Skipping class: improving human-driven data exploration and querying through instances

Improving Data-Driven Decision Making through Human-centered Knowledge Sharing

This research focuses on human-centered knowledge sharing within data-driven decision making processes enabled by advanced analytics. The paper describes an exploratory study of an innovative approach to ongoing improvement of complex data-driven decision making processes found in a large retail distribution company by considering a complex interplay of business intelligence (BI) /business anal...

Data Exploration through Dot-driven Development (Artifact)

This artifact presents The Gamma, a simple programming environment for data exploration that uses member access as the primary mechanism for constructing queries. The artifact consists of two parts. The user facing web-based component allows users to explore a simple dataset of Olympic medal winners while a back-end service provides the data and evaluates queries executed by the user. The purpo...

Data Exploration through Dot-driven Development

Data literacy is becoming increasingly important in the modern world. While spreadsheets make simple data analytics accessible to a large number of people, creating transparent scripts that can be checked, modified, reproduced and formally analyzed requires expert programming skills. In this paper, we describe the design of a data exploration language that makes the task more accessible by embe...

Improving the Recall of Live Linked Data Querying through Reasoning

Linked Data principles allow for processing SPARQL queries on-the-fly by dereferencing URIs. Link-traversal query approaches for Linked Data have the benefit of up-to-date results and decentralised execution, but operate only on explicit data from dereferenced documents, affecting recall. In this paper, we show how inferable knowledge— specifically that found through owl:sameAs and RDFS reasoni...

Querying data through ontologies