A single type of data store can hardly fulfill every end-user requirements in the NoSQL world. Therefore, polyglot systems use different types of NoSQL datastores in combination. However, the heterogeneity of the data storage models makes managing the metadata a complex task in such systems, with only a handful of research carried out to address this. In this paper, we propose a hypergraph-based approach for representing the catalog of metadata in a polyglot system. Taking an existing common programming interface to NoSQL systems, we extend and formalize it as hypergraphs. Then, we define design constraints and query transformation rules for three representative data store types. Next, we propose a simple query rewriting algorithm from the metadata of the catalog to underlying data store specific ones and provide a prototype implementation. Furthermore, we introduce a storage statistics estimator on the underlying data stores. Finally, we show the feasibility of our approach on a use case of an existing polyglot system, and its usefulness in metadata and physical query path calculations.

Document stores have become one of the most popular NoSQL systems, mainly due to their semi-structured data storage structure and well-developed query capabilities. The semi-structured nature allows them to have database designs beyond traditional normalization theories. This makes the database design decisions more complicated with a myriad of possibilities. Thus, the database design process for them has resorted to ad-hoc trial and error methods. However, having a good database design is essential for any data storage system’s performance, and bad design decisions cannot always be compensated by adding more powerful hardware. Thus, in this work, we propose DocDesign, a decision aid tool for document store database design. DocDesign allows its users to evaluate different database designs for data storage requirements under a particular workload. Through DocDesign, users can make informed decisions for a design by evaluating the estimated storage statistics and query runtimes without testing it on an actual document store. DocDesign also generates design specific queries for the input workload. This not only cuts down the time and the effort taken in design decision making and development but also save money spent on fixing poor designs in the long run. On-site, we will showcase how DocDesign facilitates the design decision-making process for MongoDB with both synthetic and real-world examples.

Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.

Publication and sharing of multidimensional (MD) data on the Semantic Web (SW) opens new opportunities for the use of On-Line Analytical Processing (OLAP). The RDF Data Cube (QB) vocabulary, the current standard for statistical data publishing, however, lacks key MD concepts such as dimension hierarchies and aggregate functions. QB4OLAP was proposed to remedy this. However, QB4OLAP requires extensive manual annotation and users must still write queries in SPARQL, the standard query language for RDF, which typical OLAP users are not familiar with. In this demo, we present QB2OLAP, a tool for enabling OLAP on existing QB data. Without requiring any RDF, QB(4OLAP), or SPARQL skills, it allows semi-automatic transformation of a QB data set into a QB4OLAP one via enrichment with QB4OLAP semantics, exploration of the enriched schema, and querying with the high-level OLAP language QL that exploits the QB4OLAP semantics and is automatically translated to SPARQL.

Next generation BI systems emerge as platforms where traditional BI tools meet semi-structured and unstructured data coming from the Web. In these settings, the user-centric orientation represents a key characteristic for the acceptance and wide usage by numerous and diverse end users in their data analysis tasks. System and user related metadata are the base for enabling user assistance features. However, current approaches typically store these metadata in ad-hoc manners. In this paper, we propose a generic and extensible approach for the definition and modeling of the relevant metadata artifacts. We present SM4AM, a Semantic Metamodel for Analytical Metadata created as an RDF formalization of the Analytical Metadata artifacts needed for user assistance exploitation purposes in next generation BI systems. We consider the Linked Data initiative and its relevance for user assistance functionalities. We discuss the metamodel benefits and present directions for future work.