically, it is based in GraphLog and second-order tuple generating dependencies, allowing expressiveness for graph querying and node creation as required by SN, while keeping the complexity of query evaluation in NLOGSPACE. Many new database applications involve querying of graph data. We present an object-oriented graph data model, and an OQL like graph query language, GOQL. The data model and the language are illustrated in the application domain of multimedia presentation graphs. We then discuss the query processing techniques for GOQL, more specifically, the translation of GOQL, into an operator-based language, called O-Algebra, extended with operators to deal with paths and sequences. We also discuss different approaches for efficient implementation of algebra operators for paths and sequences.