No abstract

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from endusers has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph-and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as pseudographs, and characterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.

The advent of XML as a universal exchange format, and of Web services as a basis for distributed computing, has fostered the apparition of a new class of documents: dynamic XML documents. These are XML documents where some data is given explicitly while other parts are given only intensionally by means of embedded calls to web services that can be called to generate the required information. By the sole presence of Web services, dynamic documents already include inherently some form of distributed computation. A higher level of distribution that also allows (fragments of) dynamic documents to be distributed and/or replicated over several sites is highly desirable in today's Web architecture, and in fact is also relevant for regular (non dynamic) documents.The goal of this paper is to study new issues raised by the distribution and replication of dynamic XML data. Our study has originated in the context of the Active XML system [1,3,22] but the results are applicable to many other systems supporting dynamic XML data. Starting from a data model and a query language, we describe a complete framework for distributed and replicated dynamic XML documents. We provide a comprehensive cost model for query evaluation and show how it applies to user queries and service calls. Finally, we describe an algorithm that, for a given peer, chooses data and services that the peer should replicate to improve the efficiency of maintaining and querying its dynamic data.

Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of gMark, a domain- and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL: http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on Knowledge and Data Engineering 201

XML is becoming the most relevant new standard for data representation and exchange on the WWW. Novel languages for extracting and restructuring the XML content have been proposed, some in the tradition of database query languages (i.e. SQL, OQL), others more closely inspired by XML. No standard for XML query language has yet been decided, but the discussion is ongoing within the World Wide Web Consortium and within many academic institutions and Internet-related major companies. We present a comparison of five, representative query languages for XML, highlighting their common features and differences.

Abstract-Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of gMark, a domain-and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.

This paper provides an in-depth and diversified analysis of the Wikidata query logs, recently made publicly available. Although the usage of Wikidata queries has been the object of recent studies, our analysis of the query traffic reveals interesting and unforeseen findings concerning the usage, types of recursion, and the shape classification of complex recursive queries. Wikidata specific features combined with recursion let us identify a significant subset of the entire corpus that can be used by the community for further assessment. We considered and analyzed the queries across many different dimensions, such as the robotic and organic queries, the presence/absence of constants along with the correctly executed and timed out queries. A further investigation that we pursue in this paper is to find, given a query, a number of queries structurally similar to the given query. We provide a thorough characterization of the queries in terms of their expressive power, their topological structure and shape, along with a deeper understanding of the usage of recursion in these logs. We make the code for the analysis available as open source. CCS CONCEPTS • Information systems → Query log analysis; Query languages for non-relational engines; World Wide Web; Information retrieval query processing; • Theory of computation → Database query languages (principles).

Data warehouses are databases devoted to analytical processing. They are used to support decision-making activities in most modern business settings, when complex data sets have to be studied and analyzed. The technology for analytical processing assumes that data are presented in the form of simple data marts, consisting of a well-identified collection of facts and data analysis dimensions (star schema). Despite the wide diffusion of data warehouse technology and concepts, we still miss methods that help and guide the designer in identifying and extracting such data marts out of an enterprisewide information system, covering the upstream, requirement-driven stages of the design process. Many existing methods and tools support the activities related to the efficient implementation of data marts on top of specialized technology (such as the ROLAP or MOLAP data servers). This paper presents a method to support the identification and design of data marts. The method is based on three basic steps. A first top-down step makes it possible to elicit and consolidate user requirements and expectations. This is accomplished by exploiting a goal-oriented process based on the Goal/Question/Metric paradigm developed at the University of Maryland. Ideal data marts are derived from user requirements. The second bottom-up step extracts candidate data marts