DebEAQ - debugging empty-answer queries on large data graphs

The large volume of freely available graph data sets impedes the users in analyzing them. For this purpose, they usually pose plenty of pattern matching queries and study their answers. Without deep knowledge about the data graph, users can create ‘failing’ queries, which deliver empty answers. Analyzing the causes of these empty answers is a time-consuming and complicated task especially for graph queries. To help users in debugging these ‘failing’ queries, there are two common approaches: one is focusing on discovering missing subgraphs of a data graph, the other one tries to rewrite the queries such that they deliver some results. In this demonstration, we will combine both approaches and give the users an opportunity to discover why empty results were delivered by the requested queries. Therefore, we propose DebEAQ, a debugging tool for pattern matching queries, which allows to compare both approaches and also provides functionality to debug queries manually

SynopSys: Foundations for Multidimensional Graph Analytics

The past few years have seen a tremendous increase in often irregularly structured data that can be represented most naturally and efficiently in the form of graphs. Making sense of incessantly growing graphs is not only a key requirement in applications like social media analysis or fraud detection but also a necessity in many traditional enterprise scenarios. Thus, a flexible approach for multidimensional analysis of graph data is needed. Whereas many existing technologies require up-front modelling of analytical scenarios and are difficult to adapt to changes, our approach allows for ad-hoc analytical queries of graph data. Extending our previous work on graph summarization, in this position paper we lay the foundation for large graph analytics to enable business intelligence on graph-structured data

NoXperanto: Crowdsourced Polyglot Persistence

This paper proposes NoXperanto , a novel crowdsourcing approach to address querying over data collections managed by polyglot persistence settings. The main contribution of NoXperanto is the ability to solve complex queries involving different data stores by exploiting queries from expert users (i.e. a crowd of database administrators, data engineers, domain experts, etc.), assuming that these users can submit meaningful queries. NoXperanto exploits the results of meaningful queries in order to facilitate the forthcoming query answering processes. In particular, queries results are used to: (i) help non-expert users in using the multi- database environment and (ii) improve performances of the multi-database environment, which not only uses disk and memory resources, but heavily rely on network bandwidth. NoXperanto employs a layer to keep track of the information produced by the crowd modeled as a Property Graph and managed in a Graph Database Management System (GDBMS)

SEMANTIC WEB BASED INTEGRATION BETWEEN BIM COST AND GEOMETRIC DOMAINS

In the architecture, engineering, construction, and facilities management (AEC/FM) industry methodologies are needed to ensure the interoperability of data and effective management of information from different sources. Integration of the cost domain and cost estimation within the Building Information Model (BIM) in the AEC/FM sector is still an unresolved problem and one of the most critical tasks due to the lack of a standardised cost domain, especially in the tendering phase. To ensure interoperability between cost data and geometric data, this research aims to address this gap by analyzing methods of converting cost data into Linked Building Data, thereby defining a cost domain in the Semantic Web, by collecting them into a graph database. This allows for structuring a cost domain, translating an IFC based structure previously developed by the research group, visualizing it using a graph system, and connecting it to the BIM geometric domain. Furthermore, it is possible to extend the cost ontology previously identified in the IFC model and facilitate the queries and analysis of cost data currently fragmented and based on unstructured data. The results show how Semantic Web technology can be used to improve data interoperability, develop a cost ontology, and join both cost data and BIM models

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Highspeed Graph Processing Exploiting Main-Memory Column Stores

A popular belief in the graph database community is that relational database management systems are generally ill-suited for efficient graph processing. This might apply for analytic graph queries performing iterative computations on the graph, but does not necessarily hold true for short-running, OLTP-style graph queries. In this paper we argue that, instead of extending a graph database management system with traditional relational operators—predicate evaluation, sorting, grouping, and aggregations among others—one should consider adding a graph abstraction and graph-specific operations, such as graph traversals and pattern matching, to relational database management systems. We use an exemplary query from the interactive query workload of the LDBC social network benchmark and run it against our enhanced in-memory, columnar relational database system to support our claims. Our performance measurements indicate that a columnar RDBMS—extended by graph-specific operators and data structures—can serve as a foundation for high-speed graph processing on big memory machines with non-uniform memory access and a large number of available cores